U 9%e(@sddlZddlZddlmZddlZddlmZmZm Z m Z ddl m Z m Z mZe rbddlmZe rtddlmZd d Zd d ZGd ddeZee dGdddeZdS)N)Dict) ExplicitEnumadd_end_docstringsis_tf_availableis_torch_available)PIPELINE_INIT_ARGS GenericTensorPipeline)2TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES)/MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMEScCsddt| S)Ng?)npexp)_outputsri/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/pipelines/text_classification.pysigmoidsrcCs0tj|ddd}t||}||jdddS)NT)ZaxisZkeepdims)rmaxrsum)rZmaxesZ shifted_exprrrsoftmaxsrc@seZdZdZdZdZdS)ClassificationFunctionrrnoneN)__name__ __module__ __qualname__SIGMOIDSOFTMAXNONErrrrrsra 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. csheZdZdZdZejZfddZdddZ fd d Z e e e fd d d ZddZdddZZS)TextClassificationPipelinea= 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). Fc s*tjf|||jdkr tntdS)Ntf)super__init__Zcheck_model_type frameworkr r )selfkwargs __class__rrr#Ss z#TextClassificationPipeline.__init__NcKs|}i}t|jjdr(|dkr(|jjj}t|ts:|dkrL||d<d|d<n*|dk rvtdt|rnd|d<nd|d<t|t rt | }|dk r||d<|i|fS)Nreturn_all_scorestop_kF_legacyz`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`.rfunction_to_apply) hasattrmodelconfigr* isinstanceintwarningswarn UserWarningstrrupper)r%r*r-r+tokenizer_kwargsZpreprocess_paramsZpostprocess_paramsrrr_sanitize_parameters\s(     z/TextClassificationPipeline._sanitize_parameterscs6tj||}d|k}t|dtr.|r.|gS|SdS)aD 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. r+rN)r"__call__r1r6)r%argsr&resultr,r'rrr:zs "z#TextClassificationPipeline.__call__)returncKs|j}t|tr&|jf|d|i|St|trt|dkrt|dtrt|ddkr|jf|dd|dd|d|St|trtd|j|fd|i|S)Nreturn_tensorsrrr)textZ text_pairr>zThe 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.)r$r1dictZ tokenizerlistlen ValueError)r%inputsr8r>rrr preprocesss  4  z%TextClassificationPipeline.preprocesscCsB|jdkr|jjn|jj}dt|jkr6d|d<|jf|S)NptZ use_cacheF)r$r/forwardcallinspect signature parameterskeys)r%Z model_inputsZ model_forwardrrr_forwardsz#TextClassificationPipeline._forwardrTcsN|dkrxjjjdks$jjjdkr,tj}nLjjjdksHjjjdkrPtj}n(tjjdrr|dkrrjjj}ntj }|dd}| }|tjkrt |}n2|tjkrt |}n|tj kr|}nt d||dkr|rjjj||dSfd d t|D}|sJ|jd d d d|dk rJ|d|}|S)NZmulti_label_classificationrZsingle_label_classificationr-Zlogitsrz+Unrecognized `function_to_apply` argument: labelscorecs(g|] \}}jjj||dqS)rN)r/r0id2labelitem).0irPr%rr sz:TextClassificationPipeline.postprocess..cSs|dS)NrPr)xrrrz8TextClassificationPipeline.postprocess..T)keyreverse)r/r0Z problem_typeZ num_labelsrrrr.r-rnumpyrrrCrQZargmaxrRr enumeratesort)r%Z model_outputsr-r+r,outputsZscoresZ dict_scoresrrUr postprocesss6       $   z&TextClassificationPipeline.postprocess)NNr))NrT)rrr__doc__r*rrr-r#r9r:rr6r rErMr` __classcell__rrr'rr "s  +r )rIr3typingrr\rutilsrrrrbaser r r Zmodels.auto.modeling_tf_autor Zmodels.auto.modeling_autor rrrr rrrrs"