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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):
# Define the specific model and tokenizer for SqueezeBERT
# 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
answer_start_index = torch.argmax(answer_start_scores) # Most likely start of answer
answer_end_index = torch.argmax(answer_end_scores) + 1 # Most likely end of answer; +1 for inclusive slicing
# 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 {"answer": answer, "start": answer_start_index.item(), "end": answer_end_index.item()}
def bert(context, question, pip):
return pip(context=context, question=question)
def deberta(context, question, pip):
return pip(context=context, question=question)
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