The multitask and transfer learning toolkit for natural language processing research

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.

GitHub

https://github.com/nyu-mll/jiant