SyllaBERTa
2
2
1 language
—
by
Ericu950
Language Model
OTHER
New
2 downloads
Early-stage
Edge AI:
Mobile
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Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Code Examples
Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)Usage Examplepythontransformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("Ericu950/SyllaBERTa", trust_remote_code=True)
# Encode a sentence
text = "Κατέβην χθὲς εἰς Πειραιᾶ μετὰ Γλαύκωνος τοῦ Ἀρίστωνος"
tokens = tokenizer.tokenize(text)
print(tokens)
# Insert a mask at random
import random
tokens[random.randint(0, len(tokens)-1)] = tokenizer.mask_token
masked_text = tokenizer.convert_tokens_to_string(tokens)
# Predict masked token
inputs = tokenizer(masked_text, return_tensors="pt", padding=True, truncation=True)
inputs.pop("token_type_ids", None)
with torch.no_grad():
outputs = model(**inputs)
# Fetch prediction
logits = outputs.logits
mask_token_index = (inputs['input_ids'] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
top_tokens = logits[0, mask_token_index].topk(5, dim=-1).indices.squeeze(0)
predicted = tokenizer.convert_ids_to_tokens(top_tokens.tolist())
print("Top predictions:", predicted)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)text
Original tokens: ['κα', 'τέ', 'βην', 'χθὲ', 'σεἰσ', 'πει', 'ραι', 'ᾶ', 'με', 'τὰγ', 'λαύ', 'κω', 'νοσ', 'τοῦ', 'ἀ', 'ρίσ', 'τω', 'νοσ']
Masked at position 6
Masked text: κα τέ βην χθὲ σεἰσ πει [MASK] ᾶ με τὰγ λαύ κω νοσ τοῦ ἀ ρίσ τω νοσ
Top 5 predictions for masked token:
ραι (score: 23.12)
ρα (score: 14.69)
ραισ (score: 12.63)
σαι (score: 12.43)
ρη (score: 12.26)Deploy This Model
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