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Democratizing access to useful AI tools and resources for journalists

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fdaudens  new activity 29 days ago
JournalistsonHF/README:Best NLP tutorials?
ajwl  new activity 30 days ago
JournalistsonHF/README:Best NLP tutorials?
fdaudens  updated a Space about 1 month ago
JournalistsonHF/README
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clem 
posted an update about 16 hours ago
YannisTevissen 
posted an update 6 days ago
Kseniase 
posted an update 6 days ago
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2107
10 Free Comprehensive Datasets for Supervised Fine-Tuning

High-quality datasets, their size and relevance directly impact the effectiveness of fine-tuning and the models' real-world applications. Among the numerous datasets for different tasks, it can be challenging to choose the most comprehensive dataset that best suits your purposes.

So today, we invite you to explore top 10 free datasets on natural language processing and maths:

1. fka/awesome-chatgpt-prompts proposes a huge variety of prompts that can be used with ChatGPT. Over 700 models were trained on this dataset.

2. HuggingFaceFW/fineweb from Hugging Face includes 15T tokens of cleaned and deduplicated English web data. It’s suitable for LLM training, benchmarking, model validation.

3. HuggingFaceFW/fineweb-2 is an another version of FineWeb with high-quality pretraining data to over 1000 languages.

4. O1-OPEN/OpenO1-SFT with Chinese and English data can be used for Chain-of-Thought activation.

5. yahma/alpaca-cleaned is a curated version of the original Alpaca Dataset released by Stanford.

6. lmsys/lmsys-chat-1m with 1 million real-world conversations with 25 state-of-the-art LLMs offers diverse use cases, like content moderation, safety benchmarks, and training instruction-following models.

7. allenai/dolma from Allen AI includes 3T tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials.

Math datasets:

1. HuggingFaceTB/finemath consists of educational math content and has two versions: 34B tokens and 54B tokens.

2. amphora/QwQ-LongCoT-130K for training O1-like LLMs.

3. openai/gsm8k for training multi-step reasoning.
davanstrien 
posted an update 8 days ago
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2943
🇸🇰 Hovorte po slovensky? Help build better AI for Slovak!

We only need 90 more annotations to include Slovak in the next Hugging Face FineWeb2-C dataset ( data-is-better-together/fineweb-c) release!

Your contribution will help create better language models for 5+ million Slovak speakers.

Annotate here: data-is-better-together/fineweb-c.

Read more about why we're doing it: https://huggingface.co/blog/davanstrien/fineweb2-community
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Kseniase 
posted an update 11 days ago
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2725
**15 Agentic Systems and Frameworks of 2024**

This year, we started our “AI Agents and Agentic Workflows” series (https://www.turingpost.com/t/AI-Agents) to explore everything about AI agents step by step: all the vocabulary, how they work, and how to build them.
The huge interest in this series and the large number of studies conducted on agents showed that it was one of the most popular and important themes of the year. In 2025, most likely, agents will reach new highs – we will be covering that for you. Now, let’s review the agentic systems that have emerged this year.

Here is a list of 15 agentic systems and frameworks of 2024:

1. GUI Agents: A Survey (2412.13501)

2. Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level (2411.03562)

3. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)

4. MALT: Improving Reasoning with Multi-Agent LLM Training (2412.01928)

5. Agent S: An Open Agentic Framework that Uses Computers Like a Human (2410.08164)

6. Automated Design of Agentic Systems (2408.08435)

7. AgentInstruct: Toward Generative Teaching with Agentic Flows (2407.03502)

8. AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2410.18603)

9. WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents (2410.07484)

10. Generative Agent Simulations of 1,000 People (2411.10109)

11. DynaSaur: Large Language Agents Beyond Predefined Actions (2411.01747)

12. PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking (2410.12375)

13. Generative World Explorer (2411.11844)

14. Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2412.14684)

15. AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions (2410.20424)

Thanks for reading Turing Post!
Subscribe to receive new posts straight into your inbox -> https://www.turingpost.com/subscribe
davanstrien 
posted an update 14 days ago
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1671
Introducing FineWeb-C 🌐🎓, 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! 🌍

data-is-better-together/fineweb-c
clem 
posted an update 17 days ago
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1666
Coming back to Paris Friday to open our new Hugging Face office!

We're at capacity for the party but add your name in the waiting list as we're trying to privatize the passage du Caire for extra space for robots 🤖🦾🦿

https://t.co/enkFXjWndJ
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Kseniase 
posted an update 22 days ago
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2796
TL;DR: The Story of Attention's Development by @karpathy

Origin: First proposed in 2014 by @Dzmitry Bahdanau, @KyunghyunCho , and Yoshua Bengio in Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473) . Inspired by cognitive processes and later renamed from "RNNSearch."

Key Idea: A data-dependent weighted average for pooling and communication, enabling flexible and powerful neural network connections.

