🚨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

Why should I use jiant?

• jiant supports multitask learning
• jiant supports transfer learning
• jiant supports 50+ natural language understanding tasks
• jiant supports the following benchmarks:
  • GLUE
  • SuperGLUE
  • XTREME
• 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
• jiant integrates with datasets to manage task data
• jiant integrates with transformers to manage models and tokenizers.

Getting Started

• Get started with some simple Examples
• Learn more about jiant by reading our Guides
• See our list of supported tasks

Installation

To import jiant from source (recommended for researchers):

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.

Quick Introduction

The following example fine-tunes a RoBERTa model on the MRPC dataset.

Python version:

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:

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.

Contributing

The jiant project's contributing guidelines can be found here.

Looking for jiant v1.3.2?

jiant v1.3.2 has been moved to 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 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.

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.