TopMost
A Topic Modeling System Toolkit (by BobXWu)
contextualized-topic-models
A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.). (by MilaNLProc)
TopMost | contextualized-topic-models | |
---|---|---|
1 | 7 | |
146 | 1,166 | |
- | 1.1% | |
7.6 | 5.0 | |
about 2 months ago | 4 months ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
TopMost
Posts with mentions or reviews of TopMost.
We have used some of these posts to build our list of alternatives
and similar projects.
contextualized-topic-models
Posts with mentions or reviews of contextualized-topic-models.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-14.
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[Project]Topic modelling of tweets from the same user
In our experiments, CTM works well with tweets: https://github.com/MilaNLProc/contextualized-topic-models (I'm one of the authors)
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Extract words from large data set of reviews by sentiment
Use CTM https://github.com/MilaNLProc/contextualized-topic-models with sentiment labels to built distribution of words over labels
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Using Transformer for Topic Modeling - what are the options?
This library from MILA seems quite neat! I haven’t had the change to play with it though : https://github.com/MilaNLProc/contextualized-topic-models
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Catogorize the Data- Topic Modelling algorithm
a bit of shameless self-promotion, but we developed a topic model (https://github.com/MilaNLProc/contextualized-topic-models) that actually supports that use case!
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(NLP) Best practices for topic modeling and generating interesting topics?
If you use CTM, you can provide the topic model two inputs: the preprocessed texts (that will be used by the topic model to generate the topical words) and the unpreprocessed texts (to generate the contextualized representations that will be later concatenated to the document bag-of-word representation). We saw that this slightly improves the performance instead of providing BERT the already-preprocessed text. This feature is supported in the original implementation of CTM, not in OCTIS. See here: https://github.com/MilaNLProc/contextualized-topic-models#combined-topic-model
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Latest trends in topic modelling?
Cross-lingual Contextualized Topic Models with Zero-shot Learning from a team at MilaNLP which uses bag of words representations in combination with multi lingual embeddings from SBERT and works like a VAE (encode the input, use the encoded representation to decode back to a bag of words as close to the input as possible). Using SBERT embeddings makes their model generalise for other languages which may be useful. One major shortfall of this model as I understand is that it can't deal with long documents very elegantly - only up to BERT'S word limit (the workaround is to truncate and use the first words)
What are some alternatives?
When comparing TopMost and contextualized-topic-models you can also consider the following projects:
tf-transformers - State of the art faster Transformer with Tensorflow 2.0 ( NLP, Computer Vision, Audio ).
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
converse - Conversational text Analysis using various NLP techniques
OCTIS - OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
medspacy - Library for clinical NLP with spaCy.
PolyFuzz - Fuzzy string matching, grouping, and evaluation.