nomic-ai/nomic-embed-text-v1.5
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AI & ML interests
embeddings, graph statistics, nlp
Recent Activity
andriym
updated
a
model
about 15 hours ago
Post
2391
‼️Sentence Transformers v5.0 is out! The biggest update yet introduces Sparse Embedding models, encode methods improvements, Router module for asymmetric models & much more. Sparse + Dense = 🔥 hybrid search performance! Details:
1️⃣ Sparse Encoder Models
Brand new support for sparse embedding models that generate high-dimensional embeddings (30,000+ dims) where <1% are non-zero:
- Full SPLADE, Inference-free SPLADE, and CSR architecture support
- 4 new modules, 12 new losses, 9 new evaluators
- Integration with @elastic-co , @opensearch-project , @NAVER LABS Europe, @qdrant , @IBM , etc.
- Decode interpretable embeddings to understand token importance
- Hybrid search integration to get the best of both worlds
2️⃣ Enhanced Encode Methods & Multi-Processing
- Introduce encode_query & encode_document automatically use predefined prompts
- No more manual pool management - just pass device list directly to encode()
- Much cleaner and easier to use than the old multi-process approach
3️⃣ Router Module & Advanced Training
- Router module with different processing paths for queries vs documents
- Custom learning rates for different parameter groups
- Composite loss logging - see individual loss components
- Perfect for two-tower architectures
4️⃣ Comprehensive Documentation & Training
- New Training Overview, Loss Overview, API Reference docs
- 6 new training example documentation pages
- Full integration examples with major search engines
- Extensive blogpost on training sparse models
Read the comprehensive blogpost about training sparse embedding models: https://huggingface.co/blog/train-sparse-encoder
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v5.0.0
What's next? We would love to hear from the community! What sparse encoder models would you like to see? And what new capabilities should Sentence Transformers handle - multimodal embeddings, late interaction models, or something else? Your feedback shapes our roadmap!
1️⃣ Sparse Encoder Models
Brand new support for sparse embedding models that generate high-dimensional embeddings (30,000+ dims) where <1% are non-zero:
- Full SPLADE, Inference-free SPLADE, and CSR architecture support
- 4 new modules, 12 new losses, 9 new evaluators
- Integration with @elastic-co , @opensearch-project , @NAVER LABS Europe, @qdrant , @IBM , etc.
- Decode interpretable embeddings to understand token importance
- Hybrid search integration to get the best of both worlds
2️⃣ Enhanced Encode Methods & Multi-Processing
- Introduce encode_query & encode_document automatically use predefined prompts
- No more manual pool management - just pass device list directly to encode()
- Much cleaner and easier to use than the old multi-process approach
3️⃣ Router Module & Advanced Training
- Router module with different processing paths for queries vs documents
- Custom learning rates for different parameter groups
- Composite loss logging - see individual loss components
- Perfect for two-tower architectures
4️⃣ Comprehensive Documentation & Training
- New Training Overview, Loss Overview, API Reference docs
- 6 new training example documentation pages
- Full integration examples with major search engines
- Extensive blogpost on training sparse models
Read the comprehensive blogpost about training sparse embedding models: https://huggingface.co/blog/train-sparse-encoder
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v5.0.0
What's next? We would love to hear from the community! What sparse encoder models would you like to see? And what new capabilities should Sentence Transformers handle - multimodal embeddings, late interaction models, or something else? Your feedback shapes our roadmap!
tarsur909
updated
2
models
18 days ago
zpn
published
a
dataset
about 1 month ago
zpn
updated
a
dataset
about 1 month ago
tarsur909
authored
5
papers
about 1 month ago
VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation
Paper
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2504.15659
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Published
SweRank: Software Issue Localization with Code Ranking
Paper
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2505.07849
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Published
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10
Improving Assembly Code Performance with Large Language Models via Reinforcement Learning
Paper
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2505.11480
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Published
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8
SATBench: Benchmarking LLMs' Logical Reasoning via Automated Puzzle Generation from SAT Formulas
Paper
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2505.14615
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Published
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1
DINGO: Constrained Inference for Diffusion LLMs
Paper
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2505.23061
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Published
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30
Where is the corpus of this dataset?
5
#1 opened about 1 month ago
by
Haon-Chen
zpn
updated
a
collection
about 1 month ago
zpn
published
a
dataset
about 1 month ago
Could not locate the configuration_hf_nomic_bert.py inside nomic-ai/nomic-bert-2048. Error
9
#25 opened about 1 year ago
by
Usersh
Cebtenzzre
updated
a
model
about 2 months ago
Tokenizer
1
#1 opened 2 months ago
by
Zeus0809
Unable to use these with llama.cpp
1
#1 opened 2 months ago
by
circadesu
Congratulations on being SOTA in The Arabic RAG Leaderboard
1
#22 opened 2 months ago
by
MohamedRashad