{"id":13711539,"url":"https://github.com/nyu-mll/jiant","last_synced_at":"2025-05-14T23:05:11.566Z","repository":{"id":37445233,"uuid":"137789266","full_name":"nyu-mll/jiant","owner":"nyu-mll","description":"jiant is an nlp toolkit","archived":false,"fork":false,"pushed_at":"2023-07-06T22:00:38.000Z","size":4532,"stargazers_count":1666,"open_issues_count":78,"forks_count":299,"subscribers_count":42,"default_branch":"master","last_synced_at":"2025-05-14T23:05:02.592Z","etag":null,"topics":["bert","multitask-learning","nlp","sentence-representation","transfer-learning","transformers"],"latest_commit_sha":null,"homepage":"https://jiant.info","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nyu-mll.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":"CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2018-06-18T18:12:47.000Z","updated_at":"2025-05-01T14:45:54.000Z","dependencies_parsed_at":"2024-01-25T17:13:04.598Z","dependency_job_id":null,"html_url":"https://github.com/nyu-mll/jiant","commit_stats":{"total_commits":1633,"total_committers":69,"mean_commits":"23.666666666666668","dds":0.8475199020208206,"last_synced_commit":"daa5a258e3af5e7503288de8401429eaf3f58e13"},"previous_names":["jsalt18-sentence-repl/jiant"],"tags_count":22,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nyu-mll%2Fjiant","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nyu-mll%2Fjiant/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nyu-mll%2Fjiant/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nyu-mll%2Fjiant/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nyu-mll","download_url":"https://codeload.github.com/nyu-mll/jiant/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254243358,"owners_count":22038046,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bert","multitask-learning","nlp","sentence-representation","transfer-learning","transformers"],"created_at":"2024-08-02T23:01:09.198Z","updated_at":"2025-05-14T23:05:06.484Z","avatar_url":"https://github.com/nyu-mll.png","language":"Python","funding_links":[],"categories":["📂 Benchmarks \u0026 Datasets","Natural Language Processing","Anthropomorphic-Taxonomy","Python"],"sub_categories":["General Purpose NLP","Typical Intelligence Quotient (IQ)-General Intelligence evaluation benchmarks"],"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.\n\n\u003cdiv align=\"center\"\u003e\n\n# `jiant` is an NLP toolkit\n**The multitask and transfer learning toolkit for natural language processing research**\n\n[![Generic badge](https://img.shields.io/github/v/release/nyu-mll/jiant)](https://shields.io/)\n[![codecov](https://codecov.io/gh/nyu-mll/jiant/branch/master/graph/badge.svg)](https://codecov.io/gh/nyu-mll/jiant)\n[![CircleCI](https://circleci.com/gh/nyu-mll/jiant/tree/master.svg?style=shield)](https://circleci.com/gh/nyu-mll/jiant/tree/master)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n\n\u003c/div\u003e\n\n**Why should I use `jiant`?**\n- `jiant` supports [multitask learning](https://colab.research.google.com/github/nyu-mll/jiant/blob/master/examples/notebooks/jiant_Multi_Task_Example.ipynb)\n- `jiant` supports [transfer learning](https://colab.research.google.com/github/nyu-mll/jiant/blob/master/examples/notebooks/jiant_STILTs_Example.ipynb)\n- `jiant` supports [50+ natural language understanding tasks](./guides/tasks/supported_tasks.md)\n- `jiant` supports the following benchmarks:\n    - [GLUE](./guides/benchmarks/glue.md)\n    - [SuperGLUE](./guides/benchmarks/superglue.md)\n    - [XTREME](./guides/benchmarks/xtreme.md)\n- `jiant` is a research library and users are encouraged to extend, change, and contribute to match their needs!\n\n**A few additional things you might want to know about `jiant`:**\n- `jiant` is configuration file driven\n- `jiant` is built with [PyTorch](https://pytorch.org)\n- `jiant` integrates with [`datasets`](https://github.com/huggingface/datasets) to manage task data\n- `jiant` integrates with [`transformers`](https://github.com/huggingface/transformers) to manage models and tokenizers.