Comments (3)
MaartenGr
commented on June 18, 2024
2
No problem! It's actually already there 😉
BERTopic/bertopic/_bertopic.py
Lines 155 to 159 in 8985f26
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MaartenGr
commented on June 18, 2024
Thanks for sharing! This is actually not a bug but by design. The underlying idea is that users not familiar with the CountVectorizer can directly use the n_gram_range parameter. However, when using the vectorizer_model it should overwrite n_gram_range since you creating your own custom vectorizer model. Other parameters related to that should have no effect.
In other words, either you use the n_gram_range parameter directly from BERTopic or via vectorizer_model but never both.
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yuanjames
commented on June 18, 2024
Okay, I see, that's good, thanks for your always instant reply, cool design, you may consider adding some notes in doc. Thanks again.
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Related Issues (20)
- multimodal problem HOT 6
- Running time is too long when using Zero-Shot Classification HOT 6
- blank labels issue with 2d documents Visualization HOT 5
- AttributeError: 'NoneType' object has no attribute 'strip' HOT 7
- Can't install hdbscan on windows 11: "'building._hdbscan_tree' extension error: [WinError 5] Access is denied" HOT 4
- bertopic.representation.LangChain Unbound Local Error HOT 1
- Evaluating fit on new data? HOT 1
- list index out of range HOT 4
- AttributeError: 'BERTopic' object has no attribute 'c_tf_idf' HOT 3
- Dynamic Topic Modelling (Quantized Models / Vectorized DBs) HOT 3
- Assigning documents to multiple topics using zero-shot topic modeling HOT 1
- using representation model takes much longer HOT 1
- Best-performing embedding models? HOT 2
- Skip topic representation when reducing topics wiht nr_topic parameter HOT 1
- Issue with loading BERTopic model. 'NNDescent' object has no attribute '_bit_trees' HOT 3
- Problem with fitting and transforming model HOT 2
- Semantic Sentence Tokenization HOT 1
- self._c_tf_idf can make more efficient use of vectorizer model HOT 3
- Drop support for Python 3.7 HOT 3
- Issue when using topic representation HOT 1
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