class textclassificationpipelinepipeline returnallscores false functiontoapply classificationfunction none def initself kwargs super initkwargs self checkmodeltype tfmodelforsequenceclassificationmappingnames if self framework tf else modelforsequenceclassificationmappingnames def sanitizeparametersself returnallscoresnone functiontoapplynone topk tokenizerkwargs using as default argument because we re going to use topknone in user code to declare no topk preprocessparams tokenizerkwargs postprocessparams if hasattrself model config returnallscores and returnallscores is none returnallscores self model config returnallscores if isinstancetopk int or topk is none postprocessparamstopk topk postprocessparamslegacy false elif returnallscores is not none warnings warn returnallscores is now deprecated if want a similar functionality use topknone instead of returnallscorestrue or topk1 instead of returnallscoresfalse userwarning if returnallscores postprocessparamstopk none else postprocessparamstopk 1 if isinstancefunctiontoapply str functiontoapply classificationfunctionfunctiontoapply upper if functiontoapply is not none postprocessparamsfunctiontoapply functiontoapply return preprocessparams postprocessparams def callself args kwargs result super callargs kwargs todo try and retrieve it in a nicer way from sanitizeparameters legacy topk not in kwargs if isinstanceargs0 str and legacy this pipeline is odd and return a list when single item is run return result else return result def preprocessself inputs tokenizerkwargs dictstr generictensor returntensors self framework if isinstanceinputs dict return self tokenizerinputs returntensorsreturntensors tokenizerkwargs elif isinstanceinputs list and leninputs 1 and isinstanceinputs0 list and leninputs0 2 it used to be valid to use a list of list of list for text pairs keeping this path for bc return self tokenizer textinputs00 textpairinputs01 returntensorsreturntensors tokenizerkwargs elif isinstanceinputs list this is likely an invalid usage of the pipeline attempting to pass text pairs raise valueerror the pipeline received invalid inputs if you are trying to send text pairs you can try to send a dictionary text my text textpair my pair in order to send a text pair return self tokenizerinputs returntensorsreturntensors tokenizerkwargs def forwardself modelinputs xxxforsequenceclassification models should not use usecachetrue even if it s supported modelforward self model forward if self framework pt else self model call if usecache in inspect signaturemodelforward parameters keys modelinputsusecache false return self modelmodelinputs def postprocessself modeloutputs functiontoapplynone topk1 legacytrue legacy is used to determine if we re running the naked pipeline and in backward compatibility mode or if running the pipeline with pipeline topk1 we re running the more natural result containing the list default value before setparameters if functiontoapply is none if self model config problemtype multilabelclassification or self model config numlabels 1 functiontoapply classificationfunction sigmoid elif self model config problemtype singlelabelclassification or self model config numlabels 1 functiontoapply classificationfunction softmax elif hasattrself model config functiontoapply and functiontoapply is none functiontoapply self model config functiontoapply else functiontoapply classificationfunction none outputs modeloutputslogits0 outputs outputs numpy if functiontoapply classificationfunction sigmoid scores sigmoidoutputs elif functiontoapply classificationfunction softmax scores softmaxoutputs elif functiontoapply classificationfunction none scores outputs else raise valueerrorfunrecognized functiontoapply argument functiontoapply if topk 1 and legacy return label self model config id2labelscores argmax item score scores max item dictscores label self model config id2labeli score score item for i score in enumeratescores if not legacy dictscores sortkeylambda x xscore reversetrue if topk is not none dictscores dictscores topk return dictscores return_all_scores bool optional defaults to false whether to return all prediction scores or just the one of the predicted class function_to_apply str optional defaults to default the function to apply to the model outputs in order to retrieve the scores accepts four different values default if the model has a single label will apply the sigmoid function on the output if the model has several labels will apply the softmax function on the output sigmoid applies the sigmoid function on the output softmax applies the softmax function on the output none does not apply any function on the output text classification pipeline using any modelforsequenceclassification see the sequence classification examples task_summary sequence classification for more information example python from transformers import pipeline classifier pipeline model distilbert base uncased finetuned sst 2 english classifier this movie is disgustingly good label positive score 1 0 classifier director tried too much label negative score 0 996 learn more about the basics of using a pipeline in the pipeline tutorial pipeline_tutorial this text classification pipeline can currently be loaded from pipeline using the following task identifier sentiment analysis for classifying sequences according to positive or negative sentiments if multiple classification labels are available model config num_labels 2 the pipeline will run a softmax over the results if there is a single label the pipeline will run a sigmoid over the result the models that this pipeline can use are models that have been fine tuned on a sequence classification task see the up to date list of available models on huggingface co models https huggingface co models filter text classification using as default argument because we re going to use top_k none in user code to declare no top_k classify the text s given as inputs args args str or list str or dict str or list dict str one or several texts to classify in order to use text pairs for your classification you can send a dictionary containing text text_pair keys or a list of those top_k int optional defaults to 1 how many results to return function_to_apply str optional defaults to default the function to apply to the model outputs in order to retrieve the scores accepts four different values if this argument is not specified then it will apply the following functions according to the number of labels if the model has a single label will apply the sigmoid function on the output if the model has several labels will apply the softmax function on the output possible values are sigmoid applies the sigmoid function on the output softmax applies the softmax function on the output none does not apply any function on the output return a list or a list of list of dict each result comes as list of dictionaries with the following keys label str the label predicted score float the corresponding probability if top_k is used one such dictionary is returned per label todo try and retrieve it in a nicer way from _sanitize_parameters this pipeline is odd and return a list when single item is run it used to be valid to use a list of list of list for text pairs keeping this path for bc this is likely an invalid usage of the pipeline attempting to pass text pairs xxxforsequenceclassification models should not use use_cache true even if it s supported _legacy is used to determine if we re running the naked pipeline and in backward compatibility mode or if running the pipeline with pipeline top_k 1 we re running the more natural result containing the list default value before set_parameters
import inspect
import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES


