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Contextualized-topic-models Alternatives
Similar projects and alternatives to contextualized-topic-models
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BERTopic
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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OCTIS
OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)
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tika-python
Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.
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transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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Top2Vec
Top2Vec learns jointly embedded topic, document and word vectors.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Sentimentanalysis
Language independent sentiment analysis
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mlconjug3
A Python library to conjugate verbs in French, English, Spanish, Italian, Portuguese and Romanian (more soon) using Machine Learning techniques.
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contextualized-topic-models reviews and mentions
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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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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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(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
My team and I have recently released a python library called OCTIS (https://github.com/mind-Lab/octis) that allows you to automatically optimize the hyperparameters of a topic model according to a given evaluation metric (not log-likelihood). I guess, in your case, you might be interested in topic coherence. So you will get good quality topics with a low effort on the choice of the hyperparameters. Also, we included some state-of-the-art topic models, e.g. contextualized topic models (https://github.com/MilaNLProc/contextualized-topic-models).
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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)
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A note from our sponsor - InfluxDB
www.influxdata.com | 17 Apr 2024
Stats
MilaNLProc/contextualized-topic-models is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of contextualized-topic-models is Python.
Popular Comparisons
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