| Name | lit-nlp JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | 🔥LIT: The Learning Interpretability Tool |
| upload_time | 2024-04-09 23:12:49 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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| keywords |
interpretability
interpretable artifical intelligence
explainable
explainable artifical intelligence
xai
computer vision
natural language processing
artifical intelligence
machine learning
deep learning
visualization
visual analytics
tensorflow
tf
torch
|
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| requirements |
No requirements were recorded.
|
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No Travis.
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| coveralls test coverage |
No coveralls.
|
# 🔥 Learning Interpretability Tool (LIT)
<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-22' } *-->
The Learning Interpretability Tool (🔥LIT, formerly known as the Language
Interpretability Tool) is a visual, interactive ML model-understanding tool that
supports text, image, and tabular data. It can be run as a standalone server, or
inside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex
AI notebooks.
LIT is built to answer questions such as:
* **What kind of examples** does my model perform poorly on?
* **Why did my model make this prediction?** Can this prediction be attributed
to adversarial behavior, or to undesirable priors in the training set?
* **Does my model behave consistently** if I change things like textual style,
verb tense, or pronoun gender?
LIT supports a variety of debugging workflows through a browser-based UI.
Features include:
* **Local explanations** via salience maps, attention, and rich visualization
of model predictions.
* **Aggregate analysis** including custom metrics, slicing and binning, and
visualization of embedding spaces.
* **Counterfactual generation** via manual edits or generator plug-ins to
dynamically create and evaluate new examples.
* **Side-by-side mode** to compare two or more models, or one model on a pair
of examples.
* **Highly extensible** to new model types, including classification,
regression, span labeling, seq2seq, and language modeling. Supports
multi-head models and multiple input features out of the box.
* **Framework-agnostic** and compatible with TensorFlow, PyTorch, and more.
LIT has a [website](https://pair-code.github.io/lit) with live demos, tutorials,
a setup guide and more.
Stay up to date on LIT by joining the
[lit-announcements mailing list](https://groups.google.com/g/lit-annoucements).
For a broader overview, check out [our paper](https://arxiv.org/abs/2008.05122) and the
[user guide](https://pair-code.github.io/lit/documentation/ui_guide).
## Documentation
* [Documentation index](https://pair-code.github.io/lit/documentation/)
* [FAQ](https://pair-code.github.io/lit/documentation/faq)
* [Release notes](./RELEASE.md)
## Download and Installation
LIT can be run via container image, installed via `pip` or built from source.
Building from source is necessary if you update any of the front-end or core
back-end code.
### Build container image
Build the image using `docker` or `podman`:
```sh
git clone https://github.com/PAIR-code/lit.git && cd lit
docker build --file Dockerfile --tag lit-nlp .
```
See the [advanced guide](https://pair-code.github.io/lit/documentation/docker) for detailed instructions on using the
default LIT Docker image, running LIT as a containerized web app in different
scenarios, and how to creating your own LIT images.
### pip installation
```sh
pip install lit-nlp
```
The `pip` installation will install all necessary prerequisite packages for use
of the core LIT package.
It **does not** install the prerequisites for the provided demos, so you need to
install those yourself. See
[requirements_examples.txt](./requirements_examples.txt) for the list of
packages required to run the demos.
### Install from source
Clone the repo:
```sh
git clone https://github.com/PAIR-code/lit.git && cd lit
```
Note: be sure you are running Python 3.10. If you have a different version on
your system, use the `conda` instructions below to set up a Python 3.10
environment.
Set up a Python environment with `venv`:
```sh
python -m venv .venv
source .venv/bin/activate
```
Or set up a Python environment using `conda`:
```sh
conda create --name lit-nlp
conda activate lit-nlp
conda install python=3.10
conda install pip
```
Once you have the environment, install LIT's dependencies:
```sh
python -m pip install -r requirements.txt
python -m pip install cudnn cupti # optional, for GPU support
python -m pip install torch # optional, for PyTorch
# Build the frontend
(cd lit_nlp; yarn && yarn build)
```
Note: Use the `-r requirements.txt` option to install every dependency required
for the LIT library, its test suite, and the built-in examples. You can also
install subsets of these using the `-r requirements_core.txt` (core library),
`-r requirements_test.txt` (test suite), `-r requirements_examples.txt`
(examples), and/or any combination thereof.
