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}