# bge-base-en-v1.5-course-recommender-v5 by Data Society Source: https://savrn.com/models/bge-base-en-v1-5-course-recommender-v5 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve bge-base-en-v1.5-course-recommender-v5 (109M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.2 GB | 0.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 23, 2026. [bge-base-en-v1.5-course-recommender-v5 on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/bge-base-en-v1-5-course-recommender-v5/gpus) ## SAVRN's Notes on bge-base-en-v1.5-course-recommender-v5 The name is the use case: Data Society's fine-tune of BAAI/bge-base-en-v1.5 aimed at course recommendation, turning each input into a 768-dimensional vector for similarity search. The arithmetic is trivial — 109M parameters, 0.3 GB at 16-bit, 0.1 GB at 4-bit, a 438 MB download — so the one MI300X the Index lists at $1.85 an hour is wasted on it alone; run it beside your serving stack. The 512-token context still applies, so long course descriptions get chunked before embedding. Treat the license as the gating item: the record lists none for this fine-tune, which makes commercial use a conversation with the publisher, not a checkbox. The disclosed training is a light touch — 24 steps at batch size 16, learning rate 3e-06 — so validate the delta against the base on your own catalog before shipping; a fine-tune this small can be a wash. Published January 2025 under sentence-transformers, derived from bge-base-en-v1.5, and drawing 4.1M downloads a month. SAVRN Research ## Model Card 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… Excerpt from the card by Data Society. ## Configuration Architecture BertModel Context length (tokens) 512 Layers 12 Hidden size 768 Feed-forward size 3,072 Attention heads 12 Vocabulary size 30,522 Stored precision float32 Model type bert ## Identity and Version Repository datasocietyco/bge-base-en-v1.5-course-recommender-v5 Publisher Data Society Task Sentence similarity Modality Text Library sentence-transformers Parameters 109M parameters Languages Not stated by the source Revision 2b069eed51ce7bc2beeaa7e3eea1a01c48ef3d29 First published 2025-01-09 Last updated 2025-01-09 ## Files and Weights 12 files, 438.9 MB in total. The weights are 1 file totalling 438.0 MB in safetensors. Weights1 file · 438.0 MB Configuration6 files · 2.3 KB Tokenizer3 files · 944.4 KB Documentation1 file · 33.0 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 438.0 MB | ee42e89c1cb2 | | 1_Pooling/config.json | Configuration | 296 B | — | | config.json | Configuration | 740 B | — | | config_sentence_transformers.json | Configuration | 195 B | — | | modules.json | Configuration | 349 B | — | | sentence_bert_config.json | Configuration | 52 B | — | | special_tokens_map.json | Configuration | 695 B | — | | README.md | Documentation | 33.0 KB | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 711.6 KB | — | | tokenizer_config.json | Tokenizer | 1.2 KB | — | | vocab.txt | Tokenizer | 231.5 KB | — | ## License and Download License Not stated by the source Access Open weights, no gate Download size 438.0 MB [Download from Data Society](https://huggingface.co/datasocietyco/bge-base-en-v1.5-course-recommender-v5) Released by Data Society through its official repository on Hugging Face. ## Built From - Derived from [BAAI/bge-base-en-v1.5](https://savrn.com/models/bge-base-en-v1-5) - Described by arXiv:1705.00652 - Described by [arXiv:1908.10084](https://savrn.com/papers/sentence-bert-sentence-embeddings-using-siamese-bert-networks) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 438.0 MB | | 16-bit | 0.2 GB | | 8-bit | 0.1 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Compare bge-base-en-v1.5-course-recommender-v5 - [bge-base-en-v1.5-course-recommender-v5 vs e5-base](https://savrn.com/models/compare/bge-base-en-v1-5-course-recommender-v5-vs-e5-base) - [bge-base-en-v1.5-course-recommender-v5 vs e5-base-v2](https://savrn.com/models/compare/bge-base-en-v1-5-course-recommender-v5-vs-e5-base-v2) - [bge-base-en-v1.5-course-recommender-v5 vs pubmedbert-base-embeddings](https://savrn.com/models/compare/bge-base-en-v1-5-course-recommender-v5-vs-pubmedbert-base-embeddings) ## Questions About bge-base-en-v1.5-course-recommender-v5 ### How much GPU memory does bge-base-en-v1.5-course-recommender-v5 need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (109M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run bge-base-en-v1.5-course-recommender-v5 on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### What is bge-base-en-v1.5-course-recommender-v5's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Sentence similarity ### [pubmedbert-base-embeddings](https://savrn.com/models/pubmedbert-base-embeddings) [NeuML](https://savrn.com/model-publishers/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… Open weights apache-2.0 109M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/pubmedbert-base-embeddings) Model · Sentence similarity ### [vandf-rxnorm-biencoder](https://savrn.com/models/vandf-rxnorm-biencoder) [Kevin Venanzi](https://savrn.com/model-publishers/kvenanzi) 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… Open weights apache-2.0 109M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/vandf-rxnorm-biencoder) Model · Sentence similarity ### [vandf-rxnorm-biencoder-all](https://savrn.com/models/vandf-rxnorm-biencoder-all) [Kevin Venanzi](https://savrn.com/model-publishers/kvenanzi) 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/… Open weights apache-2.0 109M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/vandf-rxnorm-biencoder-all) Model · Sentence similarity ### [finetuned-medical-bert](https://savrn.com/models/finetuned-medical-bert) [Fico Filbert](https://savrn.com/model-publishers/ficofilbert) 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… Open weights 109M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/finetuned-medical-bert) Model · Sentence similarity ### [simcse-bert-uncased-unsup](https://savrn.com/models/simcse-bert-uncased-unsup) [Russel Ku](https://savrn.com/model-publishers/russelkuaguilar) 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… Open weights apache-2.0 109M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/simcse-bert-uncased-unsup) Model · Sentence similarity ### [simcse-bert-base-snli-unsup](https://savrn.com/models/simcse-bert-base-snli-unsup) [Julio De Aquino](https://savrn.com/model-publishers/pyrawn) 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… Open weights 109M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/simcse-bert-base-snli-unsup) ## Data Society [All models and datasets](https://savrn.com/model-publishers/datasocietyco) ## Versions - [2b069eed51ce](https://savrn.com/models/bge-base-en-v1-5-course-recommender-v5/versions/2b069eed51ce) · current 2026-10-23 ## Explore More - [All sentence similarity models](https://savrn.com/models/tasks/sentence-similarity) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-19. - [Hugging Face record](https://huggingface.co/datasocietyco/bge-base-en-v1.5-course-recommender-v5) - [How the hub is built](https://savrn.com/model-hub/methodology)