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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) | |