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| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| class GenerationModel: | |
| def __init__(self, model_name): | |
| self.tokenizer = T5Tokenizer.from_pretrained(model_name) | |
| self.model = T5ForConditionalGeneration.from_pretrained(model_name) | |
| def generate(self, data): | |
| inputs = self.tokenizer.encode_plus( | |
| data, | |
| add_special_tokens=True, | |
| max_length=512, | |
| return_attention_mask=True, | |
| return_tensors="pt" | |
| ) | |
| outputs = self.model.generate(inputs["input_ids"], attention_mask=inputs["attention_mask"]) | |
| return self.tokenizer.decode(outputs[0], skip_special_tokens=True) |