
# Solon Embeddings — large 0.1

SOTA Open source french embedding model.

**Instructions :**  
Add "query : " before the *query* to retrieve to increase performance of retrieval.  
No instructions needed for *passages*.


| Model | Mean Score |
| --- | --- |
| **OrdalieTech/Solon-embeddings-large-0.1** | 0.7490 |
| cohere/embed-multilingual-v3 | 0.7402 |
| **OrdalieTech/Solon-embeddings-base-0.1** | 0.7306 |
| openai/ada-002 | 0.7290 |
| cohere/embed-multilingual-light-v3 | 0.6945 |
| antoinelouis/biencoder-camembert-base-mmarcoFR | 0.6826 |
| dangvantuan/sentence-camembert-large | 0.6756 |
| voyage/voyage-01 | 0.6753 |
| intfloat/multilingual-e5-large | 0.6660 |
| intfloat/multilingual-e5-base | 0.6597 |
| Sbert/paraphrase-multilingual-mpnet-base-v2 | 0.5975 |
| dangvantuan/sentence-camembert-base | 0.5456 |
| EuropeanParliament/eubert_embedding_v1 | 0.5063 |

These results have been obtained through 9 french benchmarks on a variety of text similarity tasks (classification, reranking, STS) :
- AmazonReviewsClassification (MTEB)
- MassiveIntentClassification (MTEB)
- MassiveScenarioClassification (MTEB)
- MTOPDomainClassification (MTEB)
- MTOPIntentClassification (MTEB)
- STS22 (MTEB)
- MiraclFRRerank (Miracl)
- OrdalieFRSTS (Ordalie)
- OrdalieFRReranking (Ordalie)

We created OrdalieFRSTS and OrdalieFRReranking to enhance the benchmarking capabilities of French STS and reranking assessments.

(evaluation script available here : github.com/OrdalieTech/mteb)
{"language": ["fr", "en"], "license": "mit", "tags": ["feature-extraction"], "pipeline_tag": "sentence-similarity"}
transformers safetensors xlm-roberta feature-extraction sentence-similarity fr en license:mit endpoints_compatible region:us