Sentence Similarity
sentence-transformers
Safetensors
French
deberta-v2
passage-retrieval
Eval Results (legacy)
text-embeddings-inference
Instructions to use antoinelouis/biencoder-mdebertav3-mmarcoFR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use antoinelouis/biencoder-mdebertav3-mmarcoFR with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("antoinelouis/biencoder-mdebertav3-mmarcoFR") sentences = [ "C'est une personne heureuse", "C'est un chien heureux", "C'est une personne très heureuse", "Aujourd'hui est une journée ensoleillée" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| epoch,steps,cos_sim-Accuracy@1,cos_sim-Precision@5,cos_sim-Recall@5,cos_sim-Precision@10,cos_sim-Recall@10,cos_sim-Precision@20,cos_sim-Recall@20,cos_sim-Precision@50,cos_sim-Recall@50,cos_sim-Precision@100,cos_sim-Recall@100,cos_sim-Precision@200,cos_sim-Recall@200,cos_sim-Precision@500,cos_sim-Recall@500,cos_sim-Precision@1000,cos_sim-Recall@1000,cos_sim-MRR@10,cos_sim-MRR@100,cos_sim-NDCG@10,cos_sim-NDCG@100,cos_sim-MAP@10,cos_sim-MAP@100 | |
| 20,78000,0.16103151862464182,0.07406876790830945,0.35676934097421203,0.04756446991404011,0.4561604584527221,0.02838825214899713,0.5416069723018146,0.013650429799426935,0.6469436485195798,0.007545845272206303,0.7143982808022923,0.004070916905444126,0.7697468958930277,0.0017659025787965623,0.8331423113658071,0.0009240687679083099,0.8704035339063991,0.2488015645608762,0.2592331187292908,0.29556499189828134,0.3501492619416213,0.24359634386228227,0.25432963573184086 | |