Add new SentenceTransformer model.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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/config.json

@@ -0,0 +1 @@
+{"in_features": 768, "out_features": 768, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29fec284391d20f5185528fdf7c7d62795dcc6a803a1380d87940a5f094a6d2e
+size 2364028

README.md

@@ -0,0 +1,434 @@
+---
+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.*
+-->

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "sentence-transformers/LaBSE",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 501153
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.7.0",
+    "pytorch": "1.9.0+cu102"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+oid sha256:b689709ae34bcd873353960f762ce1a8f5431da2a6f0f5ea692325cd6d738764
+size 1883730160

modules.json

@@ -0,0 +1,26 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
+  },
+  {
+    "idx": 3,
+    "name": "3",
+    "path": "3_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
+size 13632172

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
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+    }
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+  "full_tokenizer_file": null,
+  "mask_token": "[MASK]",
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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}