Model reference · open weights
bge-en-course-recommender is an open-weight embedding model from datasocietyco, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
SentenceTransformer based on BAAI/bge-base-en-v1.5 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. Model Details Model Description - Model Type: Sentence Transformer - Base model: BAAI/bge-base-en-v1.5 <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -- - Maximum Sequence Length: 512 tokens - Output Dimensionality: 768 tokens - Similarity Function: Cosine Similarity Model Sources - Documentation: Sentence Transformers Documentation - Repository: Sentence Transformers on GitHub - Hugging Face: Sentence Transformers on Hugging Face Full Model Architecture Usage Direct Usage (Sentence Transformers) First install the Sentence Transformers library: Then you can load this model and run inference. Direct Usage (Transformers) -- Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. -- Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. -- Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -- Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -- Training Details Training Dataset Unnamed Dataset Size: 45 training samples Columns: <codeanchor</code and <codepositive</code Approximate statistics based on the first 45 samples: Samples: Loss: <codeMultipleNegativesRankingLoss</code with these parameters: Evaluation Dataset Unnamed Dataset Size: 5 evaluation samples Columns: <codeanchor</code and <codepositive</code Approximate statistics based on the first 5 samples: Samples: Loss: <codeMultipleNegativesRankingLoss</code with these parameters: Training Hyperparameters Non-Default Hyperparameters - evalstrategy: steps - perdevicetrainbatchsize: 16 - perdeviceevalbatchsize: 16 - learningrate: 3e-06 - maxsteps: 24 - warmupratio: 0.1 - batchsampler: noduplicates A
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | datasocietyco |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 109M |
| Context | 512 tokens |
| Variants | 1 |
| Runs with | sentence-transformers |
| Based on | BAAI/bge-base-en-v1.5 |
| Released | 2025-01-09 |
| Popularity | 4.2M downloads / month |
| Likes | 3 |
| Licence | Commercial licence needed |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| bge-base-en-v1.5-course-recommender-v5 | 109M | BF16 | ~0.3 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys bge-en-course-recommender for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (bge-en-course-recommender below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"bge-en-course-recommender","input":"text to embed"}'
Licence
The weights are open but its licence needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗
Explore