realpython / realpython/materials
textcat -> architecture extra fields not permitted
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Description
Good evening sir,
I've been trying to practise what I've learned from your tutorial:(https://realpython.com/sentiment-analysis-python/) using PyCharm.
And these lines:
textcat.add_label("pos")
textcat.add_label("neg")
generated a warning ==>
#Cannot find reference 'add_label' in '(Doc) -> Doc | (Doc) -> Doc'
I thought it maybe because of a difference between my spacy version and yours, so I run the script anyway, but then I got the an error from this line==>
textcat = nlp.create_pipe("textcat", config={"architecture": "simple_cnn"})
Error msg:
raise ConfigValidationError(
thinc.config.ConfigValidationError:
Config validation error
textcat -> architecture extra fields not permitted
{'nlp': <spacy.lang.en.English object at 0x0000015E74F625E0>, 'name': 'textcat', 'architecture': 'simple_cnn', 'model': {'@architectures': 'spacy.TextCatEnsemble.v2', 'linear_model': {'@architectures': 'spacy.TextCatBOW.v1', 'exclusive_classes': True, 'ngram_size': 1, 'no_output_layer': False}, 'tok2vec': {'@architectures': 'spacy.Tok2Vec.v2', 'embed': {'@architectures': 'spacy.MultiHashEmbed.v1', 'width': 64, 'rows': [2000, 2000, 1000, 1000, 1000, 1000], 'attrs': ['ORTH', 'LOWER', 'PREFIX', 'SUFFIX', 'SHAPE', 'ID'], 'include_static_vectors': False}, 'encode': {'@architectures': 'spacy.MaxoutWindowEncoder.v2', 'width': 64, 'window_size': 1, 'maxout_pieces': 3, 'depth': 2}}}, 'threshold': 0.5, '@factories': 'textcat'}
I'm very new to spacy and data analysis over all, so please excuse me if it's a stupid mistake from my side.
Thank you
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Research direction
Start with the sentiment-analysis tutorial linked in the issue and the lines using nlp.create_pipe("textcat", config={"architecture": "simple_cnn"}) and textcat.add_label. Check the spaCy version expected by the tutorial against the version producing the ConfigValidationError. Done means the example runs without the extra-fields error and the label calls are valid for the supported version.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 30/100