E5 Multilingual FrenchBoost v1
E5 Multilingual FrenchBoost v1 is a multilingual sentence embedding model boosted for French, derived from intfloat/multilingual-e5-base.
This is the first public release (model-v1). The goal is practical French semantic search, reranking, clustering, and similarity while preserving the broad multilingual E5 embedding space as much as possible.
Intended use
Use this model for French-heavy multilingual embeddings in retrieval, reranking, semantic search, clustering, duplicate detection, and similarity scoring. Like E5, it works best with explicit prefixes:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("AurelPx/e5-multilingual-frenchboost-v1")
queries = ["query: Quelle est la capitale de la France ?"]
passages = ["passage: Paris est la capitale de la France."]
query_embeddings = model.encode(queries, normalize_embeddings=True)
passage_embeddings = model.encode(passages, normalize_embeddings=True)
scores = query_embeddings @ passage_embeddings.T
Training summary
- Base model:
intfloat/multilingual-e5-base - Method: conservative LoRA fine-tuning followed by merge for easy Sentence Transformers inference
- Data mixture: French retrieval/reranking signals, MIRACL-style hard-negative ranking, French STS/paraphrase/NLI signals, multilingual replay, and baseline embedding distillation
- Selection: held-out French retrieval + STS proxy evaluation with anti-regression guardrails and no train/eval leakage
- Export checks: non-zero LoRA weights, adapter reload parity, merged-model embedding parity
Evaluation
Internal evaluation was run with a memory-safe MTEB(fra)-safe v2 protocol: one task per isolated process, using bounded variants for the historically crash-prone clustering tasks. These numbers should be read as a transparent development benchmark, not as a final SOTA claim.
- Mean score over 25 tasks: 0.5636
- Benchmark label:
MTEB(fra)-safe v2 GPU-fp16 conservative-lora
Category scores
| category | score | n_tasks |
|---|---|---|
| Classification | 0.6384 | 6 |
| STS | 0.7423 | 3 |
| Reranking | 0.7681 | 2 |
| PairClass. | 0.5538 | 1 |
| Clustering | 0.4402 | 7 |
| Retrieval | 0.5062 | 5 |
| Summarization | 0.3294 | 1 |
Per-task comparison
The table below compares this V1 checkpoint against the previous internal LoRA candidate when that reference run is available locally.
| task | v1_score | reference_score | delta |
|---|---|---|---|
| SyntecRetrieval | 0.8231 | 0.3292 | 0.4939 |
| BSARDRetrieval | 0.5856 | 0.1622 | 0.4234 |
| SyntecReranking | 0.8525 | 0.5442 | 0.3083 |
| MasakhaNEWSClusteringS2S | 0.5543 | 0.2479 | 0.3064 |
| AlloprofRetrieval | 0.3558 | 0.0557 | 0.3001 |
| AlloprofReranking | 0.6837 | 0.5077 | 0.1760 |
| AlloProfClusteringP2P | 0.6263 | 0.4635 | 0.1628 |
| XPQARetrieval | 0.4571 | 0.3401 | 0.1170 |
| SummEvalFr | 0.3294 | 0.2921 | 0.0373 |
| STS22 | 0.6485 | 0.6187 | 0.0298 |
| MTOPDomainClassification | 0.8266 | 0.8025 | 0.0241 |
| MassiveScenarioClassification | 0.6710 | 0.6482 | 0.0228 |
Current limitations
This is a V1. It is already stronger in reranking in the internal evaluation, but the model card will be updated as broader validation comes in. The next version will focus on better robustness across retrieval, clustering, and pair classification while keeping STS gains.
Citation / attribution
This model builds on multilingual E5 by Microsoft / intfloat and uses the Sentence Transformers ecosystem.