HF SHAP

HF SHAP

Explains how text models reach their outputs with SHAP, on five named models for question answering, translation, emotion, summarization and text generation. For NLP learners and researchers.

Made by Hellisotherpeople on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: HF SHAP is a Streamlit app that, by its name, uses SHAP to explain text models. SHAP scores how much each part of an input pushes a model's output. The record names five models, which by their names cover question answering, English to Spanish translation, emotion, summarization and text generation.

Who it suits: It suits NLP researchers, students and teachers who want to see why a text model gave the output it did. Developers can read it as a small example of one page serving several model types.

Industry | Who uses it | How they use it | Example
Research | NLP researchers | Check which words drive a model's output | Pick a model and enter a short text
Education | Students and teachers | Show that model decisions can be inspected | Run the same sentence through two tasks
Software | Python developers | Read how several models share one Streamlit page | Open app.py and follow the model setup

You can skip it if you are not working with text models.

Where to use it:

• A web browser, on the Space's page on Hugging Face
• Python 3.12 on Windows, macOS or Linux, from a downloaded copy

Use case | Who | What they get back
Inspect a model's output | Researchers | A view of which input words mattered
Teach model explanation | Teachers | A hands-on page to click through
Compare tasks | Students | Explanations for different model types side by side

How to set it up:

• Nothing at all if you use the online Space
• Python 3.12 on your computer, only if you run a copy
• The packages listed in requirements.txt

1. Open the Space's page in a browser to use it with nothing to install.
2. To run a copy, install the Hugging Face Hub package and download the Space.
3. Move into the new folder, then install Streamlit and the listed requirements.
4. Start the app with Streamlit.

pip install -U huggingface_hub
hf download Hellisotherpeople/HF-SHAP --repo-type space --local-dir HF-SHAP
cd HF-SHAP
pip install streamlit
pip install -r requirements.txt
streamlit run app.py

How to use it: Online, open the Space and use the controls on its page. On your own machine, run the main file with Streamlit from the Space folder, then use the page it serves. The record does not list the controls, so look at the page.

streamlit run app.py

Example: You open the Space, choose one of the models and enter a short text. The app returns the model's output and shows which words weighed most on it. The record shows no sample, so the exact controls may differ.

For newcomers and experts: Explainability means asking why a model gave a certain answer. Start by opening the Space, choosing one task and typing a short sentence to see what comes back.

If you know the field, SHAP gives each input token a contribution to the output, based on Shapley values. Running it across several model types in one page shows how the method behaves per task. The record gives no settings, and basic CPU hosting may make long inputs slow.

You pick a model and enter text
The app runs the model on your text
SHAP scores each input word
The page shows the scores

Good to know:

• License: MIT, as listed on the Space
• Cost to run: none named
• Data it reaches: the text you enter, the five models it names and the DebateSum dataset
• Not for: the record names no exclusions, and it gives no accuracy figures

On Hugging Face, 13 November 2026

• Likes: 8
• Built with: streamlit
• License: mit

Links

• Space: https://huggingface.co/spaces/Hellisotherpeople/HF-SHAP
• Files: https://huggingface.co/spaces/Hellisotherpeople/HF-SHAP/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.