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Democratizar el PLN en español e incentivar su aplicación para generar impacto social 💛

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DrishtiSharma  authored a paper about 1 month ago
1-800-SHARED-TASKS at RegNLP: Lexical Reranking of Semantic Retrieval (LeSeR) for Regulatory Question Answering
DrishtiSharma  authored a paper about 1 month ago
Maya: An Instruction Finetuned Multilingual Multimodal Model
DrishtiSharma  authored a paper about 2 months ago
INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge
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somosnlp's activity

nataliaElv 
posted an update 1 day ago
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748
New chapter in the Hugging Face NLP course! 🤗 🚀

We've added a new chapter about the very basics of Argilla to the Hugging Face NLP course. Learn how to set up an Argilla instance, load & annotate datasets, and export them to the Hub. 

Any feedback for improvements welcome!

https://huggingface.co/learn/nlp-course/chapter10
nataliaElv 
posted an update 9 days ago
nataliaElv 
posted an update about 1 month ago
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1660
If you are still wondering how the FineWeb2 annotations are done, how to follow the guidelines or how Argilla works, this is your video!

I go through a few samples of the FineWeb2 dataset and classify them based on their educational content. Check it out!

https://www.youtube.com/watch?v=_-ORB4WAVGU
nataliaElv 
posted an update about 1 month ago
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1286
How do your annotations for FineWeb2 compare to your teammates'?

I started contributing some annotations to the FineWeb2 collaborative annotation sprint and I wanted to know if my labelling trends were similar to those of my teammates.

I did some analysis and I wasn't surprised to see that I'm being a bit harsher on my evaluations than my mates 😂


Do you want to see how your annotations compare to others?
👉 Go to this Gradio space: nataliaElv/fineweb2_compare_my_annotations
✍️ Enter the dataset that you've contributed to and your Hugging Face username.

How were your results?
- Contribute some annotations: data-is-better-together/fineweb-c
- Join your language channel in Rocket chat: HuggingFaceFW/discussion
nataliaElv 
posted an update about 2 months ago
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1187
We're so close to reaching 100 languages! Can you help us cover the remaining 200? Check if we're still looking for language leads for your language: nataliaElv/language-leads-dashboard
nataliaElv 
posted an update about 2 months ago
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1637
Would you like to get a high-quality dataset to pre-train LLMs in your language? 🌏

At Hugging Face we're preparing a collaborative annotation effort to build an open-source multilingual dataset as part of the Data is Better Together initiative.

Follow the link below, check if your language is listed and sign up to be a Language Lead!

https://forms.gle/s9nGajBh6Pb9G72J6
nataliaElv 
posted an update about 2 months ago
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365
You can now add your Bluesky handle to your Hugging Face profile! 🦋
Have you noticed?
mariagrandury 
updated a collection 2 months ago
haritzpuerto 
authored a paper 2 months ago
gabrielmbmb 
posted an update 4 months ago
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1836
Yesterday   @mattshumer released mattshumer/Reflection-Llama-3.1-70B, an impressive model that achieved incredible results in benchmarks like MMLU. The model was fine-tuned using Reflection-Tuning and the dataset used wasn't released, but I created a small recipe with distilabel that allows generating a dataset with a similar output format:

1. We use MagPie 🐦 in combination with https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct to generate reasoning instructions.
2. We generate a response again using https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct, but we steer the LLM to generate an specific output format using a custom system prompt. In the system prompt, we instruct the LLM that it will have first to think 💭 and have reflections that will help resolving ambiguities. After that, we instruct the LLM to generate an output based on the previous thinking

In this dataset gabrielmbmb/distilabel-reflection-tuning you can found 5 rows that I generated with this recipe. You can also found the code of the pipeline in the file called reflection.py.
daqc 
updated a model 5 months ago
gabrielmbmb 
posted an update 5 months ago
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2914
distilabel 1.3.0 is out! This release contains many core improvements and new tasks that help us building argilla/magpie-ultra-v0.1!

Distributed pipeline execution with Ray, new Magpie tasks, reward models, components for dataset diversity based on sentence embeddings, Argilla 2.0 compatibility and many more features!

Check the new release in GitHub: https://github.com/argilla-io/distilabel
gabrielmbmb 
posted an update 6 months ago
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3568
Just dropped magpie-ultra-v0.1! The first open synthetic dataset generated with Llama 3.1 405B. Created with distilabel, it's our most advanced and compute-intensive pipeline to date. We made the GPUs of the cluster go brrrrr 🚀

argilla/magpie-ultra-v0.1

Take it a look and tell us what you think! Probably, the models taking the most out of it are smol models 🤗 We will be improving the dataset in upcoming iterations!
Iker 
authored a paper 6 months ago
mariagrandury 
updated a Space 6 months ago
haritzpuerto 
authored a paper 6 months ago
mariagrandury 
updated a Space 7 months ago
rovi27 
updated a dataset 7 months ago
gabrielmbmb 
posted an update 7 months ago
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2503
⚗️ distilabel 1.2.0 is out and it comes with improved support for structured generation, new tasks for generating datasets for training embedding models, new steps for loading data, MixtureOfAgentsLLM and improved docs.

We would love to see a few new datasets for training embedding models built with distilabel on the Hub! ❤️
osanseviero 
updated a Space 7 months ago