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from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer |
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from typing import Dict, List, Any |
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from tokenizers.decoders import WordPiece |
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class EndpointHandler: |
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def __init__(self, path="."): |
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model = AutoModelForTokenClassification.from_pretrained(path) |
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tokenizer = AutoTokenizer.from_pretrained(path) |
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self.pipeline = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy='simple') |
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self.pipeline.tokenizer.backend_tokenizer.decoder = WordPiece() |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str` | `PIL.Image` | `np.array`) |
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kwargs |
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Return: |
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A :obj:`list` | `dict`: will be serialized and returned |
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""" |
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return self.pipeline(data['inputs']) |
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