usrbinenv python codingutf8 2023 the huggingface inc team 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 orglicenseslicense2 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 example py from transformers tools import textsummarizationtool summarizer textsummarizationtool summarizerlongtext usr bin env python coding utf 8 2023 the huggingface inc team 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 example py from transformers tools import textsummarizationtool summarizer textsummarizationtool summarizer long_text
from ..models.auto import AutoModelForSeq2SeqLM, AutoTokenizer
from .base import PipelineTool


class TextSummarizationTool(PipelineTool):
    

    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)