ner

问题 I am trying to figure out why my custom SpaCy NER model isn't saving to disk using nlp.to_disk . I am using this condition in my python script: # save model to output directory if output_dir is not None: output_dir = Path(output_dir) if not output_dir.exists(): output_dir.mkdir() nlp.to_disk(output_dir) print("Saved model to", output_dir) The output_dir is defined at the top of my script as: @plac.annotations( model=("Model name. Defaults to blank 'en' model.", "option", "m", str), output_dir

问题 Is there anyway by SpaCy to replace entity detected by SpaCy NER with its label? For example: I am eating an apple while playing with my Apple Macbook. I have trained NER model with SpaCy to detect "FRUITS" entity and the model successfully detects the first "apple" as "FRUITS", but not the second "Apple". I want to do post-processing of my data by replacing each entity with its label, so I want to replace the first "apple" with "FRUITS". The sentence will be " I am eating an FRUITS while

问题 Is there anyway by SpaCy to replace entity detected by SpaCy NER with its label? For example: I am eating an apple while playing with my Apple Macbook. I have trained NER model with SpaCy to detect "FRUITS" entity and the model successfully detects the first "apple" as "FRUITS", but not the second "Apple". I want to do post-processing of my data by replacing each entity with its label, so I want to replace the first "apple" with "FRUITS". The sentence will be " I am eating an FRUITS while

问题 Code: import spacy nlp = spacy.load("en_core_web_md") #read txt file, each string on its own line with open("./try.txt","r") as f: texts = f.read().splitlines() #substitute entities with their TAGS docs = nlp.pipe(texts) out = [] for doc in docs: out_ = "" for tok in doc: text = tok.text if tok.ent_type_: text = tok.ent_type_ out_ += text + tok.whitespace_ out.append(out_) # write to file with open("./out_try.txt","w") as f: f.write("\n".join(out)) Contents of input file: Georgia recently

问题 I have a dataset in CoNLL NER format which is basically a TSV file with two fields. The first field contains tokens from some text - one token per line (each punctuation symbol is also considered a token there) and the second field contains named entity tags for tokens in BIO format. I would like to load this dataset into spaCy, infer new named entity tags for the text with my model and write these tags into the same TSV file as the new third column. All I know is that I can infer named

问题 I have a dataset in CoNLL NER format which is basically a TSV file with two fields. The first field contains tokens from some text - one token per line (each punctuation symbol is also considered a token there) and the second field contains named entity tags for tokens in BIO format. I would like to load this dataset into spaCy, infer new named entity tags for the text with my model and write these tags into the same TSV file as the new third column. All I know is that I can infer named

来源： https://stackoverflow.com/questions/63456702/spacy-a-token-can-only-be-part-of-one-entity-so-make-sure-the-entities-youre

来源： https://stackoverflow.com/questions/63456702/spacy-a-token-can-only-be-part-of-one-entity-so-make-sure-the-entities-youre

来源： https://stackoverflow.com/questions/60381170/which-deep-learning-algorithm-does-spacy-uses-when-we-train-custom-model

来源： https://stackoverflow.com/questions/60381170/which-deep-learning-algorithm-does-spacy-uses-when-we-train-custom-model