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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.
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
| Date | Stars |
|---|---|
| 2026-07-24 | 1266 |
| 2026-07-25 | 1266 |
| 2026-07-28 | 1266 |
| 2026-07-30 | 1266 |
| 2026-07-31 | 1266 |
| 2026-08-06 | 1267 |
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| 2026-08-18 | 1267 |
| 2026-08-20 | 1268 |
| 2026-09-09 | 1267 |
| 2026-09-12 | 1267 |
| 2026-09-20 | 1267 |
Today
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This month
-1 stars this month
Momentum
0.0
growth rate 0.00%/day
### [Overview](#overview) | [Tutorials](#tutorials) | [Examples](#examples) | [Installation](#installation) | [FAQ](https://github.com/amaiya/ktrain/blob/master/FAQ.md) | [API Docs](https://amaiya.github.io/ktrain/index.html) | [How to Cite](#how-to-cite)
[](https://badge.fury.io/py/ktrain) [](https://pypi.python.org/pypi/ktrain) [](https://github.com/amaiya/ktrain/blob/master/LICENSE) [](https://pepy.tech/project/ktrain)
<!--[](https://twitter.com/ktrain_ai)-->
<p align="center">
<img src="https://github.com/amaiya/ktrain/raw/master/ktrain_logo_200x100.png" width="200"/>
</p>
# Welcome to ktrain
> a "Swiss Army knife" for machine learning
### News and Announcements
- **2024-02-20**
- **ktrain 0.41.x** is released and removes the `ktrain.text.qa.generative_qa` module. Our [OnPrem.LLM](https://github.com/amaiya/onprem) package should be used for Generative Question-Answering tasks. See [example notebook](https://amaiya.github.io/onprem/examples_rag.html).
----
### Overview
**ktrain** is a lightweight wrapper for the deep learning library [TensorFlow Keras](https://www.tensorflow.org/guide/keras/overview) (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like *fastai* and *ludwig*, **ktrain** is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines of code, **ktrain** allows you to easily and quickly:
- employ fast, accurate, and easy-to-use pre-canned models for `text`, `vision`, `graph`, and `tabular` data:
- `text` data:
- **Text Classification**: [BERT](https://arxiv.org/abs/1810.04805), [DistilBERT](https://arxiv.org/abs/1910.01108), [NBSVM](https://www.aclweb.org/anthology/P12-2018), [fastText](https://arxiv.org/abs/1607.01759), and other models <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/IMDb-BERT.ipynb)]</sup></sub>
- **Text Regression**: [BERT](https://arxiv.org/abs/1810.04805), [DistilBERT](https://arxiv.org/abs/1910.01108), Embedding-based linear text regression, [fastText](https://arxiv.org/abs/1607.01759), and other models <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/text_regression_example.ipynb)]</sup></sub>
- **Sequence Labeling (NER)**: Bidirectional LSTM with optional [CRF layer](https://arxiv.org/abs/1603.01360) and various embedding schemes such as pretrained [BERT](https://huggingface.co/transformers/pretrained_models.html) and [fasttext](https://fasttext.cc/docs/en/crawl-vectors.html) word embeddings and character embeddings <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/CoNLL2002_Dutch-BiLSTM.ipynb)]</sup></sub>
- **Ready-to-Use NER models for English, Chinese, and Russian** with no training required <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/shallownlp-examples.ipynb)]</sup></sub>
- **Sentence Pair Classification** for tasks like paraphrase detection <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/MRPC-BERT.ipynb)]</sup></sub>
- **Unsupervised Topic Modeling** with [LDA](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf) <sub><sup>[[example notebook](https://nbviewer.jupyter.org/github/amaiya/ktrain/blob/master/examples/text/20newsgroups-topic_modeling.ipynb)]</sup></sub>
- **Document Similarity withExcerpt of 31,871 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e2344cb9323af92b, topic:deep-learning, topic:tensorflow
matched fp:e2344cb9323af92b, topic:computer-vision
matched fp:e2344cb9323af92b, topic:nlp, readme:text classification