o h0@sddlZddlmZmZddlZddlmZddlm Z m Z ddl m Z m Z mZe eZGdd d e Ze e Gd d d eZdS) N)ListUnion)TruncationStrategy)add_end_docstringslogging)PIPELINE_INIT_ARGSArgumentHandler ChunkPipelinec@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 e/var/www/html/ai/venv/lib/python3.10/site-packages/transformers/pipelines/zero_shot_classification.py szGZeroShotClassificationArgumentHandler._parse_labels..,) isinstancestrsplit)selflabelsr r r _parse_labelss z3ZeroShotClassificationArgumentHandler._parse_labelscst|dks t|dkrtd|dkr tdt|tr(|g}g}|D]|fdd|Dq,||fS)Nrz>You must include at least one label and at least one sequence.zThe provided hypothesis_template "{}" 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)szBZeroShotClassificationArgumentHandler.__call__..)len ValueErrorrrrextend)r sequencesrrsequence_pairsr rr__call__s z.ZeroShotClassificationArgumentHandler.__call__N)__name__ __module__ __qualname____doc__rr$r r r rr s r cseZdZdZeffdd ZeddZddej fddZ d d Z d e e ee fffd 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). cs4||_tj|i||jdkrtddSdS)NzFailed to determine 'entailment' label id from the label2id mapping in the model config. Setting to -1. Define a descriptive label2id mapping in the model config to ensure correct outputs.) _args_parsersuper__init__ entailment_idloggerwarning)r args_parserargskwargs __class__r rr-Ws z'ZeroShotClassificationPipeline.__init__cCs2|jjjD]\}}|dr|SqdS)Nentailr*)modelconfiglabel2iditemslower startswith)rrindr r rr.`s z,ZeroShotClassificationPipeline.entailment_idTc Ks|j}|jjdurtd|jj|j_z |j|||||d}W|StyH}zdt|vr;|j||||tj d}n|WYd}~|Sd}~ww)ze Parse arguments and tokenize only_first so that hypothesis (label) is not truncated NzfTokenizer was not supporting padding necessary for zero-shot, attempting to use `pad_token=eos_token`)add_special_tokensreturn_tensorspadding truncationz too short) framework tokenizer pad_tokenr/error eos_token ExceptionrrDO_NOT_TRUNCATE) rr#r@r>rAr3r?inputser r r_parse_and_tokenizegs:     z2ZeroShotClassificationPipeline._parse_and_tokenizecKs||dddur|d|d<tdi}d|vr#|j|d|d<d|vr-|d|d<i}d|vr9|d|d<|i|fS)N multi_class multi_labelzThe `multi_class` argument has been deprecated and renamed to `multi_label`. `multi_class` will be removed in a future version of Transformers.candidate_labelsr)getr/r0r+r)rr3preprocess_paramspostprocess_paramsr r r_sanitize_parameterss    z3ZeroShotClassificationPipeline._sanitize_parametersr"csRt|dkrnt|dkrd|vr|d|d<ntd|tj|fi|S)a Classify the sequence(s) given as inputs. See the [`ZeroShotClassificationPipeline`] documentation for more information. Args: sequences (`str` or `List[str]`): The sequence(s) to classify, will be truncated if the model input is too large. candidate_labels (`str` or `List[str]`): The set of possible class labels to classify each sequence into. Can be a single label, a string of comma-separated labels, or a list of labels. hypothesis_template (`str`, *optional*, defaults to `"This example is {}."`): The template used to turn each label into an NLI-style hypothesis. This template must include a {} or similar syntax for the candidate label to be inserted into the template. For example, the default template is `"This example is {}."` With the candidate label `"sports"`, this would be fed into the model like `" sequence to classify This example is sports . "`. 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. Return: A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys: - **sequence** (`str`) -- The sequence for which this is the output. - **labels** (`List[str]`) -- The labels sorted by order of likelihood. - **scores** (`List[float]`) -- The probabilities for each of the labels. rrrNz%Unable to understand extra arguments )rr r,r$)rr"r2r3r4r rr$s $z'ZeroShotClassificationPipeline.__call__NThis example is {}.c csb||||\}}tt||D]\}\}}||g} ||d|t|dkd| VqdS)Nrrcandidate_labelris_last)r+ enumerateziprKr) rrIrNrr#r"irU sequence_pair model_inputr r r preprocesss z)ZeroShotClassificationPipeline.preprocesscsd}d}fdd|jjD}|jdkr|jjn|jj}dt|j vr.d|d<|jd i|}||dd |}|S) NrUrcsi|]}||qSr r )rkrIr r sz;ZeroShotClassificationPipeline._forward..pt use_cacheFrVrTr ) rCmodel_input_namesrBr7forwardcallinspect signature parameterskeys)rrIrUr model_inputs model_forwardoutputs model_outputsr r^r_forwardsz'ZeroShotClassificationPipeline._forwardFcs"dd|Ddd|D}tdd|D}|jd}t}||}|||df}|s5tdkr\|j} | dkr>dnd} |d| | gf} t| t| jdd d } | d } n|d|jf} t| t| jdd d } tt | d }|dfd d|D| d|f d S)NcSg|]}|dqS)rUr rrkr r rrz>ZeroShotClassificationPipeline.postprocess..cSrn)rr ror r rrrpcSsg|]}|dqS)logits)numpy)routputr r rrsrr*r.T)keepdims).rcsg|]}|qSr r )rrYrNr rrrp)rrscores) np concatenateshaperreshaper.expsumlistreversedargsorttolist)rrlrMr"rqNn num_sequencesreshaped_outputsr.contradiction_identail_contr_logitsrv entail_logitstop_indsr rur postprocesss(  z*ZeroShotClassificationPipeline.postprocess)NrS)F)r%r&r'r(r r-propertyr.r ONLY_FIRSTrKrRrrrr$r\rmr __classcell__r r r4rr).s'  * - r))retypingrrrrrwtokenization_utilsrutilsrrbaser r r get_loggerr%r/r r)r r r rs