Note: if you see [an error](https://github.com/yarnpkg/yarn/issues/2821)
running `yarn` on Ubuntu/Debian, be sure you have the
[correct version installed](https://yarnpkg.com/en/docs/install#linux-tab).
## Running LIT
Explore a collection of hosted demos on the
[demos page](https://pair-code.github.io/lit/demos).
### Quick-start: classification and regression
To explore classification and regression models tasks from the popular
[GLUE benchmark](https://gluebenchmark.com/):
```sh
python -m lit_nlp.examples.glue_demo --port=5432 --quickstart
```
Or, using `docker`:
```sh
docker run --rm -e DEMO_NAME=glue_demo -p 5432:5432 -t lit-nlp --quickstart
```
Navigate to http://localhost:5432 to access the LIT UI.
Your default view will be a
[small BERT-based model](https://arxiv.org/abs/1908.08962) fine-tuned on the
[Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/treebank.html),
but you can switch to
[STS-B](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) or
[MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) using the toolbar or the
gear icon in the upper right.
### Quick-start: language modeling
To explore predictions from a pre-trained language model (BERT or GPT-2), run:
```sh
python -m lit_nlp.examples.lm_demo --models=bert-base-uncased --port=5432
```
Or, using `docker`:
```sh
docker run --rm -e DEMO_NAME=lm_demo -p 5432:5432 -t lit-nlp --models=bert-base-uncased
```
And navigate to http://localhost:5432 for the UI.
### Notebook usage
Colab notebooks showing the use of LIT inside of notebooks can be found at
[lit_nlp/examples/notebooks](./lit_nlp/examples/notebooks).
We provide a simple
[Colab demo](https://colab.research.google.com/github/PAIR-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb).
Run all the cells to see LIT on an example classification model in the notebook.
### More Examples
See [lit_nlp/examples](./lit_nlp/examples). Most are run similarly to the
quickstart example above:
```sh
python -m lit_nlp.examples.<example_name> --port=5432 [optional --args]
```
## User Guide
To learn about LIT's features, check out the [user guide](https://pair-code.github.io/lit/documentation/ui_guide), or
watch this [video](https://www.youtube.com/watch?v=CuRI_VK83dU).
## Adding your own models or data
You can easily run LIT with your own model by creating a custom `demo.py`
launcher, similar to those in [lit_nlp/examples](./lit_nlp/examples). The
basic steps are:
* Write a data loader which follows the [`Dataset` API](https://pair-code.github.io/lit/documentation/api#datasets)
* Write a model wrapper which follows the [`Model` API](https://pair-code.github.io/lit/documentation/api#models)
* Pass models, datasets, and any additional
[components](https://pair-code.github.io/lit/documentation/api#interpretation-components) to the LIT server class
For a full walkthrough, see
[adding models and data](https://pair-code.github.io/lit/documentation/api#adding-models-and-data).
## Extending LIT with new components
LIT is easy to extend with new interpretability components, generators, and
more, both on the frontend or the backend. See our [documentation](https://pair-code.github.io/lit/documentation/) to get
started.
## Pull Request Process
To make code changes to LIT, please work off of the `dev` branch and
[create pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request)
(PRs) against that branch. The `main` branch is for stable releases, and it is
expected that the `dev` branch will always be ahead of `main`.
[Draft PRs](https://github.blog/2019-02-14-introducing-draft-pull-requests/) are
encouraged, especially for first-time contributors or contributors working on
complex tasks (e.g., Google Summer of Code contributors). Please use these to
communicate ideas and implementations with the LIT team, in addition to issues.
