Comments (3)
I believe this is implemented in BERTopic if you set verbose=True
when instantiating the model. It might be different for certain models but it should work for the default SBERT models.
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Hi Karol,
This was removed in v0.4 as there a few steps that do not have a tqdm process bar accessible. There are roughly four steps in BERTopic:
- Extract embeddings (tqdm possible)
- Create clusters with HDBSCAN+UMAP (tqdm not possible)
- Create c-TF-IDF matrix (tqdm not possible)
- Improve coherence words (tqdm possible)
So it is possible to construct tqdm bar for steps 1 and 4 but unfortunately not for steps 2 and 3. Therefore, it is quite difficult to get an estimate of the entire training duration.
Having said that, I might add the progress bars for steps 1 and 4 in a future version.
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Is this still on the list? It would be really useful for large corpora...
from bertopic.
Related Issues (20)
- Drop support for Python 3.7 HOT 3
- Issue when using topic representation HOT 1
- Reducing Outliers of Loaded Model HOT 1
- `IndexError: list index out of range` when using zeroshot_topic_list in 0.16.1 HOT 10
- Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28`How to slove this problem?
- Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` HOT 1
- Add support for Python 3.10+ HOT 2
- [inhomogeneous shape unresolved] [Colab] ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part. HOT 1
- Huggingface transformer does not load as expected HOT 3
- BERTopic with large dataset (10-20 Million) HOT 1
- datamap visualisation does not work. HOT 1
- datamap visulisation does not work. HOT 4
- Request: Zeroshot option to assign unassigned documents to outliers rather than reclustering HOT 3
- should we reduce the dimensionality of topic_model.topic_embeddings_ ? HOT 2
- Bertopic verion 0.16.1 fails with zero_shot topics (works fine with 0.16.0) HOT 1
- bertopic version 0.16.0 - probs are empty when executing with zero_shot HOT 3
- bertopic version 0.16.0 - when adding representation model together with zeroshot_topic_list end with failure HOT 3
- Disable warning for update_topics() HOT 1
- Wrong link in Algorithm Documentation HOT 2
- Getting probabilities for all topics given a document from loaded model HOT 1
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