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from transformers import Pipeline
class MyPipeline:
def __init__(self,model,tokenizer):
self.model=model
self.tokenizer=tokenizer
def chat_Format(self,context, quetion, answer):
return "Instruction:/n check answer is true or false of next quetion using context below:\n" + "#context: " + context + f".\n#quetion: " + quetion + f".\n#student answer: " + answer + ".\n#response:"
def __call__(self, context, quetion, answer,generate=1,max_new_tokens=4, num_beams=2, do_sample=False,num_return_sequences=1):
inp=self.chat_Format(context, quetion, answer)
w = self.tokenizer(inp, add_special_tokens=True,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt')
response=""
if(generate):
outputs = self.tokenizer.batch_decode(self.model.generate(input_ids=w['input_ids'].cuda(), attention_mask=w['attention_mask'].cuda(), max_new_tokens=max_new_tokens, num_beams=num_beams, do_sample=do_sample, num_return_sequences=num_return_sequences), skip_special_tokens=True)
response = outputs
s =self.model(input_ids=w['input_ids'].cuda(), attention_mask=w['attention_mask'].cuda())['logits'][0][-1]
s = F.softmax(s, dim=-1)
yes_token_id = self.tokenizer.convert_tokens_to_ids("صØŃÙĬØŃ")
no_token_id = self.tokenizer.convert_tokens_to_ids("خط")
print(yes_token_id,no_token_id)
for i in ["Yes", "yes", "True", "true","صحيح"]:
s[yes_token_id] += s[self.tokenizer.convert_tokens_to_ids(i)]
for i in ["No", "no", "False", "false","خطأ"]:
s[no_token_id] += s[self.tokenizer.convert_tokens_to_ids(i)]
true = (s[yes_token_id] / (s[no_token_id] + s[yes_token_id])).item()
return {"response": response, "true": true}
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