metadata

pipeline_tag: sentence-similarity
language: fr
datasets:
  - stsb_multi_mt
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
license: mit
model-index:
  - name: sentence-croissant-llm-base by Wissam Siblini
    results:
      - task:
          name: Sentence-Embedding
          type: Text Similarity
        dataset:
          name: Text Similarity fr
          type: stsb_multi_mt
          args: fr
        metrics:
          - name: Test Pearson correlation coefficient
            type: Pearson_correlation_coefficient
            value: xx.xx

Overview

The model sentence-croissant-llm-base is designed to generate French text embeddings. It has been fine-tuned using the very recent pre-trained LLM croissantllm/CroissantLLMBase with the strategy of Siamese-BERT implemented in the library 'sentences-transformers'. The fine tuning dataset used is the French training split of stsb.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
model =  SentenceTransformer("Wissam42/sentence-croissant-llm-base")
sentences = ["Le chat mange la souris", "Un felin devore un rongeur", "Je travaille sur un ordinateur", "Je developpe sur mon pc"]
embeddings = model.encode(sentences)
print(embeddings)

Citing & Authors

@article{faysse2024croissantllm,
    title={CroissantLLM: A Truly Bilingual French-English Language Model},
    author={Faysse, Manuel and Fernandes, Patrick and Guerreiro, Nuno and Loison, Ant{\'o}nio and Alves, Duarte and Corro, Caio and Boizard, Nicolas and Alves, Jo{\~a}o and Rei, Ricardo and Martins, Pedro and others},
    journal={arXiv preprint arXiv:2402.00786},
    year={2024}
}

@article{reimers2019sentence,
   title={Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks},
   author={Nils Reimers, Iryna Gurevych},
   journal={https://arxiv.org/abs/1908.10084},
   year={2019}
}