OALL/ALRAGE
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Manel ALOUI
Manel-Hik
AI & ML interests
NLP recommender system, machine learning
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updated
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about 1 month ago
OALL/ALRAGE
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Manel-Hik's activity
updated
a
dataset
about 1 month ago
reacted to
joylarkin's
post with ๐
3 months ago
Post
2628
๐ฌ Chat as a way to query SQL! The Airtrain AI team is happy to share a new Hugging Face Space that lets you interact with Hugging Face Hub datasets using a natural language chatbot. ๐ค
Start Exploring ๐ airtrain-ai/hf-dataset-chat-to-sql
This Space is forked from davidberenstein1957/text-to-sql-hub-datasetsย byย @davidberenstein1957 and features chat capability with improved table naming. The tool works with Hugging Faceโs recently released in-browser DuckDB-based SQL query engine for datasets.
Start Exploring ๐ airtrain-ai/hf-dataset-chat-to-sql
This Space is forked from davidberenstein1957/text-to-sql-hub-datasetsย byย @davidberenstein1957 and features chat capability with improved table naming. The tool works with Hugging Faceโs recently released in-browser DuckDB-based SQL query engine for datasets.
reacted to
Salama1429's
post with ๐
4 months ago
Post
1423
๐ Introducing the 101 Billion Arabic Words Dataset
๐ Exciting Milestone in Arabic Language Technology! hashtag#NLP hashtag#ArabicLLM hashtag#LanguageModels
๐ Why It Matters:
1. ๐ Large Language Models (LLMs) have brought transformative changes, primarily in English. It's time for Arabic to shine!
2. ๐ฏ This project addresses the critical challenge of bias in Arabic LLMs due to reliance on translated datasets.
๐ Approach:
1. ๐ช Undertook a massive data mining initiative focusing exclusively on Arabic from Common Crawl WET files.
2. ๐งน Employed state-of-the-art cleaning and deduplication processes to maintain data quality and uniqueness.
๐ Impact:
1. ๐ Created the largest Arabic dataset to date with 101 billion words.
2. ๐ Enables the development of Arabic LLMs that are linguistically and culturally accurate.
3. ๐ Sets a global benchmark for future Arabic language research.
๐ Paper: https://lnkd.in/dGAiaygn
๐ Dataset: https://lnkd.in/dGTMe5QV
- ๐ Share your thoughts and let's drive the future of Arabic NLP together!
hashtag#DataScience hashtag#MachineLearning hashtag#ArtificialIntelligence hashtag#Innovation hashtag#ArabicData
๐ Exciting Milestone in Arabic Language Technology! hashtag#NLP hashtag#ArabicLLM hashtag#LanguageModels
๐ Why It Matters:
1. ๐ Large Language Models (LLMs) have brought transformative changes, primarily in English. It's time for Arabic to shine!
2. ๐ฏ This project addresses the critical challenge of bias in Arabic LLMs due to reliance on translated datasets.
๐ Approach:
1. ๐ช Undertook a massive data mining initiative focusing exclusively on Arabic from Common Crawl WET files.
2. ๐งน Employed state-of-the-art cleaning and deduplication processes to maintain data quality and uniqueness.
๐ Impact:
1. ๐ Created the largest Arabic dataset to date with 101 billion words.
2. ๐ Enables the development of Arabic LLMs that are linguistically and culturally accurate.
3. ๐ Sets a global benchmark for future Arabic language research.
๐ Paper: https://lnkd.in/dGAiaygn
๐ Dataset: https://lnkd.in/dGTMe5QV
- ๐ Share your thoughts and let's drive the future of Arabic NLP together!
hashtag#DataScience hashtag#MachineLearning hashtag#ArtificialIntelligence hashtag#Innovation hashtag#ArabicData
Technical Report
#2 opened 4 months ago
by
Manel-Hik
Error while generating the datasets splits after downloading
1
#2 opened 5 months ago
by
JadwalAlmaa
Asking for params
6
#1 opened 5 months ago
by
Manel-Hik
updated
a
model
5 months ago
updated
2
datasets
5 months ago
updated
a
model
5 months ago
updated
a
dataset
5 months ago
updated
a
model
5 months ago
reacted to
alielfilali01's
post with ๐ค
7 months ago
Post
1983
I'm officially considered #gpu_poor ๐
But I'm #data_rich ๐
But I'm #data_rich ๐
liked
a
model
7 months ago
upvoted
an
article
7 months ago
Article
Introducing the Open Arabic LLM Leaderboard
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a
paper
8 months ago
upvoted
an
article
8 months ago
Article
๐ฆโ๏ธ Using Llama3 and distilabel to build fine-tuning datasets
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RuntimeError: The model is currently loading, please re-run the query.
1
#2 opened 12 months ago
by
Manel-Hik
reacted to
dvilasuero's
post with โค๏ธ
12 months ago
Post
๐ Hi there!
This is my very first post.
I'll use it to share some old news: a math preference dataset for DPO!
I created this dataset some time ago while we were developing distilabel (https://github.com/argilla-io/distilabel).
Some days ago we found out people are actually using it! So I'll use this post to explain how I built it in case it's useful for the community.
1. I used distilabel's SelfInstruct-inspired task to generate instructions about different math topics. I curated the instructions with Argilla (on Spaces!).
2. Then I used a distilabel Pipeline to build a preference dataset using gpt3.5 as generator and gpt4 as labeller. If I recall correctly I used our JudgeLM implementation (see https://distilabel.argilla.io/latest/technical-reference/tasks/#judgelmtask)
(see the screenshot with the dataset in the Argilla UI)
3. Then I just binarized into chosen, rejected pairs and voilร :
argilla/distilabel-math-preference-dpo
The funny thing is that I used this to do a second DPO run over Notus-7B. I hoped to see an improvement on math/reasoning skills but it actually improved in STEM and Humanities and did worse on Math ๐คฃ .
In conclusion, this dataset was only a quick experiement. I'm happy to see the community found it useful. Data for DPO and fine-tuning are still a mystery, let's unveil these mysteries in 2024 together!
Follow me for the most exciting datasets for LLMs (and maybe some great, small, efficient models). I plan to announce all Argilla open-source work here!
This is my very first post.
I'll use it to share some old news: a math preference dataset for DPO!
I created this dataset some time ago while we were developing distilabel (https://github.com/argilla-io/distilabel).
Some days ago we found out people are actually using it! So I'll use this post to explain how I built it in case it's useful for the community.
1. I used distilabel's SelfInstruct-inspired task to generate instructions about different math topics. I curated the instructions with Argilla (on Spaces!).
2. Then I used a distilabel Pipeline to build a preference dataset using gpt3.5 as generator and gpt4 as labeller. If I recall correctly I used our JudgeLM implementation (see https://distilabel.argilla.io/latest/technical-reference/tasks/#judgelmtask)
(see the screenshot with the dataset in the Argilla UI)
3. Then I just binarized into chosen, rejected pairs and voilร :
argilla/distilabel-math-preference-dpo
The funny thing is that I used this to do a second DPO run over Notus-7B. I hoped to see an improvement on math/reasoning skills but it actually improved in STEM and Humanities and did worse on Math ๐คฃ .
In conclusion, this dataset was only a quick experiement. I'm happy to see the community found it useful. Data for DPO and fine-tuning are still a mystery, let's unveil these mysteries in 2024 together!
Follow me for the most exciting datasets for LLMs (and maybe some great, small, efficient models). I plan to announce all Argilla open-source work here!
- 2 replies