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Data is Better Together - Russian Language Team

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Russian speakers working on prompt translation as a part of the Data is Better Together initiative, building impactful community datasets.

DIBT-Russian's activity

ZennyKenny 
posted an update about 13 hours ago
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Submitted my first dataset for the Reasoning Datasets Competition! ZennyKenny/TRON-dataset-v.1.0

This dataset is designed to post-train Metareasoning agents, or those agents whose job it is to quickly (and importantly, cheaply) reason through whether it makes sense to launch a full reasoning job or simply use a simple completions job.

There's still plenty of time to join the competition! https://www.bespokelabs.ai/blog/reasoning-datasets-competition

Generation notebook (linked in dataset) is open source and pretty well generalized if I don't say so myself, so you can use it to make your own Metareasoning datasets.

Shoutout to @onekq for his inspiring comment on this topic.
ZennyKenny 
posted an update 8 days ago
ZennyKenny 
posted an update 17 days ago
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A few new Russian-language synthetic datasets. The labelling is good, but some of the syntax and grammar is not great.

Great for Russian-language classification models, probably not great for fine-tuning Russian-langauge text generation.

- Virtual Assistant Query / Responses: ZennyKenny/ru_virtual_assistant_chatgpt_distill
- LLM Query / Responses: ZennyKenny/russian_llm_response_chatgpt_distill

Crazy how much language drift is still an issue, especially given that Russian constitutes nearly 5% of the content on the internet.
ZennyKenny 
posted an update 22 days ago
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Besides being the coolest named benchmark in the game, HellaSwag is an important measurement of здравый смысль (or common sense) in LLMs.

- More on HellaSwag: https://github.com/rowanz/hellaswag

I spent the afternoon benchmarking YandexGPT Pro 4th Gen, one of the Russian tech giant's premier models.

- Yandex HF Org: yandex
- More on Yandex models: https://yandex.cloud/ru/docs/foundation-models/concepts/yandexgpt/models

The eval notebook is available on GitHub and the resulting dataset is already on the HF Hub!

- Eval Notebook: https://github.com/kghamilton89/ai-explorer/blob/main/yandex-hellaswag/hellaswag-assess.ipynb
- Eval Dataset: ZennyKenny/yandexgptpro_4th_gen-hellaswag

And of course, everyone wants to see the results so have a look at the results in the context of other zero-shot experiments that I was able to find!
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ZennyKenny 
posted an update about 1 month ago
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It took me a while, but I've finally got it working: ZennyKenny/note-to-text

Using a Meta LLaMa checkpoint from Unsloth and some help from the HF community, you can capture handwritten notes and convert them into digital format in just a few second.

Really exciting times for AI builders on Hugging Face.
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ZennyKenny 
posted an update about 2 months ago
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I've spent most of time working with AI on user-facing apps like Chatbots and TextGen, but today I decided to work on something that I think has a lot of applications for Data Science teams: ZennyKenny/comment_classification

This Space supports uploading a user CSV and categorizing the fields based on user-defined categories. The applications of AI in production are truly endless. 🚀
ZennyKenny 
posted an update about 2 months ago
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2212
Really excited to start contributing to the SWE Arena project: https://swe-arena.com/

Led by IBM PhD fellow @terryyz , our goal is to advance research in code generation and app development by frontier LLMs.
ZennyKenny 
posted an update about 2 months ago
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Okay this is pretty crazy. Snowflake has CortexAI and Uber is already teasing QueryGPT, both of which prominently feature plain text to SQL features to query your database.

I decided to see how hard it would be to put together something similar using 🤗 smolagents. Turns out, it was pretty straightforward. I managed to get it done in London Luton airport this afternoon.

ZennyKenny/sqlAgent
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ZennyKenny 
posted an update 2 months ago
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3449
I've completed the first unit of the just-launched Hugging Face Agents Course. I would highly recommend it, even for experienced builders, because it is a great walkthrough of the smolagents library and toolkit.
ZennyKenny 
posted an update 3 months ago
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GradientBoostingClassifier is an algorithm supported by the Python SciKit library, and now you can quickly train an ML model using this powerful technique on any (viable) dataset in the Hugging Face Hub without a line of code.

