Open-Source NLP Research Projects

With more than 500 participants from 39 countries, it is the largest open-source hackathon of NLP in Spanish. The recorded events have already more than 5k visualizations! Organized by Somos NLP and sponsored by Hugging Face, Platzi and Paperspace.

A one-year long international research workshop on large multilingual models and datasets. I was part of the data tooling working group. Here is the model paper BLOOM: A 176B-Parameter Open-Access Multilingual Language Model.

BERTIN is a series of RoBERTa-based models in Spanish trained using a novel sampling technique that we call "perplexity sampling". More detailed info can be found in the model card and the paper BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling.

The first NLP course from zero to hero in Spanish. It's open-source and was organized by Somos NLP with the support of Spain AI. I taught the classes on sequential models and the Transformer architecture.

A series of Spanish language models trained with Flax/Jax and using TPUs sponsored by Google during the Flax/Jax Community Week organized by Hugging Face in June 2021. Here are the model cards: GPT-2 model , T5 model and Wav2Vec2 model.

A program where we provide women entrepreneurs with the tools, knowledge, mentoring and network to successfully realize their startup/business idea in the AI sector.

Addition of 3 datasets in Spanish to the huggingface/datasets library during the open-sprint organized by Hugging Face in Dec 2020. The datasets are HEAD-QA (a multi-choice HEAlthcare Dataset), the dataset of the eHealth-KD Challenge at IberLEF 2020, and the Spanish Billion Words Corpus.

Chatbot that understands and answers questions about the COVID-19: symptoms, prevention, regulation, the situation in Spain. Don't hesitate to chat with AURORA!

The chatbot understands correctly on the 1st attempt 92% of the requests and helped 1500+ people during the first months of the pandemic. Collaboration with Accenture’s Gijón office.

Implementation of a Neural Network that predicts - with a correlation coefficient of 0.778 - characteristics of the Higgs Boson produced in the particle collider. Collaboration with the university's high energy particle research team.

PyPI package to perform quality analyses on ML models. It focuses on the three quality pillars: functionality, robustness and explainability.

Detailed and interactive explanation of the Transformer architecture. Based on the Harvard NLP notebook "The Annotated Transformer", where the paper "Attention Is All You Need" is explained and implemented.

Visualization for the #VizForSocialGood project to support the organization Operation Fistula on their "mission to end fistula for every woman everywhere".