NV-EmbedCode-7b-v1

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

pip install transformers==4.37.2 sentence_transformers

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]

from sentence_transformers import SentenceTransformer, util

# Task instructions for different retrieval scenarios
task_instructions = {
    "general": "Retrieve code or text based on user query",
    "originalbug": "Given a bug description, retrieve codes that need to be edited to resolve it.",
    "llmsummary": "Given a summary of bug description generated by an LLM, retrieve codes that need to be edited to resolve it."
}

# Example queries and corpus
queries = [
    "Function to calculate the sum of two numbers",
    "Recursive function to calculate the factorial of a number",
]

docs = [
    "def add(a, b):\n    return a + b",
    "def factorial(n):\n    return 1 if n==0 else n*factorial(n-1)",
]

# Prepare prompt prefix for corpus
query_prefix = f"Instruct: {task_instructions['general']}\nQuery: "

# Load model
model = SentenceTransformer('nvidia/NV-EmbedCode-7b-v1', trust_remote_code=True)

# Encode queries and documents
query_emb = model.encode(queries, prompt=query_prefix, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)

# Compute similarity scores
scores = util.cos_sim(query_emb, doc_emb) * 100
print(scores.tolist())
# [[68.55826568603516, 24.0609130859375], [28.60508918762207, 76.94281005859375]]