bge-m3

Model · Sentence similarity

bge-small-en-v1.5-onnx-Q

Qdrant

Quantized ONNX port of BAAI/bge-small-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.

Model · Sentence similarity

all-MiniLM-L6-v2-onnx

Qdrant

ONNX port of sentence-transformers/all-MiniLM-L6-v2 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.

Model · Sentence similarity

bm25

Qdrant

Repository with files to perform BM25 searches with FastEmbed. BM25 (Best Matching 25) is a ranking function used by search engines to estimate the relevance of documents to a given search query. This model is supposed to be used with Qdrant. Vectors have to be configured with Modifier.IDF. Here's an example of BM25 with FastEmbed.

Model · Sentence similarity

Splade_PP_en_v1

Qdrant

ONNX port of prithivida/SpladePPenv1 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.

Model · Sentence similarity

embeddinggemma-300m

LiteRT Community (FKA TFLite)

This model card provides a few variants of the EmbeddingGemma model that are ready for deployment on Android and iOS using LiteRT, or on Android via the Google AI Edge RAG Library. Try out the demo example on GitHub. Try out the EmbeddingGemma model in the in the Google AI Edge RAG Library. You can find the SDK on GitHub or follow our Android guide to install directly from Maven. We have also published a sample app. Use the sentencepiece model as the tokenizer for the EmbeddingGemma model. Note that all benchmark stats are from a Samsung S25 Ultra. Mixed Precision refers to per-channel quantization with int4 for embeddings, feedforward, and projection layers, and int8 for attention…

ViDense is a VietNamese Embedding Model. Fine-tuned and enhanced with tailored methods, ViDense incorporates advanced techniques to optimize performance for text embeddings in various applications. Trained for 10 epochs with a train batch size of 2048. Utilizes a 3-phase training approach, where the best checkpoint from each phase serves as the base model for the next. Momentum Encoder: Incorporates MoCo (Momentum Contrast) to enhance in-batch negative sampling. of positives as relevant to the anchor, it reranks the corpus using the Spearman metric and integrates Spearman weights into the loss calculation for improved ranking. Below is a comparision table of the results I achieved compared…