snowflake-arctic-embed-l-v2.0

Model · Sentence similarity

ateneo-bge-m3-ecuador-v2

Jorge Doicela

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Evaluated with InformationRetrievalEvaluator Approximate statistics based on the first 100 samples: - perdevicetrainbatchsize: 32 - numtrainepochs: 5 - learningrate: 2e-05 - lrschedulertype: cosine - warmupsteps: 0.1 - optim: adamwtorch - weightdecay: 0.01 - bf16: True - perdeviceevalbatchsize: 32 - loadbestmodelatend: True…

Model · Sentence similarity

AX-Qwen3-Embedding-0.6B-CUDA-AXQ-NVFP4-W4A4

AutomatosX

AXQuant CUDA NVFP4 W4A4 mixed precision development preview. Converted from the original upstream BF16 source, without AWQ. Native E2M1 FP4 weights and inputs use per-16 E4M3FN scales and FP32 global scales. This is a retrieval embedding checkpoint, with no MTP or generative claim. All attention projections and the first/last two MLP blocks retain original BF16. Remaining MLP matrices use NVFP4 W4A4. Saved query instruction, last-token pooling, exactly one (151643), and L2 normalization. The chat (151645) is not appended. Factory conversion uses AXQuant's NumPy reference RTN encoder, independently checked against preserved BF16 source tensors. Source and pooling assets, calibration and…

Model · Sentence similarity

nomic-embed-text-v2-moe

Nomic AI

This model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models. nomic-embed-text-v2-moe is a SoTA multilingual MoE text embedding model that excels at multilingual retrieval: Transformer-based text embedding models have improved their performance on benchmarks like MIRACL and BEIR by increasing their parameter counts. However, this scaling approach introduces significant deployment challenges, including increased inference latency and memory usage. These challenges are particularly severe in retrieval-augmented generation (RAG) applications, where large models' increased memory requirements constrain dataset ingestion capacity, and their higher latency…

Model · Sentence similarity

LaBSE

Sentence Transformers

This is a port of the LaBSE model to PyTorch. It can be used to map 109 languages to a shared vector space. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Have a look at LaBSE for the respective publication that describes LaBSE.

Model · Sentence similarity

semantic-lite-2

Zulfah

Semantic-Lite-2 is a lightweight multilingual sentence embedding model that produces 256-dimensional semantic vectors. It is designed for semantic search, sentence similarity, clustering, retrieval, and retrieval-augmented generation (RAG) tasks. The model is built on top of the Spark-X2.5-1.7B backbone using a frozen-backbone plus trainable-projection-head approach. The 256-dimensional output keeps vector storage compact while preserving strong retrieval quality. Vectors are L2-normalized, so cosine similarity is computed as a simple dot product. Evaluated on 500 Indonesian NLI evaluation pairs (retrieval task, chance level 0.2%): Cross-lingual evaluation (10 languages, 20 pairs per…

Model · Sentence similarity

gte-large-en-v1.5

Alibaba-NLP

We introduce gte-v1.5 series, upgraded gte embeddings that support the context length of up to 8192, while further enhancing model performance. The models are built upon the transformer++ encoder backbone (BERT + RoPE + GLU). The gte-v1.5 series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation). We also present the gte-Qwen1.5-7B-instruct, a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB. Models for Multilingual Text Retrieval](https://arxiv.org/pdf/2407.19669) Use the code below to get started with the model. It is…