Model reference · open weights
S-PubMedBert-MedQuAD is an open-weight embedding model from TimKond, 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
S-PubMedBert-MedQuAD 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. Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: Usage (HuggingFace Transformers) 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. Evaluation Results For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net Training The model was trained with the parameters: DataLoader: torch.utils.data.DataLoader of length 82590 with parameters: Loss: sentencetransformers.losses.SoftmaxLoss with parameters: Parameters of the fit()-Method: Full Model Architecture Citing & Authors
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | TimKond |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 109M |
| Context | 512 tokens |
| Variants | 1 |
| Runs with | sentence-transformers |
| Released | 2022-06-09 |
| Popularity | 442k downloads / month |
| Likes | 8 |
| Licence | Open weights |
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 |
|---|---|---|---|---|---|
| S-PubMedBert-MedQuAD | 109M | BF16 | ~0.3 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys s-pubmedbert-medquad for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (s-pubmedbert-medquad 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":"s-pubmedbert-medquad","input":"text to embed"}'
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Licence
Open weights under mit — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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