Prior to sending your PR or marking a Draft PR as "Ready for Review", please run
the Python and TypeScript linters on your code to ensure compliance with
Google's [Python](https://google.github.io/styleguide/pyguide.html) and
[TypeScript](https://google.github.io/styleguide/tsguide.html) Style Guides.
```sh
# Run Pylint on your code using the following command from the root of this repo
pushd lit_nlp & pylint & popd
# Run ESLint on your code using the following command from the root of this repo
pushd lit_nlp & yarn lint & popd
```
## Citing LIT
If you use LIT as part of your work, please cite
[our EMNLP paper](https://arxiv.org/abs/2008.05122):
```
@misc{tenney2020language,
title={The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for {NLP} Models},
author={Ian Tenney and James Wexler and Jasmijn Bastings and Tolga Bolukbasi and Andy Coenen and Sebastian Gehrmann and Ellen Jiang and Mahima Pushkarna and Carey Radebaugh and Emily Reif and Ann Yuan},
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
year = "2020",
publisher = "Association for Computational Linguistics",
pages = "107--118",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.15",
}
```
## Disclaimer
This is not an official Google product.
LIT is a research project and under active development by a small team. There
will be some bugs and rough edges, but we're releasing at an early stage because
we think it's pretty useful already. We want LIT to be an open platform, not a
walled garden, and we would love your suggestions and feedback - drop us a line
in the [issues](https://github.com/pair-code/lit/issues).
Raw data
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"description": "# \ud83d\udd25 Learning Interpretability Tool (LIT)\n\n<!--* freshness: { owner: 'lit-dev' reviewed: '2023-09-22' } *-->\n\nThe Learning Interpretability Tool (\ud83d\udd25LIT, formerly known as the Language\nInterpretability Tool) is a visual, interactive ML model-understanding tool that\nsupports text, image, and tabular data. It can be run as a standalone server, or\ninside of notebook environments such as Colab, Jupyter, and Google Cloud Vertex\nAI notebooks.\n\nLIT is built to answer questions such as:\n\n* **What kind of examples** does my model perform poorly on?\n* **Why did my model make this prediction?** Can this prediction be attributed\n to adversarial behavior, or to undesirable priors in the training set?\n* **Does my model behave consistently** if I change things like textual style,\n verb tense, or pronoun gender?\n\n\n\nLIT supports a variety of debugging workflows through a browser-based UI.\nFeatures include:\n\n* **Local explanations** via salience maps, attention, and rich visualization\n of model predictions.\n* **Aggregate analysis** including custom metrics, slicing and binning, and\n visualization of embedding spaces.\n* **Counterfactual generation** via manual edits or generator plug-ins to\n dynamically create and evaluate new examples.\n* **Side-by-side mode** to compare two or more models, or one model on a pair\n of examples.\n* **Highly extensible** to new model types, including classification,\n regression, span labeling, seq2seq, and language modeling. Supports\n multi-head models and multiple input features out of the box.\n* **Framework-agnostic** and compatible with TensorFlow, PyTorch, and more.\n\nLIT has a [website](https://pair-code.github.io/lit) with live demos, tutorials,\na setup guide and more.\n\nStay up to date on LIT by joining the\n[lit-announcements mailing list](https://groups.google.com/g/lit-annoucements).\n\nFor a broader overview, check out [our paper](https://arxiv.org/abs/2008.05122) and the\n[user guide](https://pair-code.github.io/lit/documentation/ui_guide).\n\n## Documentation\n\n* [Documentation index](https://pair-code.github.io/lit/documentation/)\n* [FAQ](https://pair-code.github.io/lit/documentation/faq)\n* [Release notes](./RELEASE.md)\n\n## Download and Installation\n\nLIT can be run via container image, installed via `pip` or built from source.