soniacq / textexplorer Goto Github PK

Word Frequency:
Analyzing 69 documents (positive category)
Analyzing 65 documents (negative category)
Named Entity Recognition:
Analyzing 69 documents (positive category)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-2065f93d9da3> in <module>
----> 1 VisualTextAnalyzer.plot_text_summary(yelp_data, category_column='category', text_column='comments')

~/workspace/nyu/d3m/piracy-demo/TextExplorer/VisualTextAnalyzer/_data_preprocessing.py in plot_text_summary(data, category_column, text_column, positive_label, negative_label, words_entities)
    343     processed_data = {}
    344     if words_entities is None:
--> 345         processed_data = get_words_entities(data,category_column, text_column, positive_label, negative_label)
    346         global_processed_data = processed_data
    347     else:

~/workspace/nyu/d3m/piracy-demo/TextExplorer/VisualTextAnalyzer/_data_preprocessing.py in get_words_entities(data, category_column, text_column, positive_label, negative_label)
    261     processed_data["words"] =  get_words (positive_texts, negative_texts, labels)
    262     print('Named Entity Recognition:')
--> 263     processed_data["entities"] = get_entities (positive_texts, negative_texts, labels)
    264     raw_text = {}
    265     raw_text['positive_texts'] = positive_texts

~/workspace/nyu/d3m/piracy-demo/TextExplorer/VisualTextAnalyzer/_data_preprocessing.py in get_entities(positive_texts, negative_texts, labels)
    219 
    220 def get_entities (positive_texts, negative_texts, labels):
--> 221     positive_entities = get_entities_frequency(positive_texts, labels['pos'])
    222     negative_entities = get_entities_frequency(negative_texts, labels['neg'])
    223 

~/workspace/nyu/d3m/piracy-demo/TextExplorer/VisualTextAnalyzer/_data_preprocessing.py in get_entities_frequency(texts, label)
    191     alias = {'ORG':'ORGANIZATION', 'LOC':'PLACE', 'GPE':'CITY/COUNTRY', 'NORP':'GROUP', 'FAC':'BUILDING'}
    192     unique_entities = {}
--> 193     for idx, doc in enumerate(nlp.pipe(texts, n_threads=16, batch_size=100)):
    194         for entity in doc.ents:
    195             if entity.label_ in {'CARDINAL', 'ORDINAL', 'QUANTITY'}:

TypeError: pipe() got an unexpected keyword argument 'n_threads'

Name: spacy
Version: 3.0.3
Summary: Industrial-strength Natural Language Processing (NLP) in Python
Home-page: https://spacy.io
Author: Explosion
Author-email: [email protected]
License: MIT
Location: ~/miniconda2/envs/myenv/lib/python3.6/site-packages
Requires: preshed, tqdm, typer, pathy, srsly, requests, importlib-metadata, murmurhash, cymem, thinc, setuptools, pydantic, jinja2, packaging, spacy-legacy, typing-extensions, wasabi, numpy, catalogue, blis
Required-by: en-core-web-sm, text-labeling, visual-text-explorer