Spaces:
Sleeping
Sleeping
File size: 2,314 Bytes
00837ec c37d535 00837ec 2974ccd 00837ec 2974ccd 00837ec 2974ccd 00837ec b4d0d8e 00837ec a88183c c37d535 00837ec a88183c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import torch
def loadSqueeze():
tokenizer = AutoTokenizer.from_pretrained("ALOQAS/squeezebert-uncased-finetuned-squad-v2")
model = AutoModelForQuestionAnswering.from_pretrained("ALOQAS/squeezebert-uncased-finetuned-squad-v2")
return tokenizer, model
def squeezebert(context, question, model, tokenizer):
# Tokenize the input question-context pair
inputs = tokenizer.encode_plus(question, context, max_length=512, truncation=True, padding=True, return_tensors='pt')
# Send inputs to the same device as your model
inputs = {k: v.to(model.device) for k, v in inputs.items()}
with torch.no_grad():
# Forward pass, get model outputs
outputs = model(**inputs)
# Extract the start and end positions of the answer in the tokens
answer_start_scores, answer_end_scores = outputs.start_logits, outputs.end_logits
# Calculate probabilities from logits
answer_start_prob = torch.softmax(answer_start_scores, dim=-1)
answer_end_prob = torch.softmax(answer_end_scores, dim=-1)
# Find the most likely start and end positions
answer_start_index = torch.argmax(answer_start_prob) # Most likely start of answer
answer_end_index = torch.argmax(answer_end_prob) + 1 # Most likely end of answer; +1 for inclusive slicing
# Extract the highest probability scores
start_score = answer_start_prob.max().item() # Highest probability of start
end_score = answer_end_prob.max().item() # Highest probability of end
# Combine the scores into a singular score
combined_score = (start_score * end_score) ** 0.5 # Geometric mean of start and end scores
# Convert token indices to the actual answer text
answer_tokens = inputs['input_ids'][0, answer_start_index:answer_end_index]
answer = tokenizer.decode(answer_tokens, skip_special_tokens=True)
# Return the answer, its positions, and the combined score
return {
"answer": answer,
"start": answer_start_index.item(),
"end": answer_end_index.item(),
"score": combined_score
}
def bert(context, question, pip):
return pip(context=context, question=question)
def deberta(context, question, pip):
return pip(context=context, question=question)
|