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

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