crossencoder-distilcamembert-mmarcoFR

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What crossencoder-distilcamembert-mmarcoFR is

crossencoder-distilcamembert-mmarcoFR This is a cross-encoder model for French. It performs cross-attention between a question-passage pair and outputs a relevance score. The model should be used as a reranker for semantic search: given a query and a set of potentially relevant passages retrieved by an efficient first-stage retrieval system (e.g., BM25 or a fine-tuned dense single-vector bi-encoder), encode each query-passage pair and sort the passages in a decreasing order of relevance according to the model's predicted scores. Usage Here are some examples for using the model with Sentence-Transformers, FlagEmbedding, or Huggingface Transformers. Using Sentence-Transformers Start by installing the library: pip install -U sentence-transformers. Then, you can use the model like this: Using FlagEmbedding Start by installing the library: pip install -U FlagEmbedding. Then, you can use the model like this: Using HuggingFace Transformers Start by installing the library: pip install -U transformers. Then, you can use the model like this: Evaluation The model is evaluated on the smaller development set of mMARCO-fr, which consists of 6,980 queries for which an ensemble of 1000 passages containing the positive(s) and ColBERTv2 hard negatives need to be reranked. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k). To see how it compares to other neural retrievers in French, check out the DécouvrIR leaderboard. Training Data We use the French training samples from the mMARCO dataset, a multilingual machine-translated version of MS MARCO that contains 8.8M passages and 539K training queries. We do not use the BM25 negatives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the msmarco-hard-negatives distillation dataset. Eventually, we sample 2.6M training triplets of the form (query, passage, relevance) with a positive-to-negative ratio of 1 (i.e., 50% of the pairs are relevant and 50% are irrelevant). Implementation The model is initialized from the cmarkea/distilcamembert-base checkpoint and optimized via the binary cross-entropy loss (as in monoBERT). It is fine-tuned on one 80

Summarised from the published model card. Read the full card on the HuggingFace links below.

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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.

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Call it like any OpenAI endpoint

Once AxForge deploys crossencoder-distilcamembert-mmarcofr for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (crossencoder-distilcamembert-mmarcofr 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":"crossencoder-distilcamembert-mmarcofr","input":"text to embed"}'

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