Model reference · open weights
mmarco-mMiniLM-L12-H384 is an open-weight embedding model from cross-encoder, 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
Cross-Encoder for multilingual MS Marco This model was trained on the MMARCO dataset. It is a machine translated version of MS MARCO using Google Translate. It was translated to 14 languages. In our experiments, we observed that it performs also well for other languages. As a base model, we used the multilingual MiniLMv2 model. The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See SBERT.net Retrieve & Re-rank for more details. The training code is available here: SBERT.net Training MS Marco Usage with SentenceTransformers The usage becomes easy when you have SentenceTransformers installed. Then, you can use the pre-trained models like this: Usage with Transformers
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | cross-encoder |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 118M |
| Context | 514 tokens |
| Variants | 1 |
| Runs with | sentence-transformers |
| Based on | nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Large |
| Released | 2022-06-01 |
| Popularity | 1.8M downloads / month |
| Likes | 79 |
| 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 |
|---|---|---|---|---|---|
| mmarco-mMiniLMv2-L12-H384-v1 | 118M | BF16 | ~0.3 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys mmarco-mminilm-l12-h384 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (mmarco-mminilm-l12-h384 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":"mmarco-mminilm-l12-h384","input":"text to embed"}'
Details
Languages
Trained / evaluated on
Tags
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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