Cv Parser

Cv Parser

A Gradio app that reads a resume and pulls out details with several language models, split into reader, segmenter and parser files. It suits recruiters and developers.

Made by asimokby 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: Cv Parser is a Gradio app on Hugging Face Spaces that parses a CV, which is a resume. The README holds only the words CV parser. The code is split into a reader, a segmenter, a parser and a models file, and the settings name five models, among them dslim/bert-base-NER and flair/pos-english-fast.

Who it suits: It suits people who want to pull structured details out of resumes, and developers who want to read how a resume parsing pipeline is organised.

Industry | Who uses it | How they use it | Example
Recruiting | Recruiters | Try pulling details out of a candidate resume | Open the Space and give it a resume
Human resources | HR analysts | See what a parser can read from a document | Test it on a sample resume
Software | Python developers | Read the reader, segmenter and parser files | Download the Space and open ResumeParser.py

Anyone who needs documented output fields or measured accuracy can skip it, since the source gives none.

Where to use it:

• A web browser, straight from the Space page on Hugging Face
• A local copy on Windows, macOS or Linux, using Python 3.9.7

Use case | Who | What they get back
Parse a resume | Recruiters | Details read from the resume, shown in the browser
Study a parsing pipeline | Developers | Separate files for reading, segmenting and parsing a resume

How to set it up: Using it online needs nothing. A local copy needs these first.

• Python 3.9.7 and pip
• The huggingface_hub package, to fetch the files
• Gradio 3.50, and the packages in requirements.txt

1. Install the Hugging Face Hub package.
2. Download the Space into a folder called cv-parser-huggingface.
3. Move into that folder.
4. Install Gradio 3.50, then the packages from requirements.txt.
5. Start the app.

pip install -U huggingface_hub
hf download asimokby/cv-parser-huggingface --repo-type space --local-dir cv-parser-huggingface
cd cv-parser-huggingface
pip install gradio==3.50
pip install -r requirements.txt
python app.py

How to use it: Open the Space page on Hugging Face and give it a resume. For a local copy, run python app.py in the folder and open the page it starts.

Example: You give the app a resume file on the Space page. It reads and splits the document, runs its language models over the parts and shows the details it finds.

For newcomers and experts: If you are new, try it with a sample resume that holds no private details. Parsing means a program reads a document and sorts its content into labelled parts, such as names and dates.

If you know the field, the named models cover entity tagging, part-of-speech tagging and zero-shot classification, and one of them handles dates in French. How the files split the work is in the code, not in the README.

You give the app a resume
The reader and segmenter split the document
The models tag the parts and the page shows them

Good to know:

• The README is a one-line title, so the exact output fields are clear only on the page and in the code
• It is built for Python 3.9.7 and Gradio 3.50, and it loads five named models
• License: mit
• Cost to run: none named
• Data it reaches: the resumes you give it and the models it names

On Hugging Face, 13 November 2026

• Likes: 3
• Built with: gradio
• License: mit

Links

• Space: https://huggingface.co/spaces/asimokby/cv-parser-huggingface
• Files: https://huggingface.co/spaces/asimokby/cv-parser-huggingface/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.