Adding doctest for `token-classification` pipeline. (#20265)
* Adding doctest for `token-classification` pipeline. * Adding doctest to `token-classification` pipeline. * Remove nested_simplify.
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@ -88,6 +88,29 @@ class TokenClassificationPipeline(Pipeline):
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Named Entity Recognition pipeline using any `ModelForTokenClassification`. See the [named entity recognition
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examples](../task_summary#named-entity-recognition) for more information.
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Example:
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```python
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>>> from transformers import pipeline
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>>> token_classifier = pipeline(model="Jean-Baptiste/camembert-ner", aggregation_strategy="simple")
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>>> sentence = "Je m'appelle jean-baptiste et je vis à montréal"
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>>> token_classifier(sentence)
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[{'entity_group': 'PER', 'score': 0.9931, 'word': 'jean-baptiste', 'start': 12, 'end': 26}, {'entity_group': 'LOC', 'score': 0.998, 'word': 'montréal', 'start': 38, 'end': 47}]
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>>> token = tokens[0]
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>>> # Start and end provide an easy way to highlight words in the original text.
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>>> sentence[token["start"] : token["end"]]
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' jean-baptiste'
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>>> # Some models use the same idea to do part of speech.
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>>> syntaxer = pipeline(model="vblagoje/bert-english-uncased-finetuned-pos", aggregation_strategy="simple")
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>>> syntaxer("My name is Sarah and I live in London")
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[{'entity_group': 'PRON', 'score': 0.999, 'word': 'my', 'start': 0, 'end': 2}, {'entity_group': 'NOUN', 'score': 0.997, 'word': 'name', 'start': 3, 'end': 7}, {'entity_group': 'AUX', 'score': 0.994, 'word': 'is', 'start': 8, 'end': 10}, {'entity_group': 'PROPN', 'score': 0.999, 'word': 'sarah', 'start': 11, 'end': 16}, {'entity_group': 'CCONJ', 'score': 0.999, 'word': 'and', 'start': 17, 'end': 20}, {'entity_group': 'PRON', 'score': 0.999, 'word': 'i', 'start': 21, 'end': 22}, {'entity_group': 'VERB', 'score': 0.998, 'word': 'live', 'start': 23, 'end': 27}, {'entity_group': 'ADP', 'score': 0.999, 'word': 'in', 'start': 28, 'end': 30}, {'entity_group': 'PROPN', 'score': 0.999, 'word': 'london', 'start': 31, 'end': 37}]
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```
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[Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
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This token recognition pipeline can currently be loaded from [`pipeline`] using the following task identifier:
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`"ner"` (for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous).
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