Nomic Embed Multimodal
Collection
Multimodal models allowing you to search over interleaved text, PDFs, charts, and images!
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16 items
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Updated
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20
Nomic AI
Enterprise
company
Verified
AI & ML interests
embeddings, graph statistics, nlp
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Where is the corpus of this dataset?
4
#1 opened about 16 hours ago
by
Haon-Chen
zpn
updated
a
collection
about 4 hours ago
zpn
published
a
dataset
about 4 hours ago
Could not locate the configuration_hf_nomic_bert.py inside nomic-ai/nomic-bert-2048. Error
9
#25 opened 12 months ago
by
Usersh
Cebtenzzre
updated
a
model
20 days ago
Tokenizer
1
#1 opened 25 days ago
by
Zeus0809
Unable to use these with llama.cpp
1
#1 opened about 1 month ago
by
circadesu
Congratulations on being SOTA in The Arabic RAG Leaderboard
1
#22 opened about 1 month ago
by
MohamedRashad
Cebtenzzre
published
a
model
about 1 month ago
Cebtenzzre
updated
a
model
about 1 month ago
Quants?
2
#9 opened 3 months ago
by
SuperPauly
zpn
updated
a
collection
about 1 month ago
Post
3434
I just released Sentence Transformers v4.1; featuring ONNX and OpenVINO backends for rerankers offering 2-3x speedups and improved hard negatives mining which helps prepare stronger training datasets. Details:
🏎️ ONNX, OpenVINO, Optimization, Quantization
- I've added ONNX and OpenVINO support with just one extra argument: "backend" when loading the CrossEncoder reranker, e.g.:
- The
- I've uploaded ~340 ONNX & OpenVINO models for all existing models under the cross-encoder Hugging Face organization. You can use these without having to export when loading.
⛏ Improved Hard Negatives Mining
- Added 'absolute_margin' and 'relative_margin' arguments to
-
-
- Inspired by the excellent NV-Retriever paper from NVIDIA.
And several other small improvements. Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v4.1.0
With this release, I introduce near-feature parity between the SentenceTransformer embedding & CrossEncoder reranker models, which I've wanted to do for quite some time! With rerankers very strongly supported now, it's time to look forward to other useful architectures!
🏎️ ONNX, OpenVINO, Optimization, Quantization
- I've added ONNX and OpenVINO support with just one extra argument: "backend" when loading the CrossEncoder reranker, e.g.:
CrossEncoder("cross-encoder/ms-marco-MiniLM-L6-v2", backend="onnx")- The
export_optimized_onnx_model, export_dynamic_quantized_onnx_model, and export_static_quantized_openvino_model functions now work with CrossEncoder rerankers, allowing you to optimize (e.g. fusions, gelu approximations, etc.) or quantize (int8 weights) rerankers.- I've uploaded ~340 ONNX & OpenVINO models for all existing models under the cross-encoder Hugging Face organization. You can use these without having to export when loading.
⛏ Improved Hard Negatives Mining
- Added 'absolute_margin' and 'relative_margin' arguments to
mine_hard_negatives. -
absolute_margin ensures that sim(query, negative) < sim(query, positive) - absolute_margin, i.e. an absolute margin between the negative & positive similarities.-
relative_margin ensures that sim(query, negative) < sim(query, positive) * (1 - relative_margin), i.e. a relative margin between the negative & positive similarities.- Inspired by the excellent NV-Retriever paper from NVIDIA.
And several other small improvements. Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v4.1.0
With this release, I introduce near-feature parity between the SentenceTransformer embedding & CrossEncoder reranker models, which I've wanted to do for quite some time! With rerankers very strongly supported now, it's time to look forward to other useful architectures!
tarsur909
authored
4
papers
2 months ago
IterGen: Iterative Structured LLM Generation
Paper
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2410.07295
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Published
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1
Tamper-Resistant Safeguards for Open-Weight LLMs
Paper
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2408.00761
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Published
CoRNStack: High-Quality Contrastive Data for Better Code Ranking
Paper
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2412.01007
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Published
CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis
Paper
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2503.23145
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Published
•
34
bstadt
authored
2
papers
2 months ago
Post
2592
‼️Sentence Transformers v4.0 is out! You can now train and finetune reranker models with multi-GPU training, bf16 support, loss logging, callbacks & much more. I also prove that finetuning on your domain helps much more than you might think.
1️⃣ Reranker Training Refactor
Reranker models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-reranker
Notably, the release is fully backwards compatible: all deprecations are soft, meaning that they still work but emit a warning informing you how to upgrade.
2️⃣ New Reranker Losses
- 11 new losses:
- 2 traditional losses: BinaryCrossEntropy and CrossEntropy
- 2 distillation losses: MSE and MarginMSE
- 2 in-batch negatives losses: MNRL (a.k.a. InfoNCE) and CMNRL
- 5 learning to rank losses: Lambda, p-ListMLE, ListNet, RankNet, ListMLE
3️⃣ New Reranker Documentation
- New Training Overview, Loss Overview, API Reference docs
- 5 new, 1 refactored training examples docs pages
- 13 new, 6 refactored training scripts
- Migration guides (2.x -> 3.x, 3.x -> 4.x)
4️⃣ Blogpost
Alongside the release, I've written a blogpost where I finetune ModernBERT on a generic question-answer dataset. My finetunes easily outperform all general-purpose reranker models, even models 4x as big. Finetuning on your domain is definitely worth it: https://huggingface.co/blog/train-reranker
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v4.0.1
1️⃣ Reranker Training Refactor
Reranker models can now be trained using an extensive trainer with a lot of powerful features:
- MultiGPU Training (Data Parallelism (DP) and Distributed Data Parallelism (DDP))
- bf16 training support; loss logging
- Evaluation datasets + evaluation loss
- Improved callback support + an excellent Weights & Biases integration
- Gradient checkpointing, gradient accumulation
- Model card generation
- Resuming from a training checkpoint without performance loss
- Hyperparameter Optimization
and much more!
Read my detailed blogpost to learn about the components that make up this new training approach: https://huggingface.co/blog/train-reranker
Notably, the release is fully backwards compatible: all deprecations are soft, meaning that they still work but emit a warning informing you how to upgrade.
2️⃣ New Reranker Losses
- 11 new losses:
- 2 traditional losses: BinaryCrossEntropy and CrossEntropy
- 2 distillation losses: MSE and MarginMSE
- 2 in-batch negatives losses: MNRL (a.k.a. InfoNCE) and CMNRL
- 5 learning to rank losses: Lambda, p-ListMLE, ListNet, RankNet, ListMLE
3️⃣ New Reranker Documentation
- New Training Overview, Loss Overview, API Reference docs
- 5 new, 1 refactored training examples docs pages
- 13 new, 6 refactored training scripts
- Migration guides (2.x -> 3.x, 3.x -> 4.x)
4️⃣ Blogpost
Alongside the release, I've written a blogpost where I finetune ModernBERT on a generic question-answer dataset. My finetunes easily outperform all general-purpose reranker models, even models 4x as big. Finetuning on your domain is definitely worth it: https://huggingface.co/blog/train-reranker
See the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/v4.0.1