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