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. 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. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net
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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. This is the microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext model which has been fine-tuned over the MS-MARCO dataset using sentence-transformers framework. It can be used for the information retrieval task in the medical/health text domain. Using this model becomes easy when you have sentence-transformers installed:
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This work stands on the shoulders of 2 robust researches: Naver's From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective paper and Google's SparseEmbed. Props to both the teams for such a robust work. **Beginner ?** expand this. **Expert in Sparse & Dense representations ?** feel free skip to next section 2, Lexical search with BOW based sparse vectors are strong baselines, but they famously suffer from vocabulary mismatch problem, as they can only do exact term matching. Here are the pros and cons:
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This work stands on the shoulders of 2 robust researches: Naver's From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective paper and Google's SparseEmbed. Props to both the teams for such a robust work. **Beginner ?** expand this. **Expert in Sparse & Dense representations ?** feel free skip to next section 2, Lexical search with BOW based sparse vectors are strong baselines, but they famously suffer from vocabulary mismatch problem, as they can only do exact term matching. Here are the pros and cons:
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Quantized ONNX port of BAAI/bge-base-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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ONNX port of BAAI/bge-large-en-v1.5 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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It creates 4-dimensional embeddings for each word stem, capturing the word's meaning. These meaning embeddings are combined into a bag-of-words (BoW) representation of the input text. The final sparse representation is calculated by weighting each word using the BM25 scoring formula. In the case of a word's absence in the miniCOIL vocabulary, word weight in sparse representation is purely based on the BM25 score. > Note: This model was designed with Qdrant's specifics in mind; miniCOIL sparse vectors in Qdrant have to be configured with Modifier.IDF. Otherwise, you'll have to personally calculate & scale the produced sparse representations by the IDF part of the BM25 formula.
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Quantized ONNX port of sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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This repository contains an ONNX export of `Qwen/Qwen3-Embedding-0.6B`, converted by Qdrant for inference with FastEmbed. It encodes text into 1024-dimensional vectors, supports over 100 languages, and accepts up to 32768 input tokens. Embeddings are produced by taking the hidden state of the last non-padding token and L2-normalizing it, so the similarity score is the dot product of two embeddings. The quantized file stores its weights as 8-bit `MatMulNBits` and therefore requires `onnxruntime>=1.23`. Earlier versions only implement the 4-bit kernel and will fail to load it.
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ONNX port of prithivida/Splade_PP_en_v1 for text classification and similarity searches. Here's an example of performing inference using the model with FastEmbed.
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