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}
}
Downloads last month
48
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train Wissam42/sentence-croissant-llm-base

Spaces using Wissam42/sentence-croissant-llm-base 4

Evaluation results

  • Test Pearson correlation coefficient on Text Similarity fr
    self-reported
    xx.xx