e5-base-v2

News (May 2023): please switch to e5-base-v2, which has better performance and same method of usage. Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei, arXiv 2022 This model has 12 layers and the embedding size is 768. Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. Please refer to our paper at https://arxiv.org/pdf/2212.03533.pdf. Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark. Below is an example for usage with sentencetransformers. Package requirements pip install sentencetransformers~=2.2.2 1. Do I need to add the prefix "query: " and "passage: " to…

Model · Sentence similarity

bge-base-en-v1.5-course-recommender-v5

Data Society

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Approximate statistics based on the first 45 samples: Approximate statistics based on the first 5 samples: - evalstrategy: steps - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - learningrate: 3e-06 - maxsteps: 24 - warmupratio: 0.1 - batchsampler: noduplicates - overwriteoutputdir: False - dopredict: False…

Model · Sentence similarity

pubmedbert-base-embeddings

NeuML

This is a PubMedBERT-base model fined-tuned using sentence-transformers. It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The training dataset was generated using a random sample of PubMed title-abstract pairs along with similar title pairs. PubMedBERT Embeddings produces higher quality embeddings than generalized models for medical literature. Further fine-tuning for a medical subdomain will result in even better performance. This model can be used to build embeddings databases with txtai for semantic search and/or as a knowledge source for retrieval augmented generation (RAG). Alternatively, the model can…

Maps a VA National Drug File (VANDF) drug string to the RxNorm clinical drug confidence so you can auto-accept the sure cases and route the rest to review. Published tools such as RxMap normalize to the ingredient level (IN/MIN). This model targets the full clinical drug, where a miss on strength or dose form is a wrong answer. Method, hypotheses, the full sweep, and the error Mapper loads the encoder, trainconfig.json (input preprocessing), calibration.json (score → probability), and candidates.parquet (the 27,287 active RxNorm SCD/SBD names it searches) from this repo. The default acceptance threshold (0.92) was chosen on validation for 99% precision on a population that includes real…

The recipe of kvenanzi/vandf-rxnorm-biencoder, trained on all 14,369 VA National Drug File (VANDF) strings that have a clinical drug in RxNorm, so that every ingredient family in the file is in training. It maps a VANDF drug string to the RxNorm clinical drug it names (SCD or SBD: ingredient, strength, and dose form), with a calibrated confidence for routing uncertain strings to review. This model has no test set of its own: every VA string was used for training. Its expected accuracy comes from seven-fold cross-validation of the recipe, and a comparison with the first model on strings neither model trained on (below). Write-up: https://withinnoise.dev/blog/posts/vandf-rxnorm-interventions/…

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased. It maps inputs to a 768-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. Approximate statistics based on the first 100 samples: - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - multidatasetbatchsampler: roundrobin - dopredict: False - predictionlossonly: True - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - gradientaccumulationsteps: 1 - evalaccumulationsteps: None…