Love finishing a project right when the late night starts to turn into the early morning: sklearn-docs/GradientBoostingClassifier

Long time listener, first time caller, but always pleased to contribute, even if only adjacently, to the power of SciKit.
ZennyKenny 
posted an update 3 months ago
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Really pleased with the Bring Your Own Model (BYOM) feature in Brave Browser: https://brave.com/blog/byom-nightly/

Takes about 5 minutes to configure your own locally running LLM as an in-browser assistant. Totally local, totally private, totally yours.
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ZennyKenny 
posted an update 3 months ago
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On-demand audio transcription is an often-requested service without many good options on the market.

Using Hugging Face Spaces with Gradio SDK and the OpenAI Whisper model, I've put together a simple interface that supports the transcription and summarisation of audio files up to five minutes in length, completely open source and running on CPU upgrade. The cool thing is that it's built without a dedicated inference endpoint, completely on public infrastructure.

Check it out: ZennyKenny/AudioTranscribe

I wrote a short article about the backend mechanics for those who are interested: https://huggingface.co/blog/ZennyKenny/on-demand-public-transcription
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dvilasuero 
authored a paper 4 months ago
dvilasuero 
posted an update 4 months ago
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2593
🌐 Announcing Global-MMLU: an improved MMLU Open dataset with evaluation coverage across 42 languages, built with Argilla and the Hugging Face community.

Global-MMLU is the result of months of work with the goal of advancing Multilingual LLM evaluation. It's been an amazing open science effort with collaborators from Cohere For AI, Mila - Quebec Artificial Intelligence Institute, EPFL, Massachusetts Institute of Technology, AI Singapore, National University of Singapore, KAIST, Instituto Superior Técnico, Carnegie Mellon University, CONICET, and University of Buenos Aires.

🏷️ +200 contributors used Argilla MMLU questions where regional, dialect, or cultural knowledge was required to answer correctly. 85% of the questions required Western-centric knowledge!

Thanks to this annotation process, the open dataset contains two subsets:

1. 🗽 Culturally Agnostic: no specific regional, cultural knowledge is required.
2. ⚖️ Culturally Sensitive: requires dialect, cultural knowledge or geographic knowledge to answer correctly.

Moreover, we provide high quality translations of 25 out of 42 languages, thanks again to the community and professional annotators leveraging Argilla on the Hub.

I hope this will ensure a better understanding of the limitations and challenges for making open AI useful for many languages.

Dataset: https://huggingface.co/datasets/CohereForAI/Global-MMLU
ZennyKenny 
posted an update 5 months ago
ZennyKenny 
posted an update 5 months ago
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I've joined the Bluesky community. Interested to see what decentralized social media looks like in action: https://bsky.app/profile/kghamilton.bsky.social

Looking forward to following other AI builders, tech enthusiasts, goth doomscrollers, and ironic meme creators.
ZennyKenny 
posted an update 5 months ago
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Using AI to teach English as a Foreign Language? EFL teachers often have busy schedules, variable class sizes, and unexpected cancellations. Introducting VocabSova: ZennyKenny/VocabSova

VocabSova is a simple chatbot interface that helps teachers create topical vocabulary lists, custom worksheets using that vocabulary, and group activities on a defined theme for a specific English-speaking level (according to CEFR international standards).

There is a great use case for AI in nearly every field, and language learning is a particularly apt domain in my opinion. VocabSova is in active development during its Alpha release, all feedback welcome.
dvilasuero 
posted an update 5 months ago
dvilasuero 
posted an update 5 months ago
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Build datasets for AI on the Hugging Face Hub—10x easier than ever!

Today, I'm excited to share our biggest feature since we joined Hugging Face.

Here’s how it works:

1. Pick a dataset—upload your own or choose from 240K open datasets.
2. Paste the Hub dataset ID into Argilla and set up your labeling interface.
3. Share the URL with your team or the whole community!

And the best part? It’s:
- No code – no Python needed
- Integrated – all within the Hub
- Scalable – from solo labeling to 100s of contributors

I am incredibly proud of the team for shipping this after weeks of work and many quick iterations.

Let's make this sentence obsolete: "Everyone wants to do the model work, not the data work."


Read, share, and like the HF blog post:
https://huggingface.co/blog/argilla-ui-hub
dvilasuero 
posted an update 6 months ago
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Big news! You can now build strong ML models without days of human labelling

You simply:
- Define your dataset, including annotation guidelines, labels and fields
- Optionally label some records manually.
- Use an LLM to auto label your data with a human (you? your team?) in the loop!

Get started with this blog post:
https://huggingface.co/blog/sdiazlor/custom-text-classifier-ai-human-feedback