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Quentin Lhoest PRO
lhoestq
AI & ML interests
Maintainer of 🤗Datasets: NLP, Multimodal data processing and sharing
Recent Activity
published
a dataset
about 15 hours ago
lhoestq/tmp
liked
a dataset
about 19 hours ago
BIOMEDICA/biomedica_webdataset_24M
liked
a dataset
about 19 hours ago
BIOMEDICA/biomedica_webdataset
Articles
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lhoestq's activity
published
a
dataset
about 15 hours ago
liked
2
datasets
about 19 hours ago
updated
a
dataset
about 19 hours ago
updated
a
dataset
about 21 hours ago
upvoted
a
paper
about 23 hours ago
updated
a
dataset
1 day ago
reacted to
merve's
post with 🤗🔥
1 day ago
Post
1265
New smolagents example landed on Hugging Face cookbook 🤠
Learn how to create an inventory managing multi-agent system with smolagents, MongoDB and DeepSeek Chat 📖 https://huggingface.co/learn/cookbook/mongodb_smolagents_multi_micro_agents
Learn how to create an inventory managing multi-agent system with smolagents, MongoDB and DeepSeek Chat 📖 https://huggingface.co/learn/cookbook/mongodb_smolagents_multi_micro_agents
reacted to
ariG23498's
post with 🚀
1 day ago
Post
1451
Timm ❤️ Transformers
Wtih the latest version of transformers you can now use any timm model with the familiar transformers API.
Blog Post: https://huggingface.co/blog/timm-transformers
Repository with examples: https://github.com/ariG23498/timm-wrapper-examples
Collection: ariG23498/timmwrapper-6777b85f1e8d085d3f1374a1
Wtih the latest version of transformers you can now use any timm model with the familiar transformers API.
Blog Post: https://huggingface.co/blog/timm-transformers
Repository with examples: https://github.com/ariG23498/timm-wrapper-examples
Collection: ariG23498/timmwrapper-6777b85f1e8d085d3f1374a1
reacted to
singhsidhukuldeep's
post with 🚀
1 day ago
Post
900
Breaking News: LinkedIn's Content Search Engine Gets a Powerful Semantic Upgrade!
Excited to share insights about LinkedIn's innovative approach to content search, recently detailed in a groundbreaking paper by their Mountain View team. This advancement represents a significant shift from traditional keyword-based search to semantic understanding.
>> Technical Architecture
The new search engine employs a sophisticated two-layer architecture:
Retrieval Layer
- Token Based Retriever (TBR) for exact keyword matching
- Embedding Based Retriever (EBR) using a two-tower model with multilingual-e5 embeddings
- Pre-computed post embeddings stored in a dedicated embedding store for efficient retrieval
Multi-Stage Ranking
- L1 Stage: Initial filtering using a lightweight model
- L2 Stage: Advanced ranking with complex features including:
- Query-post semantic matching
- Author reputation analysis
- User engagement metrics
- Content freshness evaluation
>> Performance Improvements
The system has achieved remarkable results:
- 10%+ improvement in both on-topic rate and long-dwell metrics
- Enhanced ability to handle complex natural language queries
- Significant boost in sitewide engagement
This advancement enables LinkedIn to better serve complex queries like "how to ask for a raise?" while maintaining high performance at scale. The system intelligently balances between exact keyword matching and semantic understanding, ensuring optimal results for both navigational and conceptual searches.
What impresses me most is how the team solved the scale challenge - processing billions of posts efficiently using pre-computed embeddings and approximate nearest neighbor search. This is enterprise-scale AI at its finest.
Excited to share insights about LinkedIn's innovative approach to content search, recently detailed in a groundbreaking paper by their Mountain View team. This advancement represents a significant shift from traditional keyword-based search to semantic understanding.
>> Technical Architecture
The new search engine employs a sophisticated two-layer architecture:
Retrieval Layer
- Token Based Retriever (TBR) for exact keyword matching
- Embedding Based Retriever (EBR) using a two-tower model with multilingual-e5 embeddings
- Pre-computed post embeddings stored in a dedicated embedding store for efficient retrieval
Multi-Stage Ranking
- L1 Stage: Initial filtering using a lightweight model
- L2 Stage: Advanced ranking with complex features including:
- Query-post semantic matching
- Author reputation analysis
- User engagement metrics
- Content freshness evaluation
>> Performance Improvements
The system has achieved remarkable results:
- 10%+ improvement in both on-topic rate and long-dwell metrics
- Enhanced ability to handle complex natural language queries
- Significant boost in sitewide engagement
This advancement enables LinkedIn to better serve complex queries like "how to ask for a raise?" while maintaining high performance at scale. The system intelligently balances between exact keyword matching and semantic understanding, ensuring optimal results for both navigational and conceptual searches.
What impresses me most is how the team solved the scale challenge - processing billions of posts efficiently using pre-computed embeddings and approximate nearest neighbor search. This is enterprise-scale AI at its finest.
upvoted
a
collection
1 day ago
liked
a
Space
2 days ago
Dataset Viewer issue: TooBigContentError
1
#2 opened 3 days ago
by
safikhan
liked
a
dataset
3 days ago
Dataset Viewer issue: TransformRowsProcessingError
3
#3 opened 7 months ago
by
ellisbrown
updated
2
datasets
4 days ago
Re-upload as multiple independant TAR files instead of one big TAR file split into chunks
2
#1 opened 5 days ago
by
lhoestq
updated
a
dataset
6 days ago