spacy

问题 I have a sentence that has already been tokenized into words. I want to get the part of speech tag for each word in the sentence. When I check the documentation in SpaCy I realized it starts with the raw sentence. I don't want to do that because in that case, the spacy might end up with a different tokenization. Therefore, I wonder if using spaCy with the list of words (rather than a string) is possible or not ? Here is an example about my question: # I know that it does the following

问题 I am training customized Named Entity Recognition(NER) model using stanford NLP but the thing is i want to re-train the model . Example : Suppose i trained xyz model , then i will test it on some text if model detected somethings wrong then i (end user) will correct it and wanna re-train(append mode) the model on the corrected text. Stanford Doesn't provide re-training facility so thats why i shifted towards spacy library of python , where i can retrain the model means , i can append new

问题 I am training customized Named Entity Recognition(NER) model using stanford NLP but the thing is i want to re-train the model . Example : Suppose i trained xyz model , then i will test it on some text if model detected somethings wrong then i (end user) will correct it and wanna re-train(append mode) the model on the corrected text. Stanford Doesn't provide re-training facility so thats why i shifted towards spacy library of python , where i can retrain the model means , i can append new

问题 Hi I am working on implementing a multi-classification model (5 classes) with the new SpaCy Model en_pytt_bertbaseuncased_lg . The code for the new pipe is here: nlp = spacy.load('en_pytt_bertbaseuncased_lg') textcat = nlp.create_pipe( 'pytt_textcat', config={ "nr_class":5, "exclusive_classes": True, } ) nlp.add_pipe(textcat, last = True) textcat.add_label("class1") textcat.add_label("class2") textcat.add_label("class3") textcat.add_label("class4") textcat.add_label("class5") The code for the

问题 Hi I am working on implementing a multi-classification model (5 classes) with the new SpaCy Model en_pytt_bertbaseuncased_lg . The code for the new pipe is here: nlp = spacy.load('en_pytt_bertbaseuncased_lg') textcat = nlp.create_pipe( 'pytt_textcat', config={ "nr_class":5, "exclusive_classes": True, } ) nlp.add_pipe(textcat, last = True) textcat.add_label("class1") textcat.add_label("class2") textcat.add_label("class3") textcat.add_label("class4") textcat.add_label("class5") The code for the

问题 I have been trying to train a model with the same method as #887 is using, just for a test case. I have a question, what would be the best format for a training corpus to import in spacy. I have a text-file with a list of of entities that requires new entities for tagging. Let me explain my case, I follow the update.training script like this: nlp = spacy.load('en_core_web_md', entity=False, parser=False) ner= EntityRecognizer(nlp.vocab, entity_types=['FINANCE']) for itn in range(5): random

问题 I'm trying to integrate a custom PhraseMatcher() component into my nlp pipeline in a way that will allow me to load the custom Spacy model without having to re-add my custom components to a generic model on each load. How can I load a Spacy model containing custom pipeline components? I create the component, add it to my pipeline and save it with the following: import requests from spacy.lang.en import English from spacy.matcher import PhraseMatcher from spacy.tokens import Doc, Span, Token

问题 I am trying to evaluate a trained NER Model created using spacy lib. Normally for these kind of problems you can use f1 score (a ratio between precision and recall). I could not find in the documentation an accuracy function for a trained NER model. I am not sure if it's correct but I am trying to do it with the following way(example) and using f1_score from sklearn : from sklearn.metrics import f1_score import spacy from spacy.gold import GoldParse nlp = spacy.load("en") #load NER model test

问题 When running: import spacy nlp = spacy.load('en') the following is printed: Warning: no model found for 'en' Only loading the 'en' tokenizer. /site-packages/spacy/data is empty with the exception of the init file. all filepaths are only pointing to my single installation of python. Any help appreciated on resolving this. Thanks! Will 回答1: I had this same issue when I tried this on Windows 10 - the problem was the output of python -m spacy.en.download all said Linking successful but above that

问题 I'm trying to install spacy by running pip install spacy for python version 3.6.1 but continuously i'm getting errors like below,how to get rid of this issue? previously i was having cl.exe not found error, after that i added visual studio path in environment variables where cl.exe exists. Failed building wheel for spacy Running setup.py clean for spacy Running setup.py bdist_wheel for murmurhash ... error Complete output from command c:\users\sh00428701\appdata\local\programs\python\python36