\nBuilding from source is necessary if you update any of the front-end or core\nback-end code.\n\n### Build container image\n\nBuild the image using `docker` or `podman`:\n```sh\ngit clone https://github.com/PAIR-code/lit.git && cd lit\ndocker build --file Dockerfile --tag lit-nlp .\n```\n\nSee the [advanced guide](https://pair-code.github.io/lit/documentation/docker) for detailed instructions on using the\ndefault LIT Docker image, running LIT as a containerized web app in different\nscenarios, and how to creating your own LIT images.\n\n### pip installation\n\n```sh\npip install lit-nlp\n```\n\nThe `pip` installation will install all necessary prerequisite packages for use\nof the core LIT package.\n\nIt **does not** install the prerequisites for the provided demos, so you need to\ninstall those yourself. See\n[requirements_examples.txt](./requirements_examples.txt) for the list of\npackages required to run the demos.\n\n### Install from source\n\nClone the repo:\n\n```sh\ngit clone https://github.com/PAIR-code/lit.git && cd lit\n```\n\n\nNote: be sure you are running Python 3.10. If you have a different version on \nyour system, use the `conda` instructions below to set up a Python 3.10 \nenvironment.\n\nSet up a Python environment with `venv`:\n\n```sh\npython -m venv .venv\nsource .venv/bin/activate\n```\n\nOr set up a Python environment using `conda`:\n\n```sh\nconda create --name lit-nlp\nconda activate lit-nlp\nconda install python=3.10\nconda install pip\n```\n\nOnce you have the environment, install LIT's dependencies:\n```sh\npython -m pip install -r requirements.txt\npython -m pip install cudnn cupti # optional, for GPU support\npython -m pip install torch # optional, for PyTorch\n\n# Build the frontend\n(cd lit_nlp; yarn && yarn build)\n```\n\nNote: Use the `-r requirements.txt` option to install every dependency required\nfor the LIT library, its test suite, and the built-in examples. You can also\ninstall subsets of these using the `-r requirements_core.txt` (core library),\n`-r requirements_test.txt` (test suite), `-r requirements_examples.txt`\n(examples), and/or any combination thereof.\n\nNote: if you see [an error](https://github.com/yarnpkg/yarn/issues/2821)\nrunning `yarn` on Ubuntu/Debian, be sure you have the\n[correct version installed](https://yarnpkg.com/en/docs/install#linux-tab).\n\n\n## Running LIT\n\nExplore a collection of hosted demos on the\n[demos page](https://pair-code.github.io/lit/demos).\n\n### Quick-start: classification and regression\n\nTo explore classification and regression models tasks from the popular\n[GLUE benchmark](https://gluebenchmark.com/):\n\n```sh\npython -m lit_nlp.examples.glue_demo --port=5432 --quickstart\n```\n\nOr, using `docker`:\n\n```sh\ndocker run --rm -e DEMO_NAME=glue_demo -p 5432:5432 -t lit-nlp --quickstart\n```\n\nNavigate to http://localhost:5432 to access the LIT UI.\n\nYour default view will be a\n[small BERT-based model](https://arxiv.org/abs/1908.08962) fine-tuned on the\n[Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/treebank.html),\nbut you can switch to\n[STS-B](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark) or\n[MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) using the toolbar or the\ngear icon in the upper right.\n\n### Quick-start: language modeling\n\nTo explore predictions from a pre-trained language model (BERT or GPT-2), run:\n\n```sh\npython -m lit_nlp.examples.lm_demo --models=bert-base-uncased --port=5432\n```\n\nOr, using `docker`:\n\n```sh\ndocker run --rm -e DEMO_NAME=lm_demo -p 5432:5432 -t lit-nlp --models=bert-base-uncased\n```\n\nAnd navigate to http://localhost:5432 for the UI.\n\n### Notebook usage\n\nColab notebooks showing the use of LIT inside of notebooks can be found at\n[lit_nlp/examples/notebooks](./lit_nlp/examples/notebooks).