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Description
Hi @jantrienes ,
I am following the step mention at README.md but I am not able to get any result.
While loading the model I am facing this error. Please help me out how to solve it.
Select downloaded model
model = './model_bilstmcrf_ons_large-v0.2.0/final-model.pt'
Instantiate tokenizer
tokenizer = TokenizerFactory().tokenizer(corpus='ons', disable=("tagger", "ner"))
Output ==> 2022-04-11 10:22:37.046 | INFO | deidentify.tokenizer.base:tokenizer:36 - Tokenizer for corpus: ons
Load tagger with a downloaded model file and tokenizer
tagger = FlairTagger(model=model, tokenizer=tokenizer, verbose=False)
Annotate your documents
annotated_docs = tagger.annotate(documents)
Output ==>
2022-04-11 10:30:37.252 | INFO | deidentify.taggers.flair_tagger:init:20 - Load flair model from /content/drive/MyDrive/47billion/model_bilstmcrf_ons_large-v0.2.0/final-model.pt
2022-04-11 10:30:37,257 loading file /content/drive/MyDrive/47billion/model_bilstmcrf_ons_large-v0.2.0/final-model.pt
2022-04-11 10:30:59,342 SequenceTagger predicts: Dictionary with 33 tags: , O, B-Date, B-Name, I-Name, B-Hospital, B-Internal_Location, B-Care_Institute, B-Initials, B-Organization_Company, I-Organization_Company, I-Date, B-ID, I-ID, I-Care_Institute, B-Address, I-Address, B-Age, I-Age, I-Hospital, I-Internal_Location, B-Phone_fax, B-Profession, I-Profession, I-Initials, B-Other, I-Other, B-URL_IP, I-Phone_fax, B-Email, B-SSN, ,
2022-04-11 10:31:00.242 | INFO | deidentify.taggers.flair_tagger:init:22 - Finish loading flair model.
TypeError Traceback (most recent call last)
in ()
2 tagger = FlairTagger(model=model, tokenizer=tokenizer, verbose=False)
3 # Annotate your documents
----> 4 annotated_docs = tagger.annotate(documents)
1 frames
/usr/local/lib/python3.7/dist-packages/deidentify/methods/bilstmcrf/flair_utils.py in standoff_to_flair_sents(docs, tokenizer, verbose)
59 flair_sents = []
60 for sent in sents:
---> 61 flair_sent = Sentence()
62 for token in sent:
63 if token.text.isspace():
TypeError: init() missing 1 required positional argument: 'text'