def sigmoid(_outputs):
    return 1.0 / (1.0 + np.exp(-_outputs))


def softmax(_outputs):
    maxes = np.max(_outputs, axis=-1, keepdims=True)
    shifted_exp = np.exp(_outputs - maxes)
    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)


class ClassificationFunction(ExplicitEnum):
    SIGMOID = "sigmoid"
    SOFTMAX = "softmax"
    NONE = "none"


@add_end_docstrings(
    PIPELINE_INIT_ARGS,
    r,
)
class TextClassificationPipeline(Pipeline):
    

    return_all_scores = False
    function_to_apply = ClassificationFunction.NONE

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

        self.check_model_type(
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
            if self.framework == "tf"
            else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
        )

    def _sanitize_parameters(self, return_all_scores=None, function_to_apply=None, top_k="", **tokenizer_kwargs):
        
        
        preprocess_params = tokenizer_kwargs

        postprocess_params = {}
        if hasattr(self.model.config, "return_all_scores") and return_all_scores is None:
            return_all_scores = self.model.config.return_all_scores

        if isinstance(top_k, int) or top_k is None:
            postprocess_params["top_k"] = top_k
            postprocess_params["_legacy"] = False
        elif return_all_scores is not None:
            warnings.warn(
                "`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"
                " `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.",
                UserWarning,
            )
            if return_all_scores:
                postprocess_params["top_k"] = None
            else:
                postprocess_params["top_k"] = 1

        if isinstance(function_to_apply, str):
            function_to_apply = ClassificationFunction[function_to_apply.upper()]

        if function_to_apply is not None:
            postprocess_params["function_to_apply"] = function_to_apply
        return preprocess_params, {}, postprocess_params

    def __call__(self, *args, **kwargs):
        
        result = super().__call__(*args, **kwargs)
        
        _legacy = "top_k" not in kwargs
        if isinstance(args[0], str) and _legacy:
            
            return [result]
        else:
            return result

    def preprocess(self, inputs, **tokenizer_kwargs) -> Dict[str, GenericTensor]:
        return_tensors = self.framework
        if isinstance(inputs, dict):
            return self.tokenizer(**inputs, return_tensors=return_tensors, **tokenizer_kwargs)
        elif isinstance(inputs, list) and len(inputs) == 1 and isinstance(inputs[0], list) and len(inputs[0]) == 2:
            
            return self.tokenizer(
                text=inputs[0][0], text_pair=inputs[0][1], return_tensors=return_tensors, **tokenizer_kwargs
            )
        elif isinstance(inputs, list):
            
            raise ValueError(
                "The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"
                ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'
            )
        return self.tokenizer(inputs, return_tensors=return_tensors, **tokenizer_kwargs)

    def _forward(self, model_inputs):
        
        model_forward = self.model.forward if self.framework == "pt" else self.model.call
        if "use_cache" in inspect.signature(model_forward).parameters.keys():
            model_inputs["use_cache"] = False
        return self.model(**model_inputs)

    def postprocess(self, model_outputs, function_to_apply=None, top_k=1, _legacy=True):
        
        
        
        
        if function_to_apply is None:
            if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                function_to_apply = ClassificationFunction.SIGMOID
            elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                function_to_apply = ClassificationFunction.SOFTMAX
            elif hasattr(self.model.config, "function_to_apply") and function_to_apply is None:
                function_to_apply = self.model.config.function_to_apply
            else:
                function_to_apply = ClassificationFunction.NONE

        outputs = model_outputs["logits"][0]
        outputs = outputs.numpy()

        if function_to_apply == ClassificationFunction.SIGMOID:
            scores = sigmoid(outputs)
        elif function_to_apply == ClassificationFunction.SOFTMAX:
            scores = softmax(outputs)
        elif function_to_apply == ClassificationFunction.NONE:
            scores = outputs
        else:
            raise ValueError(f"Unrecognized `function_to_apply` argument: {function_to_apply}")

        if top_k == 1 and _legacy:
            return {"label": self.model.config.id2label[scores.argmax().item()], "score": scores.max().item()}

        dict_scores = [
            {"label": self.model.config.id2label[i], "score": score.item()} for i, score in enumerate(scores)
        ]
        if not _legacy:
            dict_scores.sort(key=lambda x: x["score"], reverse=True)
            if top_k is not None:
                dict_scores = dict_scores[:top_k]
        return dict_scores