aminlouhichi / CDGSmilarity

SentenceTransformer based on sentence-transformers/LaBSE

This is a sentence-transformers model finetuned from sentence-transformers/LaBSE . 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.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/LaBSE
• Maximum Sequence Length: 256 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': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  (3): Normalize()
)

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("aminlouhichi/CDGSmilarity")
# Run inference
sentences = [
    'Temps partiel surcotisé',
    'Temps partiel surcotisé de droit',
    'Départ définitif - Radiation des cadres',
]
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]

Training Details

Training Dataset

Unnamed Dataset

• Size: 295 training samples
• Columns: premise , hypothesis , and label
• Approximate statistics based on the first 1000 samples:

  	premise	hypothesis	label
  type	string	string	float
  details	min: 4 tokens mean: 9.31 tokens max: 20 tokens	min: 4 tokens mean: 10.41 tokens max: 20 tokens	min: 0.9 mean: 0.95 max: 1.0

• Samples:

  premise	hypothesis	label
  Compte rendu d'entretien professionnel	Synthèse des discussions professionnelles	0.9820208462484844
  Congé Accident de trajet	Arrêt de travail pour accident de trajet	0.9755981363214147
  Retrait ou suppression du CTI (complément de traitement indiciaire)	Retrait du Complément de Traitement Indiciaire (CTI)	0.9524167934189104

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 74 evaluation samples
• Columns: premise , hypothesis , and label
• Approximate statistics based on the first 1000 samples:

  	premise	hypothesis	label
  type	string	string	float
  details	min: 4 tokens mean: 10.26 tokens max: 25 tokens	min: 5 tokens mean: 10.5 tokens max: 20 tokens	min: 0.9 mean: 0.95 max: 1.0

• Samples:

  premise	hypothesis	label
  Sanction disciplinaire	Mesure punitive suite à une violation du règlement	0.958828679924412
  Départ définitif / Radiation - Décès	Départ définitif suite au décès d'un agent	0.9003635138326387
  Nomination par intégration directe	Intégration immédiate avec nomination	0.9993378836623817

• Loss: CoSENTLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy : steps
• per_device_train_batch_size : 16
• per_device_eval_batch_size : 16
• num_train_epochs : 30
• warmup_ratio : 0.1
• fp16 : True

All Hyperparameters

Training Logs

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.0
• Transformers: 4.41.1
• PyTorch: 2.3.0+cu121
• Accelerate: 0.30.1
• Datasets: 2.19.1
• Tokenizers: 0.19.1

Citation

BibTeX

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",
}

CoSENTLoss

@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}