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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
class TextSummarizationTool(PipelineTool):
"""
Example:
```py
from transformers.tools import TextSummarizationTool
summarizer = TextSummarizationTool()
summarizer(long_text)
```
"""
default_checkpoint = "philschmid/bart-large-cnn-samsum"
description = (
"This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, "
"and returns a summary of the text."
)
name = "summarizer"
pre_processor_class = AutoTokenizer
model_class = AutoModelForSeq2SeqLM
inputs = ["text"]
outputs = ["text"]
def encode(self, text):
return self.pre_processor(text, return_tensors="pt", truncation=True)
def forward(self, inputs):
return self.model.generate(**inputs)[0]
def decode(self, outputs):
return self.pre_processor.decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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