fTh0SSKrSSKJrJr SSKrSSKJr SSKJ r J r SSK J r J r Jr \ R"\5r"SS \ 5r\ "\"S S 95"S S \ 55rg)N)ListUnion)TruncationStrategy)add_end_docstringslogging)ArgumentHandler ChunkPipelinebuild_pipeline_init_argsc$\rSrSrSrSrSrSrg)%ZeroShotClassificationArgumentHandlerz} Handles arguments for zero-shot for text classification by turning each possible label into an NLI premise/hypothesis pair. c[U[5(aEURS5Vs/sH)o"R5(dMUR5PM+ nnU$s snf)N,) isinstancestrsplitstrip)selflabelslabels g/var/www/auris/envauris/lib/python3.13/site-packages/transformers/pipelines/zero_shot_classification.py _parse_labels3ZeroShotClassificationArgumentHandler._parse_labelssG fc " "17c1BT1Bkkmmekkm1BFT Us AAc r[U5S:Xd[U5S:Xa [S5eURUS5U:Xa[SRU55e[U[5(aU/n/nUH5nUR UVs/sHoeURU5/PM sn5 M7 XA4$s snf)Nrz>You must include at least one label and at least one sequence.zThe provided hypothesis_template "{}" was not able to be formatted with the target labels. Make sure the passed template includes formatting syntax such as {{}} where the label should go.)len ValueErrorformatrrextend)r sequencesrhypothesis_templatesequence_pairssequencers r__call__.ZeroShotClassificationArgumentHandler.__call__s v;! s9~2]^ ^  % %fQi 04G Gw&,-   i % %" I!H  ! !^d"e^dUZ.A.H.H.O#P^d"e f"((#fs B4 N)__name__ __module__ __qualname____firstlineno____doc__rr%__static_attributes__r'rrrs  )r.rT) has_tokenizerc^\rSrSrSr\"54U4Sjjr\S5rSS\ R4Sjr Sr S\ \\\44U4S jjrSS jrS rSS jrS rU=r$)ZeroShotClassificationPipeline.a 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). c~>Xl[TU]"U0UD6 URS:Xa[R S5 gg)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__s rr7'ZeroShotClassificationPipeline.__init__Ws@' $)&)    # NNk  $r.cURRRR5H-upUR 5R S5(dM+Us $ g)Nentailr4)modelconfiglabel2iditemslower startswith)rrinds rr8,ZeroShotClassificationPipeline.entailment_id`sH**++44::> # # + LL) (,~~'?'?DNN $ ^^#5-% $F2 % c!f$"'9#1#1AA ( ! sA44 B;>0B64B66B;c URSS5bUSUS'[RS5 0nSU;a!URR US5US'SU;aUSUS'0nSU;aUSUS'U0U4$)N multi_class multi_labelzThe `multi_class` argument has been deprecated and renamed to `multi_label`. `multi_class` will be removed in a future version of Transformers.candidate_labelsr")getr9r:r5r)rr=preprocess_paramspostprocess_paramss r_sanitize_parameters3ZeroShotClassificationPipeline._sanitize_parameterss ::mT * 6$*=$9F= ! NNU   '484E4E4S4STZ[mTn4o 0 1 F *7=>S7T 3 4 F "06}0E } - "&888r.r!c>[U5S:XaO,[U5S:XaSU;a USUS'O[SU35e[TU] "U40UD6$)aJ 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. rr r]z%Unable to understand extra arguments )rrr6r%)rr!r<r=r>s rr%'ZeroShotClassificationPipeline.__call__s^H t9>  Y!^ 2& @)-aF% &DTFKL Lw 4V44r.c## URXU5upE[[X$55H6unupxURU/5n UUSU[ U5S- :HS.U Ev M8 g7f)Nrr candidate_labelr$is_last)r5 enumerateziprXr) rrVr]r"r#r!irg sequence_pair model_inputs r preprocess)ZeroShotClassificationPipeline.preprocesss~$($5$5fPc$d!3>T !^^Ta$bTa&H%5%;%;%=%C%C%ETa$bcF^^M$ZM&H%5%;%;%=M$Z[F LLO  ! !>>=R*@A #./14 ..M%2a%7rQ "239I8Y3Y"Z VV/0266:M3N3R3RSU`d3R3eeFF^F-S$2D2D-DEMVVM*RVVM-B-F-FrTX-F-YYF!2!2!456!! 4<=Hq'*H=Q[)002  1UF$b$Z*>sG=H *HH H)r5)NzThis example is {}.)F)r(r)r*r+r,rr7propertyr8r ONLY_FIRSTrXrarrrr%rnrrr- __classcell__)r>s@rr1r1.su%N$I#J'+tPbPmPm(T9$+5d3i(+5Z $  r.r1)rvtypingrrrrtokenization_utilsrutilsrrbaser r r get_loggerr(r9rr1r'r.rrsc3/JJ   H %)O)@,4@A] ]] B] r.