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wzkariampuzha
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Update extract_abs.py
Browse files- extract_abs.py +1 -1
extract_abs.py
CHANGED
@@ -94,7 +94,7 @@ def get_diseases(sentence:str, GARD_dict:Dict[str,str], max_length:int) -> Tuple
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## Section: Prepare ML/DL Models
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# This fuction prepares the model. Should call before running in notebook. -- The [Any] Type is a Huggingface Pipeline variable
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# Default with typing from here: https://stackoverflow.com/questions/38727520/how-do-i-add-default-parameters-to-functions-when-using-type-hinting
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def init_NER_pipeline(name_or_path_to_model_folder:str = "./EpiExtract4GARD-v2") -> Tuple[Any, Set[str]]: #NER_pipeline, entities = init_NER_pipeline()
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tokenizer = BertTokenizer.from_pretrained(name_or_path_to_model_folder)
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custommodel = AutoModelForTokenClassification.from_pretrained(name_or_path_to_model_folder)
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customNER = pipeline('ner', custommodel, tokenizer=tokenizer, aggregation_strategy='simple')
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## Section: Prepare ML/DL Models
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# This fuction prepares the model. Should call before running in notebook. -- The [Any] Type is a Huggingface Pipeline variable
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# Default with typing from here: https://stackoverflow.com/questions/38727520/how-do-i-add-default-parameters-to-functions-when-using-type-hinting
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+
def init_NER_pipeline(name_or_path_to_model_folder:str = "./EpiExtract4GARD-v2/") -> Tuple[Any, Set[str]]: #NER_pipeline, entities = init_NER_pipeline()
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tokenizer = BertTokenizer.from_pretrained(name_or_path_to_model_folder)
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custommodel = AutoModelForTokenClassification.from_pretrained(name_or_path_to_model_folder)
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customNER = pipeline('ner', custommodel, tokenizer=tokenizer, aggregation_strategy='simple')
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