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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Topic Modelling for Humans
| Date | Stars |
|---|---|
| 2026-07-24 | 16470 |
| 2026-07-25 | 16470 |
| 2026-07-28 | 16470 |
| 2026-07-30 | 16470 |
| 2026-08-06 | 16470 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
gensim – Topic Modelling in Python
==================================
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[](https://github.com/RaRe-Technologies/gensim/actions)
[](https://github.com/RaRe-Technologies/gensim/releases)
[](https://pepy.tech/project/gensim/)
[](https://doi.org/10.13140/2.1.2393.1847)
[](https://groups.google.com/g/gensim)
[](https://twitter.com/gensim_py)
Gensim is a Python library for *topic modelling*, *document indexing*
and *similarity retrieval* with large corpora. Target audience is the
*natural language processing* (NLP) and *information retrieval* (IR)
community.
## ⚠️ Want to help out? [Sponsor Gensim](https://github.com/sponsors/piskvorky) ❤️
## ⚠️ Gensim is in stable maintenance mode: we are not accepting new features, but bug and documentation fixes are still welcome! ⚠️
Features
--------
- All algorithms are **memory-independent** w.r.t. the corpus size
(can process input larger than RAM, streamed, out-of-core),
- **Intuitive interfaces**
- easy to plug in your own input corpus/datastream (trivial
streaming API)
- easy to extend with other Vector Space algorithms (trivial
transformation API)
- Efficient multicore implementations of popular algorithms, such as
online **Latent Semantic Analysis (LSA/LSI/SVD)**, **Latent
Dirichlet Allocation (LDA)**, **Random Projections (RP)**,
**Hierarchical Dirichlet Process (HDP)** or **word2vec deep
learning**.
- **Distributed computing**: can run *Latent Semantic Analysis* and
*Latent Dirichlet Allocation* on a cluster of computers.
- Extensive [documentation and Jupyter Notebook tutorials].
If this feature list left you scratching your head, you can first read
more about the [Vector Space Model] and [unsupervised document analysis]
on Wikipedia.
Installation
------------
This software depends on [NumPy], a Python package for
scientific computing. Please bear in mind that building NumPy from source
(e.g. by installing gensim on a platform which lacks NumPy .whl distribution)
is a non-trivial task involving [linking NumPy to a BLAS library].
It is recommended to provide a fast one (such as MKL, [ATLAS] or
[OpenBLAS]) which can improve performance by as much as an order of
magnitude. On OSX, NumPy picks up its vecLib BLAS automatically,
so you don’t need to do anything special.
Install the latest version of gensim:
```bash
pip install --upgrade gensim
```
Or, if you have instead downloaded and unzipped the [source tar.gz]
package:
```bash
tar -xvzf gensim-X.X.X.tar.gz
cd gensim-X.X.X/
pip install .
```
For alternative modes of installation, see the [documentation].
Gensim is being [continuously tested](https://radimrehurek.com/gensim/#testing) under all
[supported Python versions](https://github.com/RaRe-Technologies/gensim/wiki/Gensim-And-Compatibility).
Support for Python 2.7 was dropped in gensim 4.0.0 – install gensim 3.8.3 if you must use Python 2.7.
How come gensim is so fast and memory efficient? Isn’t it pure Python, and isn’t Python slow and greedy?
--------------------------------------------------------------------------------------------------------
Many scientific algorithms can be expressed in terms of large matrix
operations (see the BLAS note above). Gensim taps into these low-level
BLAS libExcerpt of 10,007 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3d66a68cc5ecaac6, topic:nlp, topic:natural-language-processing, readme:natural language processing
matched fp:3d66a68cc5ecaac6, topic:neural-network