Model reference · open weights

bge-fr-en

Available as managed deployment Embeddings manu · community Embeddings 1 variants 1k dl/mo

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 bymanu
TypeEmbedding models
TaskEmbeddings
Parameters (lead)568M
Context8194 tokens
Runs withsentence-transformers
Released2024-04-11
Popularity1k downloads / month
LicenceUnknown

About

What bge-fr-en is

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.

Read the full model card

Usage (Sentence-Transformers)

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)

Evaluation Results

This model was evaluated using the MTEB package.

Full Model Architecture

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()
)

Citing & Authors

From the published model card. Full card on the HuggingFace links in the sidebar.

Benchmarks

Reported results

As published on the model card — the maker's own numbers, not measured by AxForge.

TaskDatasetMetricScore
ClusteringMTEB AlloProfClusteringP2Pv_measure56.727
ClusteringMTEB AlloProfClusteringS2Sv_measure38.199
RerankingMTEB AlloprofRerankingmap65.175
RerankingMTEB AlloprofRerankingmrr66.514
RetrievalMTEB AlloprofRetrievalmap_at_129.836
RetrievalMTEB AlloprofRetrievalmap_at_1039.916
RetrievalMTEB AlloprofRetrievalmap_at_10040.816
RetrievalMTEB AlloprofRetrievalmap_at_100040.877
RetrievalMTEB AlloprofRetrievalmap_at_337.294
RetrievalMTEB AlloprofRetrievalmap_at_538.838
RetrievalMTEB AlloprofRetrievalmrr_at_129.836
RetrievalMTEB AlloprofRetrievalmrr_at_1039.916
RetrievalMTEB AlloprofRetrievalmrr_at_10040.816
RetrievalMTEB AlloprofRetrievalmrr_at_100040.877
RetrievalMTEB AlloprofRetrievalmrr_at_337.294
RetrievalMTEB AlloprofRetrievalmrr_at_538.838
RetrievalMTEB AlloprofRetrievalndcg_at_129.836
RetrievalMTEB AlloprofRetrievalndcg_at_1045.097
RetrievalMTEB AlloprofRetrievalndcg_at_10049.683
RetrievalMTEB AlloprofRetrievalndcg_at_100051.429
RetrievalMTEB AlloprofRetrievalndcg_at_339.717
RetrievalMTEB AlloprofRetrievalndcg_at_542.501
RetrievalMTEB AlloprofRetrievalprecision_at_129.836
RetrievalMTEB AlloprofRetrievalprecision_at_106.149

Using it via the API

Call it like any OpenAI endpoint

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.

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