14 models · ranked by HuggingFace downloads
by sentence-transformers
Distilled BERT model that encodes sentences into 384-dimensional vectors for measuring semantic similarity. Trained on over a billion sentence pairs spanning scientific papers, web QA, NLI datasets, and community forums. At 22M parameters and 6 transformer layers, it is fast enough for CPU inference while remaining competitive on standard sentence similarity benchmarks.
by sentence-transformers
Multilingual sentence embedding model covering 50+ languages, built on a 12-layer distilled MiniLM architecture. Produces 384-dimensional vectors designed for semantic similarity and paraphrase detection across language boundaries. Trained on multilingual paraphrase data to align semantically equivalent sentences even when expressed in different languages.
by BAAI
BAAI's BGE-M3 embedding model supporting over 100 languages with a unified architecture capable of dense, sparse (lexical), and late-interaction (ColBERT-style) retrieval modes from a single checkpoint. Built on XLM-RoBERTa with large-scale multilingual training, it targets multi-lingual and cross-lingual retrieval where a single model must handle diverse language inputs.
by sentence-transformers
Sentence embedding model based on the MPNet architecture, producing 768-dimensional vectors. Trained on over a billion sentence pairs from MS MARCO, NLI datasets, and community QA forums, it is frequently used when accuracy matters more than inference speed among English embedding models. The MPNet backbone enables masked and permuted prediction during pre-training for stronger representations.
by nomic-ai
Nomic Embed Text v1.5 is a matryoshka-capable English embedding model from Nomic AI, built on a custom nomic-BERT architecture trained with contrastive learning on large-scale text pairs. Matryoshka Representation Learning allows truncating embeddings to shorter dimensions (e.g. 64, 128, 256) without retraining, enabling flexible precision-cost tradeoffs. The model is transformers.js-compatible for browser-side inference.
by intfloat
Multilingual-E5-Small is a compact multilingual embedding model from Microsoft Research supporting 100+ languages on a BERT-based backbone, smaller and faster than the E5-large variant. It uses the same instruction-prefix training approach as E5-large ('query:'/'passage:') for asymmetric retrieval. MIT licensed with ONNX and OpenVINO export.
by sentence-transformers
A 12-layer sentence encoder producing 384-dimensional embeddings, offering a quality step up from all-MiniLM-L6-v2 at roughly 2x the inference cost. Fine-tuned on a billion sentence pairs using contrastive objectives for semantic similarity and retrieval.
by sentence-transformers
DistilRoBERTa fine-tuned as a sentence encoder on over 1 billion sentence pairs, producing 768-dimensional embeddings. Offers a balance between the speed of DistilBERT and the richer representations of full RoBERTa.
by Alibaba-NLP
GTE-multilingual-base is Alibaba's 305M-parameter embedding model covering 70+ languages, designed for multilingual dense retrieval and semantic similarity. It uses a modified transformer backbone with improved positional encoding for cross-lingual transfer.
by TaylorAI
BGE-Micro-v2 is a heavily distilled BERT embedding model targeting near-zero latency sentence encoding with acceptable MTEB scores. Extremely small footprint allows embedding generation in CPU-only or mobile environments. MIT-licensed with ONNX and transformers.js support.
by Alibaba-NLP
GTE-Qwen2-1.5B-instruct is Alibaba's embedding model built on a 1.5B Qwen2 decoder backbone with instruction fine-tuning for text retrieval. It significantly outperforms encoder-only models its size on MTEB by leveraging the Qwen2 language model's broader world knowledge.
by cl-nagoya
Ruri v3 (310M) is Nagoya University's Japanese text embedding model built on the ModernBERT architecture, optimised for semantic similarity and retrieval in Japanese. It is part of the Ruri series, which targets Japanese-specific sentence embedding quality. The v3 310M variant balances embedding dimension, retrieval quality, and inference speed for production Japanese NLP pipelines.
by LazarusNLP
all-indo-e5-small is LazarusNLP's Indonesian fine-tune of a small e5 embedding model, designed to improve semantic search and sentence similarity quality on Bahasa Indonesia text. v4 reflects iterative improvements over previous Indonesian embedding baselines.
by cisco-ai
The cross-encoder companion to SecureBERT2.0-biencoder, designed for reranking in cybersecurity retrieval pipelines. Cross-encoders jointly encode query and document pairs, making them more accurate but slower than biencoder retrieval for re-scoring top candidates.