Sentence Camembert Large

🚀 Sentence-CamemBERT-Large

This model is an embedding model for French, capable of representing sentence semantics as vectors and offering powerful semantic search capabilities.

✨ Features

• Sentence Embedding: Represents the content and semantics of French sentences as mathematical vectors, enabling understanding of text meaning beyond individual words.
• Enhanced Performance: An improvement over the dangvantuan/sentence-camembert-base, offering greater robustness and better performance on all STS benchmark datasets.
• Fine - Tuned: Fine - tuned using the pre - trained facebook/camembert-large and Siamese BERT - Networks with 'sentences - transformers' on dataset stsb. Also combined with Augmented SBERT on the same dataset.
• Pair Sampling Strategies: Benefits from pair sampling strategies using two models: CrossEncoder-camembert-large and dangvantuan/sentence-camembert-large.

📦 Model Information

💻 Usage Examples

Basic Usage

from sentence_transformers import SentenceTransformer
model =  SentenceTransformer("Lajavaness/sentence-camembert-large")

sentences = ["Un avion est en train de décoller.",
          "Un homme joue d'une grande flûte.",
          "Un homme étale du fromage râpé sur une pizza.",
          "Une personne jette un chat au plafond.",
          "Une personne est en train de plier un morceau de papier.",
          ]

embeddings = model.encode(sentences)

Advanced Usage (Evaluation)

from sentence_transformers import SentenceTransformer
from sentence_transformers.readers import InputExample
from datasets import load_dataset
def convert_dataset(dataset):
    dataset_samples=[]
    for df in dataset:
        score = float(df['similarity_score'])/5.0  # Normalize score to range 0 ... 1
        inp_example = InputExample(texts=[df['sentence1'], 
                                    df['sentence2']], label=score)
        dataset_samples.append(inp_example)
    return dataset_samples

# Loading the dataset for evaluation
df_dev = load_dataset("stsb_multi_mt", name="fr", split="dev")
df_test = load_dataset("stsb_multi_mt", name="fr", split="test")

# Convert the dataset for evaluation

# For Dev set:
dev_samples = convert_dataset(df_dev)
val_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(dev_samples, name='sts-dev')
val_evaluator(model, output_path="./")

# For Test set:
test_samples = convert_dataset(df_test)
test_evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')
test_evaluator(model, output_path="./")

📚 Evaluation Results

Dev Set

Test Set - Pearson Score

Test Set - Spearman Score

📄 License

This project is licensed under the Apache 2.0 License.

📚 Citation

@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 Suárez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, Éric Villemonte and Seddah, Djamé and Sagot, Benoît},
   journal={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
   year={2020}
}