Projects in Awesome Lists tagged with top2vec

https://github.com/ddangelov/top2vec

Top2Vec learns jointly embedded topic, document and word vectors.

bert document-embedding pre-trained-language-models semantic-search sentence-encoder sentence-transformers text-search text-semantic-similarity top2vec topic-modeling topic-modelling topic-search topic-vector word-embeddings

Last synced: 23 Apr 2025

https://github.com/ddangelov/Top2Vec

Top2Vec learns jointly embedded topic, document and word vectors.

bert document-embedding pre-trained-language-models semantic-search sentence-encoder sentence-transformers text-search text-semantic-similarity top2vec topic-modeling topic-modelling topic-search topic-vector word-embeddings

Last synced: 30 Mar 2025

https://github.com/ddangelov/restful-top2vec

Expose a Top2Vec model with a REST API.

document-embedding fastapi rest-api restful-api semantic-search semantic-search-engine text-search text-similarity top2vec topic-model topic-modeling word-embedding

Last synced: 01 May 2025

https://github.com/nazir20/twitter-topic-modeling-with-lsa-lda-bertopic-top2vec-and-nmf

Text Mining Final Project about Twitter Topic Modeling with different models

bertopic lda-model lsa-model nmf python top2vec topic-modeling

Last synced: 09 Oct 2025

https://github.com/gulabpatel/topic_modeling

bert berttopic lda nmf sklearn top2vec

Last synced: 16 May 2026

https://github.com/suryavamsi-p/conflict-nlp-topic-modeling-sentiment-analysis-using-llms

Extracts insights from 26K+ protest events using BERTopic, Top2Vec, and LLMs for real-world applications like crisis monitoring, policy research, and social unrest analysis.

all-mpnet-base-v2 bertopic conflict-data data data-science lda llama2 llms machine-learning mistral-7b nlp nltk protest-analysis pyldavis python3 top2vec topic-modeling transformers visualization

Last synced: 11 May 2026

https://github.com/klaragtknst/text_topic

This repository implements a pipeline to store various data of files from a large unstructured dataset. These fields are used for topic modeling (wordclouds, based on low-dimensional versions of embedding vectors, Named Entity Clustering and document-topic incidences). The information is aggregated and visualised using FCA.

documents elasticsearch embeddings fca ner ner-clustering sentence-transformers text-data top2vec topic-aggregation topics-modeling visualisation