\n\nWe provide a simple\n[Colab demo](https://colab.research.google.com/github/PAIR-code/lit/blob/main/lit_nlp/examples/notebooks/LIT_sentiment_classifier.ipynb).\nRun all the cells to see LIT on an example classification model in the notebook.\n\n### More Examples\n\nSee [lit_nlp/examples](./lit_nlp/examples). Most are run similarly to the\nquickstart example above:\n\n```sh\npython -m lit_nlp.examples.<example_name> --port=5432 [optional --args]\n```\n\n## User Guide\n\nTo learn about LIT's features, check out the [user guide](https://pair-code.github.io/lit/documentation/ui_guide), or\nwatch this [video](https://www.youtube.com/watch?v=CuRI_VK83dU).\n\n## Adding your own models or data\n\nYou can easily run LIT with your own model by creating a custom `demo.py`\nlauncher, similar to those in [lit_nlp/examples](./lit_nlp/examples). The\nbasic steps are:\n\n* Write a data loader which follows the [`Dataset` API](https://pair-code.github.io/lit/documentation/api#datasets)\n* Write a model wrapper which follows the [`Model` API](https://pair-code.github.io/lit/documentation/api#models)\n* Pass models, datasets, and any additional\n [components](https://pair-code.github.io/lit/documentation/api#interpretation-components) to the LIT server class\n\nFor a full walkthrough, see\n[adding models and data](https://pair-code.github.io/lit/documentation/api#adding-models-and-data).\n\n## Extending LIT with new components\n\nLIT is easy to extend with new interpretability components, generators, and\nmore, both on the frontend or the backend. See our [documentation](https://pair-code.github.io/lit/documentation/) to get\nstarted.\n\n## Pull Request Process\n\nTo make code changes to LIT, please work off of the `dev` branch and\n[create pull requests](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request)\n(PRs) against that branch. The `main` branch is for stable releases, and it is\nexpected that the `dev` branch will always be ahead of `main`.\n\n[Draft PRs](https://github.blog/2019-02-14-introducing-draft-pull-requests/) are\nencouraged, especially for first-time contributors or contributors working on\ncomplex tasks (e.g., Google Summer of Code contributors). Please use these to\ncommunicate ideas and implementations with the LIT team, in addition to issues.\n\nPrior to sending your PR or marking a Draft PR as \"Ready for Review\", please run\nthe Python and TypeScript linters on your code to ensure compliance with\nGoogle's [Python](https://google.github.io/styleguide/pyguide.html) and\n[TypeScript](https://google.github.io/styleguide/tsguide.html) Style Guides.\n\n```sh\n# Run Pylint on your code using the following command from the root of this repo\npushd lit_nlp & pylint & popd\n\n# Run ESLint on your code using the following command from the root of this repo\npushd lit_nlp & yarn lint & popd\n```\n\n## Citing LIT\n\nIf you use LIT as part of your work, please cite\n[our EMNLP paper](https://arxiv.org/abs/2008.05122):\n\n```\n@misc{tenney2020language,\n title={The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for {NLP} Models},\n author={Ian Tenney and James Wexler and Jasmijn Bastings and Tolga Bolukbasi and Andy Coenen and Sebastian Gehrmann and Ellen Jiang and Mahima Pushkarna and Carey Radebaugh and Emily Reif and Ann Yuan},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations\",\n year = \"2020\",\n publisher = \"Association for Computational Linguistics\",\n pages = \"107--118\",\n url = \"https://www.aclweb.org/anthology/2020.emnlp-demos.15\",\n}\n```\n\n## Disclaimer\n\nThis is not an official Google product.\n\nLIT is a research project and under active development by a small team. There\nwill be some bugs and rough edges, but we're releasing at an early stage because\nwe think it's pretty useful already. We want LIT to be an open platform, not a\nwalled garden, and we would love your suggestions and feedback - drop us a line\nin the [issues](https://github.com/pair-code/lit/issues).\n",
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