spacy

I use spaCy 1.6.0 on Ubuntu 14.04.4 LTS x64 with python3.5. To install the English model of spaCy, I tried to run: This gives me the error message: ubun@ner-3:~/NeuroNER-master/src$ python3.5 -m spacy.en.download Downloading parsing model Traceback (most recent call last): File "/usr/lib/python3.5/runpy.py", line 184, in _run_module_as_main "__main__", mod_spec) File "/usr/lib/python3.5/runpy.py", line 85, in _run_code exec(code, run_globals) File "/usr/local/lib/python3.5/dist-packages/spacy/en/download.py", line 25, in <module> plac.call(main) File "/usr/local/lib/python3.5/dist-packages

I am trying to deploy my Django and spaCy project to Heroku. But I am getting an error: No matching distribution found for en-core-web-sm (It is an ML model downloadable via pip). How can I solve this problem? The model is installed locally in a virtual environment and working alright. I got the requirements file via pip freeze. I am using Python 3.6.4. It doesn't look like pip install en-core-web-sm works either, so I'm wondering how you installed it locally? One possible solution is to get it from github instead of pypi, by adding this line in requirements.txt instead -e https://github.com

I'm trying to apply Spacy NLP (Natural Language Processing) pipline to a big text file like Wikipedia Dump. Here is my code based on Spacy's documentation example: from spacy.en import English input = open("big_file.txt") big_text= input.read() input.close() nlp= English() out = nlp.pipe([unicode(big_text, errors='ignore')], n_threads=-1) doc = out.next() Spacy applies all nlp operations like POS tagging, Lemmatizing and etc all at once. It is like a pipeline for NLP that takes care of everything you need in one step. Applying pipe method tho is supposed to make the process a lot faster by

How can I break a document (e.g., paragraph, book, etc) into sentences. For example, "The dog ran. The cat jumped" into ["The dog ran", "The cat jumped"] with spacy? The up-to-date answer is this: from __future__ import unicode_literals, print_function from spacy.lang.en import English # updated raw_text = 'Hello, world. Here are two sentences.' nlp = English() nlp.add_pipe(nlp.create_pipe('sentencizer')) # updated doc = nlp(raw_text) sentences = [sent.string.strip() for sent in doc.sents] From spacy's github support page from __future__ import unicode_literals, print_function from spacy.en

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 sucessfully : import spacy nlp = spacy.load('en_core_web_sm') raw_text = 'Hello, world.' doc = nlp(raw_text)

I installed spacy on my system and I want to parse/extract person name, organization for english. But I saw here , there is 4 model for english. And there is model versioning. I didn't get which model is large and which I have to choose for development? sm / md / lg refer to the sizes of the models (small, medium, large respectively). As it says on the models page you linked to, Model differences are mostly statistical. In general, we do expect larger models to be "better" and more accurate overall. Ultimately, it depends on your use case and requirements. We recommend starting with the

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 entities into the existing model.But after re-training the model using spacy , it overriding the existing