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davanstrienĀ 
posted an update 1 day ago
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Introducing FineWeb-C šŸŒšŸŽ“, a community-built dataset for improving language models in ALL languages.

Inspired by FineWeb-Edu the community is labelling the educational quality of texts for many languages.

318 annotators, 32K+ annotations, 12 languages - and growing! šŸŒ

data-is-better-together/fineweb-c
davanstrienĀ 
posted an update 23 days ago
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465
Increasingly, LLMs are becoming very useful for helping scale annotation tasks, i.e. labelling and filtering. When combined with the structured generation, this can be a very scalable way of doing some pre-annotation without requiring a large team of human annotators.

However, there are quite a few cases where it still doesn't work well. This is a nice paper looking at the limitations of LLM as an annotator for Low Resource Languages: On Limitations of LLM as Annotator for Low Resource Languages (2411.17637).

Humans will still have an important role in the loop to help improve models for all languages (and domains).
davanstrienĀ 
posted an update 26 days ago
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2467
First dataset for the new Hugging Face Bluesky community organisation: bluesky-community/one-million-bluesky-posts šŸ¦‹

šŸ“Š 1M public posts from Bluesky's firehose API
šŸ” Includes text, metadata, and language predictions
šŸ”¬ Perfect to experiment with using ML for Bluesky šŸ¤—

Excited to see people build more open tools for a more open social media platform!
davanstrienĀ 
posted an update 26 days ago
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1348
The Bluesky AT Protocol unlocks exciting possibilities:
- Building custom feeds using ML
- Creating dashboards for data exploration
- Developing custom models for Bluesky
To gather Bluesky resources on the Hub, I've created a community org: https://huggingface.co/bluesky-community

My first rather modest contribution is a dashboard that shows the number of posts every second. Drinking straight from the firehose API šŸš°

bluesky-community/bluesky-posts-over-time
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davanstrienĀ 
posted an update about 1 month ago
davanstrienĀ 
posted an update about 2 months ago
davanstrienĀ 
posted an update 3 months ago
davanstrienĀ 
posted an update 3 months ago
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2187
Yesterday, I shared a blog post on generating data for fine-tuning ColPali using the Qwen/Qwen2-VL-7B-Instruct model.

To simplify testing this approach, I created a Space that lets you generate queries from an input document page image: davanstrien/ColPali-Query-Generator

I think there is much room for improvement, but I'm excited about the potential for relatively small VLMs to create synthetic data.

You can read the original blog post that goes into more detail here: https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html
davanstrienĀ 
posted an update 3 months ago
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3168
ColPali is revolutionizing multimodal retrieval, but could it be even more effective with domain-specific fine-tuning?

Check out my latest blog post, where I guide you through creating a ColPali fine-tuning dataset using Qwen/Qwen2-VL-7B-Instruct to generate queries for a collection of UFO documents sourced from the Internet Archive.

The post covers:
- Introduction to data for ColPali models
- Using Qwen2-VL for retrieval query generation
- Tips for better query generation

Check out the post here:
https://danielvanstrien.xyz/posts/post-with-code/colpali/2024-09-23-generate_colpali_dataset.html

The resulting Hugging Face dataset: davanstrien/ufo-ColPali
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davanstrienĀ 
posted an update 3 months ago
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šŸ›ø I'm working on a pipeline for creating domain-specific ColPali fine-tuning datasets using a collection of UFO newsletters from the Internet Archive as a case study.

I will have a full notebook to share on Monday, but you can already take a look at the dataset here: davanstrien/ufo-ColPali
davanstrienĀ 
posted an update 3 months ago
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1690
Almost ready: search for a Hugging Face dataset on the Hub from information in the datasets viewer preview!

Soon, you can find deep-cut datasets even if they don't have a full dataset card (you should still document your datasets!)

You can help improve this project by rating synthetic user search queries for hub datasets.

If you have a Hub login, you can start annotating in Argilla
in < 5 seconds here: https://davanstrien-my-argilla.hf.space/dataset/1100a091-7f3f-4a6e-ad51-4e859abab58f/annotation-mode

I need to do some tidying, but I'll share all the code and in-progress datasets for this soon!