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 textclassificationtool classifier textclassificationtool classifierthis is a super nice api labelspositive negative 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 textclassificationtool classifier textclassificationtool classifier this is a super nice api labels positive negative
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool


class TextClassificationTool(PipelineTool):
    

    default_checkpoint = "facebook/bart-large-mnli"
    description = (
        "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
        "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
        "It returns the most likely label in the list of provided `labels` for the input text."
    )
    name = "text_classifier"
    pre_processor_class = AutoTokenizer
    model_class = AutoModelForSequenceClassification

    inputs = ["text", ["text"]]
    outputs = ["text"]

    def setup(self):
        super().setup()
        config = self.model.config
        self.entailment_id = -1
        for idx, label in config.id2label.items():
            if label.lower().startswith("entail"):
                self.entailment_id = int(idx)
        if self.entailment_id == -1:
            raise ValueError("Could not determine the entailment ID from the model config, please pass it at init.")

    def encode(self, text, labels):
        self._labels = labels
        return self.pre_processor(
            [text] * len(labels),
            [f"This example is {label}" for label in labels],
            return_tensors="pt",
            padding="max_length",
        )

    def decode(self, outputs):
        logits = outputs.logits
        label_id = torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]