\n\n## Getting Started\n\n* Get started with some simple [Examples](./examples)\n* Learn more about `jiant` by reading our [Guides](./guides)\n* See our [list of supported tasks](./guides/tasks/supported_tasks.md)\n\n## Installation\n\nTo import `jiant` from source (recommended for researchers):\n```bash\ngit clone https://github.com/nyu-mll/jiant.git\ncd jiant\npip install -r requirements.txt\n\n# Add the following to your .bash_rc or .bash_profile \nexport PYTHONPATH=/path/to/jiant:$PYTHONPATH\n```\nIf you plan to contribute to jiant, install additional dependencies with `pip install -r requirements-dev.txt`.\n\nTo install `jiant` from source (alternative for researchers):\n```\ngit clone https://github.com/nyu-mll/jiant.git\ncd jiant\npip install . -e\n```\n\nTo install `jiant` from pip (recommended if you just want to train/use a model):\n```\npip install jiant\n```\n\nWe recommended that you install `jiant` in a virtual environment or a conda environment.\n\nTo check `jiant` was correctly installed, run a [simple example](./examples/notebooks/simple_api_fine_tuning.ipynb).\n\n\n## Quick Introduction\nThe following example fine-tunes a RoBERTa model on the MRPC dataset.\n\nPython version:\n```python\nfrom jiant.proj.simple import runscript as run\nimport jiant.scripts.download_data.runscript as downloader\n\nEXP_DIR = \"/path/to/exp\"\n\n# Download the Data\ndownloader.download_data([\"mrpc\"], f\"{EXP_DIR}/tasks\")\n\n# Set up the arguments for the Simple API\nargs = run.RunConfiguration(\n   run_name=\"simple\",\n   exp_dir=EXP_DIR,\n   data_dir=f\"{EXP_DIR}/tasks\",\n   hf_pretrained_model_name_or_path=\"roberta-base\",\n   tasks=\"mrpc\",\n   train_batch_size=16,\n   num_train_epochs=3\n)\n\n# Run!\nrun.run_simple(args)\n```\n\nBash version:\n```bash\nEXP_DIR=/path/to/exp\n\npython jiant/scripts/download_data/runscript.py \\\n    download \\\n    --tasks mrpc \\\n    --output_path ${EXP_DIR}/tasks\npython jiant/proj/simple/runscript.py \\\n    run \\\n    --run_name simple \\\n    --exp_dir ${EXP_DIR}/ \\\n    --data_dir ${EXP_DIR}/tasks \\\n    --hf_pretrained_model_name_or_path roberta-base \\\n    --tasks mrpc \\\n    --train_batch_size 16 \\\n    --num_train_epochs 3\n```\n\nExamples of more complex training workflows are found [here](./examples/).\n\n\n## Contributing\nThe `jiant` project's contributing guidelines can be found [here](CONTRIBUTING.md).\n\n## Looking for `jiant v1.3.2`?\n`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`.\n\n`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.\n\n## Citation\n\nIf you use `jiant ≥ v2.0.0` in academic work, please cite it directly:\n\n```\n@misc{phang2020jiant,\n    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},\n    title = {\\texttt{jiant} 2.0: A software toolkit for research on general-purpose text understanding models},\n    howpublished = {\\url{http://jiant.info/}},\n    year = {2020}\n}\n```\n\nIf you use `jiant ≤ v1.3.2` in academic work, please use the citation found [here](https://github.com/nyu-mll/jiant-v1-legacy).\n\n## Acknowledgments\n\n- This work was made possible in part by a donation to NYU from Eric and Wendy Schmidt made\nby recommendation of the Schmidt Futures program, and by support from Intuit Inc.\n- We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan V GPU used at NYU in this work.\n- Developer Jesse Swanson is supported by the Moore-Sloan Data Science Environment as part of the NYU Data Science Services initiative.\n\n## License\n`jiant` is released under the [MIT License](https://github.com/nyu-mll/jiant/blob/master/LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnyu-mll%2Fjiant","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnyu-mll%2Fjiant","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnyu-mll%2Fjiant/lists"}