deepseek-ai/DeepSeek-V3
Updated
ā¢
71.7k
ā¢
1.29k
Will Brooks
TornButter
AI & ML interests
None yet
Recent Activity
liked
a model
4 days ago
deepseek-ai/DeepSeek-V3
liked
a model
11 days ago
Qwen/QVQ-72B-Preview
liked
a model
11 days ago
answerdotai/ModernBERT-base
Organizations
None yet
TornButter's activity
liked
a
model
4 days ago
liked
3
models
11 days ago
reacted to
singhsidhukuldeep's
post with š„
13 days ago
Post
2173
Exciting News in AI: JinaAI Releases JINA-CLIP-v2!
The team at Jina AI has just released a groundbreaking multilingual multimodal embedding model that's pushing the boundaries of text-image understanding. Here's why this is a big deal:
š Technical Highlights:
- Dual encoder architecture combining a 561M parameter Jina XLM-RoBERTa text encoder and a 304M parameter EVA02-L14 vision encoder
- Supports 89 languages with 8,192 token context length
- Processes images up to 512Ć512 pixels with 14Ć14 patch size
- Implements FlashAttention2 for text and xFormers for vision processing
- Uses Matryoshka Representation Learning for efficient vector storage
ā”ļø Under The Hood:
- Multi-stage training process with progressive resolution scaling (224ā384ā512)
- Contrastive learning using InfoNCE loss in both directions
- Trained on massive multilingual dataset including 400M English and 400M multilingual image-caption pairs
- Incorporates specialized datasets for document understanding, scientific graphs, and infographics
- Uses hard negative mining with 7 negatives per positive sample
š Performance:
- Outperforms previous models on visual document retrieval (52.65% nDCG@5)
- Achieves 89.73% image-to-text and 79.09% text-to-image retrieval on CLIP benchmark
- Strong multilingual performance across 30 languages
- Maintains performance even with 75% dimension reduction (256D vs 1024D)
šÆ Key Innovation:
The model solves the long-standing challenge of unifying text-only and multi-modal retrieval systems while adding robust multilingual support. Perfect for building cross-lingual visual search systems!
Kudos to the research team at Jina AI for this impressive advancement in multimodal AI!
The team at Jina AI has just released a groundbreaking multilingual multimodal embedding model that's pushing the boundaries of text-image understanding. Here's why this is a big deal:
š Technical Highlights:
- Dual encoder architecture combining a 561M parameter Jina XLM-RoBERTa text encoder and a 304M parameter EVA02-L14 vision encoder
- Supports 89 languages with 8,192 token context length
- Processes images up to 512Ć512 pixels with 14Ć14 patch size
- Implements FlashAttention2 for text and xFormers for vision processing
- Uses Matryoshka Representation Learning for efficient vector storage
ā”ļø Under The Hood:
- Multi-stage training process with progressive resolution scaling (224ā384ā512)
- Contrastive learning using InfoNCE loss in both directions
- Trained on massive multilingual dataset including 400M English and 400M multilingual image-caption pairs
- Incorporates specialized datasets for document understanding, scientific graphs, and infographics
- Uses hard negative mining with 7 negatives per positive sample
š Performance:
- Outperforms previous models on visual document retrieval (52.65% nDCG@5)
- Achieves 89.73% image-to-text and 79.09% text-to-image retrieval on CLIP benchmark
- Strong multilingual performance across 30 languages
- Maintains performance even with 75% dimension reduction (256D vs 1024D)
šÆ Key Innovation:
The model solves the long-standing challenge of unifying text-only and multi-modal retrieval systems while adding robust multilingual support. Perfect for building cross-lingual visual search systems!
Kudos to the research team at Jina AI for this impressive advancement in multimodal AI!
liked
a
Space
17 days ago
liked
a
model
23 days ago
liked
a
model
26 days ago
liked
a
Space
28 days ago
liked
2
models
about 1 month ago
liked
a
Space
about 1 month ago
liked
3
models
about 1 month ago
liked
a
Space
about 1 month ago
liked
2
models
about 1 month ago
liked
a
Space
about 2 months ago
liked
a
model
about 2 months ago
