Alessandro Ercolani

giux78

AI & ML interests

NLP, Reinforcement Learning, Semantics, Computational Neuroscience

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giux78's activity

posted an update 13 days ago
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1312
@FinancialSupport and I just released a new version of the Italian LLMs leaderboard FinancialSupport/open_ita_llm_leaderboard
using the super useful https://huggingface.co/demo-leaderboard template from @clefourrier .
We’ve evaluated over 50 models (base, merged, fine-tuned, etc.) from:
- Major companies like Meta, Mistral, Google ...
- University groups such as https://huggingface.co/sapienzanlp or https://huggingface.co/swap-uniba
- Italian Companies like https://huggingface.co/MoxoffSpA , https://huggingface.co/FairMind or https://huggingface.co/raicrits
- Various communities and individuals
All models were tested on #Italian benchmarks #mmlu #arc-c #hellaswag, which we contributed to the opensource lm-evaluation-harness library from https://huggingface.co/EleutherAI.
Plus, you can now submit your model for automatic evaluation, thanks to to https://huggingface.co/seeweb sponsored computation.
Curious about the top Italian models? Check out the leaderboard and submit your model!

FinancialSupport/open_ita_llm_leaderboard

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posted an update 24 days ago
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1519
@mik3ml just released ReDiX/wikipediaQA-ita an interesting synthetic dataset originated from wikipedia using a fine tuned version of mistral-7B specific for the Italian language 🇮🇹 .

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posted an update about 2 months ago
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1794
🎉 Super @DeepMount00 just released 𝗚𝗲𝗺𝗺𝗮_𝗤𝗔_𝗜𝗧𝗔_𝘃𝟯 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 the 𝗥𝗔𝗚 𝘁𝗮𝘀𝗸 on the Italian 𝗟𝗟𝗠_𝗜𝗧𝗔_𝗟𝗘𝗔𝗗𝗘𝗥𝗕𝗢𝗔𝗥𝗗. The model is a fine tuned version of Gemma 2B.
Model details: DeepMount00/Gemma_QA_ITA_v3
Explore the full RAG section rankings here: FinancialSupport/open_ita_llm_leaderboard on section Classifica RAG
posted an update 2 months ago
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1779
On evaluating fine tuned 7B Italian open source LLMs I have collected many data points and I created a super simple explorative analyses. My hypothesis based on data are:

- mmlu is hard to improve when fine tuning a base model on a different language
- fine tuning also on single GPUs can improve by 5% to 10% the base model on common tasks but a lot more on specific cases with the right training time and data
- fine tuning can specialize well but at cost of loosing some foundational knowledge.

Here the data https://docs.google.com/spreadsheets/d/1MBcxy1loK8eIycZG4DN84Q2ejZ0jSjxUBgoShHDR6IY/edit?usp=sharing
Here the colab https://colab.research.google.com/drive/1ra4_skG5QYWSYOzvagOoIoj4bibQD8Gw?usp=sharing
Here an article with some considerations https://medium.com/@giuxale/an-analyses-on-italian-llms-models-evaluations-51bffe1d44d1

replied to osanseviero's post 2 months ago
posted an update 2 months ago
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1284
Based on the work of @mrinaldi and @ruggsea we just released the biggest - ready for training - conversational dataset based on Usenet data in the Italian language 🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹. It contains about 9 millions of conversations made by real humans.

mii-community/UsenetArchiveIT-conversations
replied to their post 2 months ago
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@clefourrier sometimes rendering the data took lot of time as in the pic attached. But data are in a static a file inside the app. Is there a way to improve? FYI we are on the free tier.

Screenshot 2024-03-26 at 12.59.10.png

replied to their post 2 months ago
posted an update 2 months ago
posted an update 3 months ago
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Wonderful open source Italian dataset from @manalog and @ruggsea :

https://huggingface.co/datasets/manalog/UsenetArchiveIT

The dataset contributes to the https://huggingface.co/mii-community project, aimed at advancing the creation of Italian open-source Language Models (LLMs).🇮🇹 🤖 About 10-20 billion token, probably the best conversational open source dataset in the Italian language. 🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹🇮🇹
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replied to their post 3 months ago
posted an update 3 months ago
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Super work from @DeepMount00 :

🚀 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐥 𝐍𝐞𝐫: 𝐀 𝐆𝐥𝐢𝐍𝐞𝐫-𝐁𝐚𝐬𝐞𝐝 𝐈𝐭𝐚𝐥𝐢𝐚𝐧 𝐍𝐄𝐑

Introducing 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐥 𝐍𝐞𝐫 𝐟𝐨𝐫 𝐈𝐭𝐚𝐥𝐢𝐚𝐧 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞, a revolutionary Named Entity Recognition (NER) model evolved from the GliNer architecture and meticulously tailored for the Italian language. This advanced model is a beacon of efficiency and versatility, engineered to 𝐫𝐞𝐜𝐨𝐠𝐧𝐢𝐳𝐞 𝐚𝐧𝐲 𝐞𝐧𝐭𝐢𝐭𝐲 𝐭𝐲𝐩𝐞 within the rich nuances of Italian, using a bidirectional transformer encoder. It stands out as an ideal solution for those navigating the challenges of resource-limited environments or seeking an efficient alternative to the cumbersome Large Language Models (LLMs).
𝐑𝐮𝐧𝐬 𝐟𝐚𝐬𝐭 𝐚𝐥𝐬𝐨 𝐨𝐧 𝐂𝐏𝐔!

Experience this Italian-focused innovation live on Hugging Face Spaces:
DeepMount00/universal_ner_ita

Paper: https://arxiv.org/abs/2311.08526 Urchade Zaratiana et all. great work!
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posted an update 3 months ago