# Demos

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The LIT team maintains a number of hosted demos, as well as pre-built launchers
for some common tasks and model types.

For publicly-visible demos hosted on Google Cloud, see
https://pair-code.github.io/lit/demos/.

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## Classification <!-- DO NOT REMOVE {#classification .demo-section-header} -->

### Sentiment and NLI <!-- DO NOT REMOVE {#glue .demo-header} -->

**Hosted instance:** https://pair-code.github.io/lit/demos/glue.html \
**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)

*   Multi-task demo:
    *   Sentiment analysis as a binary classification task
        ([SST-2](https://nlp.stanford.edu/sentiment/treebank.html)) on single
        sentences.
    *   Natural Language Inference (NLI) using
        [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/), as a three-way
        classification task with two-segment input (premise, hypothesis).
    *   STS-B textual similarity task (see
        [Regression / Scoring](#regression-scoring) below).
    *   Switch tasks using the Settings (⚙️) menu.
*   BERT models of different sizes, built on HuggingFace TF2 (Keras).
*   Supports the widest range of LIT interpretability features:
    *   Model output probabilities, custom thresholds, and multiclass metrics.
    *   Jitter plot of output scores, to find confident examples or ones near
        the margin.
    *   Embedding projector to find clusters in representation space.
    *   Integrated Gradients, LIME, and other salience methods.
    *   Counterfactual generators, including HotFlip for targeted adversarial
        perturbations.

Tip: check out a case study for this demo on the public LIT website:
https://pair-code.github.io/lit/tutorials/sentiment

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## Regression / Scoring <!-- DO NOT REMOVE {#regression-scoring .demo-section-header} -->

### Textual Similarity (STS-B) <!-- DO NOT REMOVE {#stsb .demo-header} -->

**Hosted instance:** https://pair-code.github.io/lit/demos/glue.html?models=stsb&dataset=stsb_dev \
**Code:** [examples/glue/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/glue/demo.py)

*   STS-B textual similarity task, predicting scores on a range from 0
    (unrelated) to 5 (very similar).
*   BERT models built on HuggingFace TF2 (Keras).
*   Supports a wide range of LIT interpretability features:
    *   Model output scores and metrics.
    *   Scatter plot of scores and error, and jitter plot of true labels for
        quick filtering.
    *   Embedding projector to find clusters in representation space.
    *   Integrated Gradients, LIME, and other salience methods.

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## Sequence-to-Sequence <!-- DO NOT REMOVE {#seq2seq .demo-section-header} -->

### Gemma <!-- DO NOT REMOVE {#gemma .demo-header} -->

**Code:**
[examples/prompt_debugging/server.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/prompt_debugging/server.py)

*   Supports Gemma 2B and 7B models using KerasNLP (with TensorFlow or PyTorch)
    and Transformers (with PyTorch).
*   Interactively debug LLM prompts using
    [sequence salience](./components.md#sequence-salience).
*   Multiple salience methods (grad-l2 and grad-dot-input), at multiple
    granularities: token-, word-, line-, sentence-, and paragraph-level.

Tip: check out the in-depth walkthrough at
https://ai.google.dev/responsible/model_behavior, part of the Responsible
Generative AI Toolkit.

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## Multimodal <!-- DO NOT REMOVE {#multimodal .demo-section-header} -->

### Tabular Data: Penguin Classification <!-- DO NOT REMOVE {#penguin .demo-header} -->

**Hosted instance:** https://pair-code.github.io/lit/demos/penguins.html \
**Code:** [examples/penguin/demo.py](https://github.com/PAIR-code/lit/blob/main/lit_nlp/examples/penguin/demo.py)

*   Binary classification on
    [penguin dataset](https://www.tensorflow.org/datasets/catalog/penguins).
*   Showing using of LIT on non-text data (numeric and categorical features).
*   Use partial-dependence plots to understand feature importance on individual
    examples, selections, or the entire evaluation dataset.
*   Use binary classifier threshold setters to find best thresholds for slices
    of examples to achieve specific fairness constraints, such as demographic
    parity.