a h 1@sddlZddlmZddlZddlmZddlmZm Z ddl m Z m Z m Z e eZGdd d e Zee d d Gd d d e ZdS)N)Union)TruncationStrategy)add_end_docstringslogging)ArgumentHandler ChunkPipelinebuild_pipeline_init_argsc@s eZdZdZddZddZdS)%ZeroShotClassificationArgumentHandlerz Handles arguments for zero-shot for text classification by turning each possible label into an NLI premise/hypothesis pair. cCs"t|trdd|dD}|S)NcSsg|]}|r|qS)strip.0labelr r k/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/pipelines/zero_shot_classification.py zGZeroShotClassificationArgumentHandler._parse_labels..,) isinstancestrsplit)selflabelsr r r _parse_labelss z3ZeroShotClassificationArgumentHandler._parse_labelscst|dkst|dkr td|dkrBtddt|trR|g}g}|D]|fdd|DqZ||fS)Nrz>You must include at least one label and at least one sequence.z"The provided hypothesis_template "z" was not able to be formatted with the target labels. Make sure the passed template includes formatting syntax such as {} where the label should go.csg|]}|gqSr )formatrhypothesis_templatesequencer rr'rzBZeroShotClassificationArgumentHandler.__call__..)len ValueErrorrrrextend)r sequencesrrsequence_pairsr rr__call__s  z.ZeroShotClassificationArgumentHandler.__call__N)__name__ __module__ __qualname____doc__rr$r r r rr sr T)Z has_tokenizercseZdZdZdZdZdZdZeffdd Z e ddZ dde j fdd Zd d Zeeeefd fd d ZdddZddZdddZZS)ZeroShotClassificationPipelinea NLI-based zero-shot classification pipeline using a `ModelForSequenceClassification` trained on NLI (natural language inference) tasks. Equivalent of `text-classification` pipelines, but these models don't require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is **much** more flexible. Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis pair and passed to the pretrained model. Then, the logit for *entailment* is taken as the logit for the candidate label being valid. Any NLI model can be used, but the id of the *entailment* label must be included in the model config's :attr:*~transformers.PretrainedConfig.label2id*. Example: ```python >>> from transformers import pipeline >>> oracle = pipeline(model="facebook/bart-large-mnli") >>> oracle( ... "I have a problem with my iphone that needs to be resolved asap!!", ... candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"], ... ) {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]} >>> oracle( ... "I have a problem with my iphone that needs to be resolved asap!!", ... candidate_labels=["english", "german"], ... ) {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['english', 'german'], 'scores': [0.814, 0.186]} ``` Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial) This NLI pipeline can currently be loaded from [`pipeline`] using the following task identifier: `"zero-shot-classification"`. The models that this pipeline can use are models that have been fine-tuned on an NLI task. See the up-to-date list of available models on [huggingface.co/models](https://huggingface.co/models?search=nli). FTcs0||_tj|i||jdkr,tddS)NzFailed to determine 'entailment' label id from the label2id mapping in the model config. Setting to -1. 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The default template works well in many cases, but it may be worthwhile to experiment with different templates depending on the task setting. multi_label (`bool`, *optional*, defaults to `False`): Whether or not multiple candidate labels can be true. If `False`, the scores are normalized such that the sum of the label likelihoods for each sequence is 1. If `True`, the labels are considered independent and probabilities are normalized for each candidate by doing a softmax of the entailment score vs. the contradiction score. 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