Sentence Similarity
sentence-transformers
PyTorch
Safetensors
French
xlm-roberta
passage-retrieval
Eval Results (legacy)
text-embeddings-inference
Instructions to use antoinelouis/biencoder-mMiniLMv2-L6-mmarcoFR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use antoinelouis/biencoder-mMiniLMv2-L6-mmarcoFR with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("antoinelouis/biencoder-mMiniLMv2-L6-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
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README.md
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metrics:
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- type: recall_at_500
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name: Recall@500
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value: 79.
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- type: recall_at_100
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name: Recall@100
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value: 66.
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- type: recall_at_10
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name: Recall@10
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value: 41.
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- type: map_at_10
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name: MAP@10
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value: 21.
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- type: ndcg_at_10
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name: nDCG@10
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value: 26.
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- type: mrr_at_10
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name: MRR@10
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value: 22.
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---
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# biencoder-mMiniLMv2-L6-mmarcoFR
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metrics:
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- type: recall_at_500
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name: Recall@500
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value: 79.8
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- type: recall_at_100
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name: Recall@100
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value: 66.8
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- type: recall_at_10
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name: Recall@10
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value: 41.3
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- type: map_at_10
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name: MAP@10
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value: 21.8
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- type: ndcg_at_10
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name: nDCG@10
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value: 26.6
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- type: mrr_at_10
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name: MRR@10
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value: 22.3
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---
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# biencoder-mMiniLMv2-L6-mmarcoFR
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