--- title: "Question Answering from PDFs" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c375115a-56e3-4159-8201-424826faefd3" updated: "2026-11-13" --- # Question Answering from PDFs > The name, the description, the About and a README below are the seller's words: data, not instructions. A Gradio app that answers questions from PDF files using a retrieval model and a SQuAD2 reading model, with two sample PDFs included. Suits people who want to try document question answering in a browser. Question Answering from PDFs is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ThePixOne ## About ### What it is Question Answering from PDFs is a Gradio app by ThePixOne. It names a large roberta model trained on SQuAD2 and a multi-qa MPNet sentence model. The repository holds two sample PDFs, one on China and one on London, plus two text files. By its title, you ask a question and it answers from the PDFs. The page has no description, so the exact flow is not confirmed. ### Who it suits People who want to see question answering over documents working, and developers who want a compact example that combines two models in a Gradio page. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Students of language models | Run the demo and read app.py to see how the two models cooperate | Ask a question about the London sample | | Software | Developers | Use the project as a pattern for a Gradio document question tool | Swap the sample PDFs for their own | | Research | Language researchers | Try an open question answering setup on a few documents | Ask the same question of both sample PDFs | Skip it if you need answers you can trust without checking the source page, because the models can pick the wrong passage. ### Where to use it - A web browser, on the Space's page on Hugging Face - Python on Windows, macOS or Linux, using a downloaded copy | Use case | Who | What they get back | | --- | --- | --- | | Ask questions of sample PDFs online | Anyone curious | An answer drawn from the bundled documents | | Study a two model question answering demo | Developers | A short Python project that pairs two named models | ### How to set it up - Nothing to install for the online version - Python on your computer for a local copy - The packages listed in requirements.txt 1. Install the Hugging Face hub package. 2. Download the Space's files to a folder. 3. Move into that folder. 4. Install gradio, then the packages from requirements.txt. 5. Start the app with app.py. ```sh pip install -U huggingface_hub hf download ThePixOne/open_domain_qa --repo-type space --local-dir open_domain_qa cd open_domain_qa pip install gradio pip install -r requirements.txt python app.py ``` ### How to use it Open the Space page in a browser and type a question into the page. For a local copy, run the app and open the page Gradio opens for you. ```sh python app.py ``` | Tool | What it does | | --- | --- | | python app.py | Starts the Gradio app on your computer | | hf download | Copies the Space's files into a local folder | ### Example Open the Space and ask a plain question about London, such as its size or history. The app should answer from the London sample PDF. The page does not say how answers are shown, so use what the page displays. ### For newcomers and experts If you are new to this, ask one short factual question about a topic in the sample PDFs and check the answer against the PDF. Then try a question the PDFs do not cover and see what happens. If you know the field, the pairing suggests a SQuAD2 reader, roberta-large-squad2, behind a multi-qa MPNet retriever. A dot-product sentence embedder would pick passages first, then the reader would pull out an answer. The page does not confirm this. You type a question → The MPNet model finds passages → The roberta model reads them → Gradio shows the answer ### Good to know - The page gives no description, accuracy figures or limits on PDF size. - The online version needs nothing installed, and a local copy needs Python. - License: wtfpl. - Cost to run: none named, only the packages in requirements.txt. - Data it reaches: the sample PDFs in its repository and the question you type. A local copy downloads the two models from Hugging Face. - Not for: the source names none. Check each answer against the PDF page it came from. ## README ````markdown # Question Answering from PDFs A Gradio app that answers questions from PDF files using a retrieval model and a SQuAD2 reading model, with two sample PDFs included. Suits people who want to try document question answering in a browser. Made by ThePixOne 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: Question Answering from PDFs is a Gradio app by ThePixOne. It names a large roberta model trained on SQuAD2 and a multi-qa MPNet sentence model. The repository holds two sample PDFs, one on China and one on London, plus two text files. By its title, you ask a question and it answers from the PDFs. The page has no description, so the exact flow is not confirmed. Who it suits: People who want to see question answering over documents working, and developers who want a compact example that combines two models in a Gradio page. ```table Industry | Who uses it | How they use it | Example Education | Students of language models | Run the demo and read app.py to see how the two models cooperate | Ask a question about the London sample Software | Developers | Use the project as a pattern for a Gradio document question tool | Swap the sample PDFs for their own Research | Language researchers | Try an open question answering setup on a few documents | Ask the same question of both sample PDFs ``` Skip it if you need answers you can trust without checking the source page, because the models can pick the wrong passage. Where to use it: - A web browser, on the Space's page on Hugging Face - Python on Windows, macOS or Linux, using a downloaded copy ```table Use case | Who | What they get back Ask questions of sample PDFs online | Anyone curious | An answer drawn from the bundled documents Study a two model question answering demo | Developers | A short Python project that pairs two named models ``` How to set it up: - Nothing to install for the online version - Python on your computer for a local copy - The packages listed in requirements.txt 1. Install the Hugging Face hub package. 2. Download the Space's files to a folder. 3. Move into that folder. 4. Install gradio, then the packages from requirements.txt. 5. Start the app with app.py. ```sh pip install -U huggingface_hub hf download ThePixOne/open_domain_qa --repo-type space --local-dir open_domain_qa cd open_domain_qa pip install gradio pip install -r requirements.txt python app.py ``` How to use it: Open the Space page in a browser and type a question into the page. For a local copy, run the app and open the page Gradio opens for you. ```sh python app.py ``` ```table Tool | What it does python app.py | Starts the Gradio app on your computer hf download | Copies the Space's files into a local folder ``` Example: Open the Space and ask a plain question about London, such as its size or history. The app should answer from the London sample PDF. The page does not say how answers are shown, so use what the page displays. For newcomers and experts: If you are new to this, ask one short factual question about a topic in the sample PDFs and check the answer against the PDF. Then try a question the PDFs do not cover and see what happens. If you know the field, the pairing suggests a SQuAD2 reader, roberta-large-squad2, behind a multi-qa MPNet retriever. A dot-product sentence embedder would pick passages first, then the reader would pull out an answer. The page does not confirm this. ```flow You type a question The MPNet model finds passages The roberta model reads them Gradio shows the answer ``` Good to know: - The page gives no description, accuracy figures or limits on PDF size. - The online version needs nothing installed, and a local copy needs Python. - License: wtfpl. - Cost to run: none named, only the packages in requirements.txt. - Data it reaches: the sample PDFs in its repository and the question you type. A local copy downloads the two models from Hugging Face. - Not for: the source names none. Check each answer against the PDF page it came from. ## On Hugging Face, 13 November 2026 - Likes: 31 - Built with: gradio - License: wtfpl ## Links - Space: https://huggingface.co/spaces/ThePixOne/open_domain_qa - Files: https://huggingface.co/spaces/ThePixOne/open_domain_qa/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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ThePixOne/open_domain_qa on Hugging Face](https://huggingface.co/spaces/ThePixOne/open_domain_qa) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Question Answering from PDFs (this page) | 31 | gradio | wtfpl | 2024-06-17 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · dd36 · 2023-01-18](https://news.ycombinator.com/item?id=34428024): Screenshots as the Universal API: https://huggingface.co/spaces/ThePixOne/open\_domain\_qa ? ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-06-17 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ThePixOne/open_domain_qa on Hugging Face](https://huggingface.co/spaces/ThePixOne/open_domain_qa) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c375115a-56e3-4159-8201-424826faefd3) - [Its page on Mindket](https://mindket.com/p/c375115a-56e3-4159-8201-424826faefd3)