This Sentence-CamemBERT-Large Model is an Embedding Model for French developed by La Javaness. The purpose of this embedding model is to represent the content and semantics of a French sentence as a mathematical vector, allowing it to understand the meaning of the text beyond individual words in queries and documents. It offers powerful semantic search capabilities.
Pre-trained sentence embedding models are state-of-the-art of Sentence Embeddings for French.
The model can be used directly (without a language model) as follows:
python
1from sentence_transformers import SentenceTransformer
2model = SentenceTransformer("Lajavaness/sentence-camembert-large")34sentences =["Un avion est en train de décoller.",5"Un homme joue d'une grande flûte.",6"Un homme étale du fromage râpé sur une pizza.",7"Une personne jette un chat au plafond.",8"Une personne est en train de plier un morceau de papier.",9]1011embeddings = model.encode(sentences)
Evaluation
The model can be evaluated as follows on the French test data of stsb.
python
1from sentence_transformers import SentenceTransformer
2from sentence_transformers.readers import InputExample
3from datasets import load_dataset
4defconvert_dataset(dataset):5 dataset_samples=[]6for df in dataset:7 score =float(df['similarity_score'])/5.0# Normalize score to range 0 ... 18 inp_example = InputExample(texts=[df['sentence1'],9 df['sentence2']], label=score)10 dataset_samples.append(inp_example)11return dataset_samples
1213# Loading the dataset for evaluation14df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev")15df_test = load_dataset("stsb_multi_mt", name="fr", split="test")1617# Convert the dataset for evaluation1819# For Dev set:20dev_samples = convert_dataset(df_dev)21val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')22val_evaluator(model, output_path="./")2324# For Test set:25test_samples = convert_dataset(df_test)26test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')27test_evaluator(model, output_path="./")
Test Result:
The performance is measured using Pearson and Spearman correlation:
@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}
}
@article{martin2020camembert,
title={CamemBERT: a Tasty French Language Mode},
author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
year={2020}
}
