Comments (1)
There is the full
parameter which returns all the representations as a dictionary that you can access. You can use that to access the KeyBERTInspired representations.
from bertopic.
Related Issues (20)
- Chosen represented Topic HOT 5
- merge_topics from a model loaded from safe tensors HOT 1
- Add test coverage around OpenAI(BaseRepresentation) HOT 3
- Can't import bertopic in pycharm but runs fine in Anaconda prompt console HOT 3
- Adding representation model does not change topic_model.get_document_info results() HOT 2
- Installing bertopic cause Spyder 5.5.1 in Anaconda to not launch ith errors. HOT 2
- Updating and Pushing a BERTopic Model with New Documents to Hugging Face Hub still shows old no of training document HOT 3
- Calculating Probabilities on Zero-Shot Learning HOT 5
- Verbose setting gets lost when embeddings and embedding model provided HOT 1
- top_n_words doesn't work on zero-shot modeling HOT 4
- Incompatibility with Python 3.12.1 on Linux HOT 3
- partial_fit example code does not work HOT 1
- partial_fit throws warning and inf scores HOT 2
- BERTopic - Topic reduction produced 18 topics when nr_topics=96 HOT 1
- KeyError: 'topics_from' HOT 19
- incorrect result by topic_model.get_topic_info() due to zeroshot_topic_list was set HOT 12
- UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128)
- UnicodeEncodeError: 'ascii' codec can't encode characters in position 18-20: ordinal not in range(128) HOT 6
- ImportError: cannot import name 'Dataset' from 'datasets' (unknown location) HOT 1
- reduce_outliers and update_topics remove stop_words and ngram_range effects HOT 1
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from bertopic.