lt-kn-en_familyname-linkage

Model · Sentence similarity

bge-m3

Beijing Academy of Artificial Intelligence

For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity. Some suggestions for retrieval pipeline in RAG We recommend to use the following pipeline: hybrid retrieval + re-ranking. - Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities. Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval. This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings. To…

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

paraphrase-albert-small-v2

Sentence Transformers

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. This model was trained by sentence-transformers. If you find this model helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks