53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
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#!/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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class TextSummarizationTool(PipelineTool):
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"""
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Example:
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```py
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from transformers.tools import TextSummarizationTool
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summarizer = TextSummarizationTool()
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summarizer(long_text)
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```
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"""
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default_checkpoint = "philschmid/bart-large-cnn-samsum"
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description = (
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"This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
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"and returns a summary of the text."
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)
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name = "summarizer"
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pre_processor_class = AutoTokenizer
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model_class = AutoModelForSeq2SeqLM
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inputs = ["text"]
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outputs = ["text"]
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def encode(self, text):
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return self.pre_processor(text, return_tensors="pt", truncation=True)
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def forward(self, inputs):
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return self.model.generate(**inputs)[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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