Comments (3)
Apologies, managed to try it on GPU enabled cloud server and it was significantly faster.
from bertopic.
Yes! Using a GPU is highly recommended to speed-up the inference at the sentence-transformers stage.
However, if you do not have a GPU available to you, then you can actually use TF-IDF instead since BERTopic
allows for custom embeddings to be passed:
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
# Create TF-IDF sparse matrix
docs = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))['data']
vectorizer = TfidfVectorizer(min_df=5)
embeddings = vectorizer.fit_transform(docs)
# Run BERTopic with embeddings
model = BERTopic(allow_st_model=True)
topics, probabilities = model.fit_transform(docs, embeddings)
Note that I used the parameter allow_st_model
which basically uses a sentence-transformer model to fine-tune the topic representation. This should be very efficient regardless of using a GPU since you would only need to embed a few hundred words. However, you can set this to False
if you do not want to be using a sentence-transformer model at all.
EDIT: Did not saw your response but I will leave this up here for those who are interested in other embedding methods.
from bertopic.
Thanks @MaartenGr ! This was very useful.
from bertopic.
Related Issues (20)
- Getting probabilities for all topics given a document from loaded model HOT 1
- Issues with Zero-shot Topic Modeling regarding outliers and future operations HOT 3
- Switch from setup.py to pyproject.toml HOT 4
- Seed Words
- random openai issue with plain bertopic use HOT 18
- Nan Representative Docs when loading a serialized model HOT 1
- ModuleNotFoundError: Can't use LangChain with version 0.16.0 HOT 1
- Should raise an Exception when tokenizer is not defined HOT 1
- Handle Responsible AI scenarios for OpenAI HOT 2
- Warn when automatically choosing SklearnEmbedder backend HOT 3
- PartOfSpeech representation reproducibility and word with index 0 HOT 2
- Zero-Shot HOT 2
- Supervised topic model generating different topics to training data HOT 3
- Where is the full data set of embeddings? HOT 3
- Visualization in html page HOT 1
- Guided Modeling: Problem with seed_topic_list HOT 2
- Utilizing the GPU of MacBook Pro M3 to accelerate the process of fit_transform HOT 1
- Could we know the weights of each topic? HOT 6
- Can't reproduce same results when using cuml version of UMAP and HDBSCAN HOT 2
- approximate_distribution returns only 0s HOT 5
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from bertopic.