snowflake-arctic-embed-xs-zyda-2

1
license:mit
by
agentlans
Embedding Model
OTHER
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Quick Summary

This model is a fine-tuned version of Snowflake/snowflake-arctic-embed-xs on a subset of the Zyphra/Zyda-2 dataset.

Code Examples

Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Framework Versionspythontransformers
from transformers import pipeline

unmasker = pipeline('fill-mask', model='agentlans/snowflake-arctic-embed-xs-zyda-2')
result = unmasker("[MASK] is the capital of France.")
print(result)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)
Text Embeddingpythontransformers
from transformers import AutoTokenizer, AutoModel
import torch

model_name = "agentlans/snowflake-arctic-embed-xs-zyda-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

text = "Example sentence for embedding."
inputs = tokenizer(text, return_tensors='pt')
with torch.no_grad():
    outputs = model(**inputs)

embeddings = outputs.last_hidden_state.mean(dim=1)
print(embeddings)

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