Model reference · open weights
bge-fr-en is an open-weight embedding model from manu. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | manu |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 568M |
| Context | 8194 tokens |
| Runs with | sentence-transformers |
| Released | 2024-04-11 |
| Popularity | 1k downloads / month |
| Licence | Unknown |
About
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model is a finetuned version of BGE M3's dense model on custom french and english data.
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('manu/bge-fr-en')
embeddings = model.encode(sentences)
print(embeddings)
This model was evaluated using the MTEB package.
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
From the published model card. Full card on the HuggingFace links in the sidebar.
Benchmarks
As published on the model card — the maker's own numbers, not measured by AxForge.
| Task | Dataset | Metric | Score |
|---|---|---|---|
| Clustering | MTEB AlloProfClusteringP2P | v_measure | 56.727 |
| Clustering | MTEB AlloProfClusteringS2S | v_measure | 38.199 |
| Reranking | MTEB AlloprofReranking | map | 65.175 |
| Reranking | MTEB AlloprofReranking | mrr | 66.514 |
| Retrieval | MTEB AlloprofRetrieval | map_at_1 | 29.836 |
| Retrieval | MTEB AlloprofRetrieval | map_at_10 | 39.916 |
| Retrieval | MTEB AlloprofRetrieval | map_at_100 | 40.816 |
| Retrieval | MTEB AlloprofRetrieval | map_at_1000 | 40.877 |
| Retrieval | MTEB AlloprofRetrieval | map_at_3 | 37.294 |
| Retrieval | MTEB AlloprofRetrieval | map_at_5 | 38.838 |
| Retrieval | MTEB AlloprofRetrieval | mrr_at_1 | 29.836 |
| Retrieval | MTEB AlloprofRetrieval | mrr_at_10 | 39.916 |
| Retrieval | MTEB AlloprofRetrieval | mrr_at_100 | 40.816 |
| Retrieval | MTEB AlloprofRetrieval | mrr_at_1000 | 40.877 |
| Retrieval | MTEB AlloprofRetrieval | mrr_at_3 | 37.294 |
| Retrieval | MTEB AlloprofRetrieval | mrr_at_5 | 38.838 |
| Retrieval | MTEB AlloprofRetrieval | ndcg_at_1 | 29.836 |
| Retrieval | MTEB AlloprofRetrieval | ndcg_at_10 | 45.097 |
| Retrieval | MTEB AlloprofRetrieval | ndcg_at_100 | 49.683 |
| Retrieval | MTEB AlloprofRetrieval | ndcg_at_1000 | 51.429 |
| Retrieval | MTEB AlloprofRetrieval | ndcg_at_3 | 39.717 |
| Retrieval | MTEB AlloprofRetrieval | ndcg_at_5 | 42.501 |
| Retrieval | MTEB AlloprofRetrieval | precision_at_1 | 29.836 |
| Retrieval | MTEB AlloprofRetrieval | precision_at_10 | 6.149 |
Using it via the API
Once AxForge deploys bge-fr-en for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (bge-fr-en 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-fr-en","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.