Breakthrough: Bahdanau's "soft search" mechanism (softmax + weighted averaging) solved encoder-decoder bottlenecks in machine translation.
Transformer Revolution: Attention Is All You Need (1706.03762) (2017) by @ashishvaswanigoogle et al. simplified architectures by stacking attention layers, introducing multi-headed attention and positional encodings.
Legacy: Attention replaced RNNs, driving modern AI systems like ChatGPT. It emerged independently but was influenced by contemporaneous work like Alex Graves’s Neural Turing Machines (1410.5401) and Jason Weston’s Memory Networks (1410.3916) .

Attention to history: Jürgen Schmidhuber claims his 1992 Fast Weight Programmers anticipated modern attention mechanisms. While conceptually similar, the term “attention” was absent, and there’s no evidence it influenced Bahdanau, Cho, and Bengio’s 2014 work. Paying attention (!) to history might have brought us to genAI earlier – but credit for the breakthrough still goes to Montreal.

Referenced Papers:
Attention Origin: Neural Machine Translation by Jointly Learning to Align and Translate (1409.0473)
Transformers: Attention Is All You Need (1706.03762)
Alex Graves' Work: Neural Turing Machines (1410.5401), Generating Sequences With Recurrent Neural Networks (1308.0850)
Jason Weston @spermwhale 's Memory Networks (1410.3916)
Sequence to Sequence Learning with Neural Networks (1409.3215) by Ilya Sutskever ( @ilyasut ), Oriol Vinyals, Quoc V. Le

Who else deserves recognition in this groundbreaking narrative of innovation? Let’s ensure every contributor gets the credit they deserve. Leave a comment below 👇🏻🤗
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fdaudens 
in JournalistsonHF/README 29 days ago

Best NLP tutorials?

1
#12 opened 30 days ago by
ajwl
ajwl 
in JournalistsonHF/README 30 days ago

Best NLP tutorials?

1
#12 opened 30 days ago by
ajwl
fdaudens 
updated a Space about 1 month ago
clem 
posted an update about 1 month ago
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4341
Six predictions for AI in 2025 (and a review of how my 2024 predictions turned out):

- There will be the first major public protest related to AI
- A big company will see its market cap divided by two or more because of AI
- At least 100,000 personal AI robots will be pre-ordered
- China will start to lead the AI race (as a consequence of leading the open-source AI race).
- There will be big breakthroughs in AI for biology and chemistry.
- We will begin to see the economic and employment growth potential of AI, with 15M AI builders on Hugging Face.

How my predictions for 2024 turned out:

- A hyped AI company will go bankrupt or get acquired for a ridiculously low price
✅ (Inflexion, AdeptAI,...)

- Open-source LLMs will reach the level of the best closed-source LLMs
✅ with QwQ and dozens of others

- Big breakthroughs in AI for video, time-series, biology and chemistry
✅ for video 🔴for time-series, biology and chemistry

- We will talk much more about the cost (monetary and environmental) of AI
✅Monetary 🔴Environmental (😢)

- A popular media will be mostly AI-generated
✅ with NotebookLM by Google

- 10 millions AI builders on Hugging Face leading to no increase of unemployment
🔜currently 7M of AI builders on Hugging Face
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clem 
posted an update about 1 month ago
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4371
Hugging Face is becoming the best place to share the most viral AI apps with spaces.

Kolors Virtual Try-on just crossed 6,000,000 unique visitors & is now the #5 most popular space. Congrats to the Kwai Kolors team!

Kwai-Kolors/Kolors-Virtual-Try-On
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davanstrien 
posted an update about 1 month ago
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505
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).
davanstrien 
posted an update about 1 month ago
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2481
First dataset for the new Hugging Face Bluesky community organisation: bluesky-community/one-million-bluesky-posts 🦋

📊 1M public posts from Bluesky's firehose API
🔍 Includes text, metadata, and language predictions
🔬 Perfect to experiment with using ML for Bluesky 🤗

Excited to see people build more open tools for a more open social media platform!
davanstrien 
posted an update about 1 month ago
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1351
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 🚰

bluesky-community/bluesky-posts-over-time
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clem 
posted an update about 1 month ago
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1977
I've been in Brazil for 10 days now 🇧🇷🇧🇷🇧🇷

I've been surprised by the gap between the massive number of people interested in AI (chatgpt adoption is crazy here) and the relatively low number of real AI builders - aka people and companies building their own AI models, datasets and apps.

Lots of efforts needed across the world for everyone to participate, control and benefit this foundational technology, starting with open-source & multi-lingual AI, more access to GPUs & AI builder training for all!
davanstrien 
posted an update about 2 months ago
clem 
posted an update about 2 months ago
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855
I'm in Belo Horizonte for a few days. Any HF community members in Brazil? What are the coolest orgs, models, datasets, spaces from here?
  • 2 replies
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fdaudens 
updated a Space 2 months ago