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Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
| Date | Stars |
|---|---|
| 2026-07-24 | 4253 |
| 2026-07-25 | 4253 |
| 2026-07-28 | 4253 |
| 2026-07-30 | 4253 |
| 2026-07-31 | 4251 |
| 2026-08-06 | 4251 |
Today
— stars today
This week
-2 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
[](https://opensource.org/licenses/Apache-2.0) [](https://pepy.tech/project/simpletransformers)
<!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section -->
[](#contributors-)
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# Simple Transformers
This library is based on the [Transformers](https://github.com/huggingface/transformers) library by HuggingFace. `Simple Transformers` lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to **initialize**, **train**, and **evaluate** a model.
**Supported Tasks:**
- Information Retrieval (Dense Retrieval)
- (Large) Language Models (Training, Fine-tuning, and Generation)
- Encoder Model Training and Fine-tuning
- Sequence Classification
- Token Classification (NER)
- Question Answering
- Language Generation
- T5 Model
- Seq2Seq Tasks
- Multi-Modal Classification
- Conversational AI
# Citation
If you use Simple Transformers in your work, please cite:
```bibtex
@inproceedings{Rajapakse2024SimpleTransformers,
author = {Rajapakse, Thilina C. and Yates, Andrew and de Rijke, Maarten},
title = {Simple Transformers: Open-source for All},
booktitle = {Proceedings of the 2024 Annual International ACM SIGIR
Conference on Research and Development in Information
Retrieval in the Asia Pacific Region},
series = {SIGIR-AP 2024},
pages = {209--215},
year = {2024},
doi = {10.1145/3673791.3698412},
url = {https://doi.org/10.1145/3673791.3698412},
location = {Tokyo, Japan}
}
```
# Table of contents
<!--ts-->
- [Simple Transformers](#simple-transformers)
- [Table of contents](#table-of-contents)
- [Setup](#setup)
- [With Conda](#with-conda)
- [Optional](#optional)
- [Usage](#usage)
- [A quick example](#a-quick-example)
- [Experiment Tracking with Weights and Biases](#experiment-tracking-with-weights-and-biases)
- [Current Pretrained Models](#current-pretrained-models)
- [Contributors ✨](#contributors-)
- [How to Contribute](#how-to-contribute)
- [How to Update Docs](#how-to-update-docs)
- [Acknowledgements](#acknowledgements)
<!--te-->
## Setup
### With Conda
1. Install `Anaconda` or `Miniconda` Package Manager from [here](https://www.anaconda.com/distribution/)
2. Create a new virtual environment and install packages.
```bash
$ conda create -n st python pandas tqdm
$ conda activate st
```
Using Cuda:
```bash
$ conda install pytorch>=1.6 cudatoolkit=11.0 -c pytorch
```
Without using Cuda
```bash
$ conda install pytorch cpuonly -c pytorch
```
3. Install `simpletransformers`.
```bash
$ pip install simpletransformers
```
#### Optional
1. Install `Weights` and `Biases` (wandb) for tracking and visualizing training in a web browser.
```bash
$ pip install wandb
```
## Usage
**All documentation is now live at [thilinarajapakse.github.io/simpletransformers](https://thilinarajapakse.github.io/simpletransformers)**
`Simple Transformer` models are built with a particular Natural Language Processing (NLP) task in mind. Each such model comes equipped with features and functionality designed to best fit the task that they are intended to perform. The high-level process of using Simple Transformers models follows the same pattern.
1. Initialize a task-specific model
2. Train the model with `train_model()`
3. Evaluate the model with `eval_model()`
4. Make predictions on (unlabelled) data with `predict()`
However, there are necessary differences between the different models to ensure that they are well suited for their intended task. The key differences will typically be the differences in input/output data formats and any task specific features/configuration options. These can all be found in the documentation section for Excerpt of 41,935 characters
Read on GitHub
Thilina Rajapakse · University of Amsterdam · Netherlands
672
236
37
Manuel · Germany
33
24
Florian Zimmermeister · Germany
23
20
17
Cahya Wirawan · Austria
16
Karthik Ganesan · United States
15
15
14
13
13
Luke Tudge · Netherlands
8
8
7
6
5
5
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3baa5f7b976ce61b, topic:named-entity-recognition, topic:text-classification, readme:natural language processing