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| #!/usr/bin/env python | |
| # coding=utf-8 | |
| # Copyright 2023 The HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from ..models.auto import AutoModelForSeq2SeqLM, AutoTokenizer | |
| from .base import PipelineTool | |
| QA_PROMPT = """Here is a text containing a lot of information: '''{text}'''. | |
| Can you answer this question about the text: '{question}'""" | |
| class TextQuestionAnsweringTool(PipelineTool): | |
| default_checkpoint = "google/flan-t5-base" | |
| description = ( | |
| "This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the " | |
| "text where to find the answer, and `question`, which is the question, and returns the answer to the question." | |
| ) | |
| name = "text_qa" | |
| pre_processor_class = AutoTokenizer | |
| model_class = AutoModelForSeq2SeqLM | |
| inputs = ["text", "text"] | |
| outputs = ["text"] | |
| def encode(self, text: str, question: str): | |
| prompt = QA_PROMPT.format(text=text, question=question) | |
| return self.pre_processor(prompt, return_tensors="pt") | |
| def forward(self, inputs): | |
| output_ids = self.model.generate(**inputs) | |
| in_b, _ = inputs["input_ids"].shape | |
| out_b = output_ids.shape[0] | |
| return output_ids.reshape(in_b, out_b // in_b, *output_ids.shape[1:])[0][0] | |
| def decode(self, outputs): | |
| return self.pre_processor.decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True) | |