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

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…

Model · Sentence similarity

simcse-bert-uncased-unsup

Russel Ku

Unsupervised SimCSE sentence encoder trained from bert-base-uncased on a 100k-record subset of SNLI, as a course replication of Gao, Yao & Chen (EMNLP 2021). It maps an English sentence to a 768-dimensional vector; compare sentences with cosine similarity. Team (Universidad Politécnica de Yucatán, U2T02): Acosta Castellanos Bianca Alexandra, Canche Chuc Angel Rivaldo, Ku Aguilar Russel, Sanchez Novelo Damian, Velasco Martin Jonathan Abisai. 165,528 unique sentences (premises and hypotheses, whitespace-stripped) from snlitrain100k.jsonl, a 100k-record subset of the SNLI train split. Labels are not used: each sentence is its own positive, the two views differ only by dropout. The paper…

Unsupervised SimCSE (Gao, Yao & Chen, EMNLP 2021, Eq. 1) on top of bert-base-uncased, trained on sentences from a 100k-record SNLI subset as a course assignment (U2T02, Trends in Data Science). It maps a sentence to a 768-dimensional, L2-normalized vector; compare vectors with cosine similarity. Team: Julio Cesar De Aquino Castellanos, Gustavo Alexander Fuentes Marin, Valeria Nicol Hernandez Leon, Ricardo Daniel Horta Sanchez, Jose Angel Pech Xool, Lorena Danae Perez Lopez. This is a reproduction under a reduced budget, not the paper's released checkpoint: it is 8.5 Spearman points below the paper on STS-B test (see Limitations). The supervised companion model is…