README.md

---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dataset_size:n<1K
- loss:CoSENTLoss
base_model: sentence-transformers/LaBSE
widget:
- source_sentence: Personnel contractuel
  sentences:
  - Vacataire
  - Départ définitif pour cause de mutation
  - Fin du temps partiel thérapeutique
- source_sentence: Prolongation de stage
  sentences:
  - Titularisation
  - Renouvellement du congé de longue durée
  - Fin du temps partiel thérapeutique
- source_sentence: ' avancement d''échelon'
  sentences:
  - 'Avancement d''échelon '
  - Renouvellement du congé de longue durée
  - Disponibilité pour suivre un conjoint ou un partenaire lié par un PACS
- source_sentence: Sanction disciplinaire
  sentences:
  - Sanction suite à une infraction disciplinaire
  - Départ définitif - Radiation des cadres
  - Disponibilité pour suivre un conjoint ou un partenaire lié par un PACS
- source_sentence: Temps partiel surcotisé
  sentences:
  - Temps partiel surcotisé de droit
  - Départ définitif - Radiation des cadres
  - Fin du temps partiel thérapeutique
pipeline_tag: sentence-similarity
---

# SentenceTransformer based on sentence-transformers/LaBSE

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/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](https://huggingface.co/sentence-transformers/LaBSE) <!-- at revision 50fe0940fa3ca3be4d2170f21395beb6d581fc44 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### 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:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
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]
```

<!--
### Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details>
-->

<!--
### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
-->

<!--
### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->

<!--
## Bias, Risks and Limitations

*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->

<!--
### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->

## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 295 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                          | hypothesis                                                                        | label                                                          |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | float                                                          |
  | details | <ul><li>min: 4 tokens</li><li>mean: 9.31 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.41 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 0.9</li><li>mean: 0.95</li><li>max: 1.0</li></ul> |
* Samples:
  | premise                                                                          | hypothesis                                                        | label                           |
  |:---------------------------------------------------------------------------------|:------------------------------------------------------------------|:--------------------------------|
  | <code>Compte rendu d'entretien professionnel</code>                              | <code>Synthèse des discussions professionnelles</code>            | <code>0.9820208462484844</code> |
  | <code>Congé Accident de trajet</code>                                            | <code>Arrêt de travail pour accident de trajet</code>             | <code>0.9755981363214147</code> |
  | <code>Retrait ou suppression du CTI (complément de traitement indiciaire)</code> | <code>Retrait du Complément de Traitement Indiciaire (CTI)</code> | <code>0.9524167934189104</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset


* Size: 74 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | premise                                                                           | hypothesis                                                                       | label                                                          |
  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                           | float                                                          |
  | details | <ul><li>min: 4 tokens</li><li>mean: 10.26 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.5 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 0.9</li><li>mean: 0.95</li><li>max: 1.0</li></ul> |
* Samples:
  | premise                                           | hypothesis                                                      | label                           |
  |:--------------------------------------------------|:----------------------------------------------------------------|:--------------------------------|
  | <code>Sanction disciplinaire</code>               | <code>Mesure punitive suite à une violation du règlement</code> | <code>0.958828679924412</code>  |
  | <code>Départ définitif / Radiation - Décès</code> | <code>Départ définitif suite au décès d'un agent</code>         | <code>0.9003635138326387</code> |
  | <code>Nomination par intégration directe</code>   | <code>Intégration immédiate avec nomination</code>              | <code>0.9993378836623817</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
  ```json
  {
      "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
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 30
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch   | Step | Training Loss | loss   |
|:-------:|:----:|:-------------:|:------:|
| 0.5263  | 10   | 12.4933       | -      |
| 1.0526  | 20   | 10.5909       | -      |
| 1.5789  | 30   | 7.0607        | -      |
| 2.1053  | 40   | 4.7061        | -      |
| 2.6316  | 50   | 4.7957        | -      |
| 3.1579  | 60   | 4.624         | -      |
| 3.6842  | 70   | 4.7854        | -      |
| 4.2105  | 80   | 4.5902        | -      |
| 4.7368  | 90   | 4.7051        | -      |
| 5.2632  | 100  | 4.5562        | 4.6756 |
| 5.7895  | 110  | 4.6376        | -      |
| 6.3158  | 120  | 4.4501        | -      |
| 6.8421  | 130  | 4.5993        | -      |
| 7.3684  | 140  | 4.4878        | -      |
| 7.8947  | 150  | 4.5443        | -      |
| 8.4211  | 160  | 4.3091        | -      |
| 8.9474  | 170  | 4.6699        | -      |
| 9.4737  | 180  | 4.3727        | -      |
| 10.0    | 190  | 4.3888        | -      |
| 10.5263 | 200  | 4.5099        | 5.3597 |
| 11.0526 | 210  | 4.3427        | -      |
| 11.5789 | 220  | 4.4409        | -      |
| 12.1053 | 230  | 4.3151        | -      |
| 12.6316 | 240  | 4.3522        | -      |
| 13.1579 | 250  | 4.3133        | -      |
| 13.6842 | 260  | 4.3842        | -      |
| 14.2105 | 270  | 4.2708        | -      |
| 14.7368 | 280  | 4.387         | -      |
| 15.2632 | 290  | 4.1131        | -      |
| 15.7895 | 300  | 4.3394        | 5.5109 |
| 16.3158 | 310  | 4.2948        | -      |
| 16.8421 | 320  | 4.3413        | -      |
| 17.3684 | 330  | 4.1427        | -      |
| 17.8947 | 340  | 4.5521        | -      |
| 18.4211 | 350  | 4.2146        | -      |
| 18.9474 | 360  | 4.2039        | -      |
| 19.4737 | 370  | 4.1412        | -      |
| 20.0    | 380  | 4.0869        | -      |
| 20.5263 | 390  | 4.4763        | -      |
| 21.0526 | 400  | 3.9572        | 5.7054 |
| 21.5789 | 410  | 4.2114        | -      |
| 22.1053 | 420  | 4.2651        | -      |
| 22.6316 | 430  | 4.2231        | -      |
| 23.1579 | 440  | 4.0521        | -      |
| 23.6842 | 450  | 4.3246        | -      |
| 24.2105 | 460  | 3.9145        | -      |
| 24.7368 | 470  | 4.1701        | -      |
| 25.2632 | 480  | 4.0958        | -      |
| 25.7895 | 490  | 4.1177        | -      |
| 26.3158 | 500  | 4.2388        | 6.3162 |
| 26.8421 | 510  | 4.3043        | -      |
| 27.3684 | 520  | 3.9634        | -      |
| 27.8947 | 530  | 4.117         | -      |
| 28.4211 | 540  | 4.1732        | -      |
| 28.9474 | 550  | 4.1243        | -      |
| 29.4737 | 560  | 3.7898        | -      |
| 30.0    | 570  | 4.0227        | -      |


### 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
```bibtex
@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
```bibtex
@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},
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->