CATIE-AQ / distilcamembert-base-embedding

CATIE-AQ/distilcamembert-base-embedding

Description

This is a sentence-transformers model finetuned from cmarkea/distilcamembert-base (68.1M parameters). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Score on the MTEB leaderboard:

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: cmarkea/distilcamembert-base
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: CamembertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': True, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("CATIE-AQ/distilcamembert-base-embedding")
# Run inference
sentences = [
    "Tenet est sous surveillance depuis novembre, lorsque l'ancien directeur général Jeffrey Barbakow a déclaré que la société a utilisé des prix agressifs pour déclencher des paiements plus élevés pour les patients les plus malades de l'assurance maladie.",
    "En novembre, Jeffrey Brabakow, le directeur général de l'époque, a déclaré que la société utilisait des prix agressifs pour obtenir des paiements plus élevés pour les patients les plus malades de l'assurance maladie.",
    'La femme est en route pour un rendez-vous.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Citation

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}