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https://github.com/nyu-mll/jiant
jiant is an nlp toolkit
https://github.com/nyu-mll/jiant
bert multitask-learning nlp sentence-representation transfer-learning transformers
Last synced: 25 days ago
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jiant is an nlp toolkit
- Host: GitHub
- URL: https://github.com/nyu-mll/jiant
- Owner: nyu-mll
- License: mit
- Created: 2018-06-18T18:12:47.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-07-06T22:00:38.000Z (over 1 year ago)
- Last Synced: 2024-05-23T06:46:32.846Z (6 months ago)
- Topics: bert, multitask-learning, nlp, sentence-representation, transfer-learning, transformers
- Language: Python
- Homepage: https://jiant.info
- Size: 4.32 MB
- Stars: 1,612
- Watchers: 44
- Forks: 292
- Open Issues: 77
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: CODEOWNERS
Awesome Lists containing this project
- awesome-list - jiant - The multitask and transfer learning toolkit for natural language processing research. (Natural Language Processing / General Purpose NLP)
README
🚨**Update**🚨: As of 2021/10/17, the `jiant` project is no longer being actively maintained. This means there will be no plans to add new models, tasks, or features, or update support to new libraries.
# `jiant` is an NLP toolkit
**The multitask and transfer learning toolkit for natural language processing research**
[](https://shields.io/)
[](https://codecov.io/gh/nyu-mll/jiant)
[](https://circleci.com/gh/nyu-mll/jiant/tree/master)
[](https://github.com/psf/black)
[](https://opensource.org/licenses/MIT)
**Why should I use `jiant`?**
- `jiant` supports [multitask learning](https://colab.research.google.com/github/nyu-mll/jiant/blob/master/examples/notebooks/jiant_Multi_Task_Example.ipynb)
- `jiant` supports [transfer learning](https://colab.research.google.com/github/nyu-mll/jiant/blob/master/examples/notebooks/jiant_STILTs_Example.ipynb)
- `jiant` supports [50+ natural language understanding tasks](./guides/tasks/supported_tasks.md)
- `jiant` supports the following benchmarks:
- [GLUE](./guides/benchmarks/glue.md)
- [SuperGLUE](./guides/benchmarks/superglue.md)
- [XTREME](./guides/benchmarks/xtreme.md)
- `jiant` is a research library and users are encouraged to extend, change, and contribute to match their needs!
**A few additional things you might want to know about `jiant`:**
- `jiant` is configuration file driven
- `jiant` is built with [PyTorch](https://pytorch.org)
- `jiant` integrates with [`datasets`](https://github.com/huggingface/datasets) to manage task data
- `jiant` integrates with [`transformers`](https://github.com/huggingface/transformers) to manage models and tokenizers.
## Getting Started
* Get started with some simple [Examples](./examples)
* Learn more about `jiant` by reading our [Guides](./guides)
* See our [list of supported tasks](./guides/tasks/supported_tasks.md)
## Installation
To import `jiant` from source (recommended for researchers):
```bash
git clone https://github.com/nyu-mll/jiant.git
cd jiant
pip install -r requirements.txt
# Add the following to your .bash_rc or .bash_profile
export PYTHONPATH=/path/to/jiant:$PYTHONPATH
```
If you plan to contribute to jiant, install additional dependencies with `pip install -r requirements-dev.txt`.
To install `jiant` from source (alternative for researchers):
```
git clone https://github.com/nyu-mll/jiant.git
cd jiant
pip install . -e
```
To install `jiant` from pip (recommended if you just want to train/use a model):
```
pip install jiant
```
We recommended that you install `jiant` in a virtual environment or a conda environment.
To check `jiant` was correctly installed, run a [simple example](./examples/notebooks/simple_api_fine_tuning.ipynb).
## Quick Introduction
The following example fine-tunes a RoBERTa model on the MRPC dataset.
Python version:
```python
from jiant.proj.simple import runscript as run
import jiant.scripts.download_data.runscript as downloader
EXP_DIR = "/path/to/exp"
# Download the Data
downloader.download_data(["mrpc"], f"{EXP_DIR}/tasks")
# Set up the arguments for the Simple API
args = run.RunConfiguration(
run_name="simple",
exp_dir=EXP_DIR,
data_dir=f"{EXP_DIR}/tasks",
hf_pretrained_model_name_or_path="roberta-base",
tasks="mrpc",
train_batch_size=16,
num_train_epochs=3
)
# Run!
run.run_simple(args)
```
Bash version:
```bash
EXP_DIR=/path/to/exp
python jiant/scripts/download_data/runscript.py \
download \
--tasks mrpc \
--output_path ${EXP_DIR}/tasks
python jiant/proj/simple/runscript.py \
run \
--run_name simple \
--exp_dir ${EXP_DIR}/ \
--data_dir ${EXP_DIR}/tasks \
--hf_pretrained_model_name_or_path roberta-base \
--tasks mrpc \
--train_batch_size 16 \
--num_train_epochs 3
```
Examples of more complex training workflows are found [here](./examples/).
## Contributing
The `jiant` project's contributing guidelines can be found [here](CONTRIBUTING.md).
## Looking for `jiant v1.3.2`?
`jiant v1.3.2` has been moved to [jiant-v1-legacy](https://github.com/nyu-mll/jiant-v1-legacy) to support ongoing research with the library. `jiant v2.x.x` is more modular and scalable than `jiant v1.3.2` and has been designed to reflect the needs of the current NLP research community. We strongly recommended any new projects use `jiant v2.x.x`.
`jiant 1.x` has been used in in several papers. For instructions on how to reproduce papers by `jiant` authors that refer readers to this site for documentation (including Tenney et al., Wang et al., Bowman et al., Kim et al., Warstadt et al.), refer to the [jiant-v1-legacy](https://github.com/nyu-mll/jiant-v1-legacy) README.
## Citation
If you use `jiant ≥ v2.0.0` in academic work, please cite it directly:
```
@misc{phang2020jiant,
author = {Jason Phang and Phil Yeres and Jesse Swanson and Haokun Liu and Ian F. Tenney and Phu Mon Htut and Clara Vania and Alex Wang and Samuel R. Bowman},
title = {\texttt{jiant} 2.0: A software toolkit for research on general-purpose text understanding models},
howpublished = {\url{http://jiant.info/}},
year = {2020}
}
```
If you use `jiant ≤ v1.3.2` in academic work, please use the citation found [here](https://github.com/nyu-mll/jiant-v1-legacy).
## Acknowledgments
- This work was made possible in part by a donation to NYU from Eric and Wendy Schmidt made
by recommendation of the Schmidt Futures program, and by support from Intuit Inc.
- We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan V GPU used at NYU in this work.
- Developer Jesse Swanson is supported by the Moore-Sloan Data Science Environment as part of the NYU Data Science Services initiative.
## License
`jiant` is released under the [MIT License](https://github.com/nyu-mll/jiant/blob/master/LICENSE).