sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:132020 loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:1705.00652 base_model:pyrac/rse_engagement_des_collaborateurs base_model:finetune:pyrac/rse_engagement_des_collaborateurs license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: pyrac/rse_engagement_des_collaborateurs
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:132020
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: And it is ecolabel too!!
  sentences:
  - Leur transparence sur les actions éthiques est impressionnante.
  - On ne voulait pas cette chambre et l'avoir imposée a vraiment gâché notre expérience.
  - L'établissement met en place des protocoles de sécurité au travail qui garantissent
    un environnement sain pour tous
- source_sentence: Les employés semblent travailler dans des conditions de stress
    élevé et sans soutien.
  sentences:
  - 'Le personnel étranger étranger vraiment du mal à comprendre ce qu''on lui demande.
    Ne comprend même pas le terme petit dejeuner... un peu limité quand même... '
  - Leur engagement envers la responsabilité sociale est admirable.
  - Trop petit pour un si grand hôtel.
- source_sentence: Par ailleurs impossible de se lever tard, la femme de chambre étant
    sous payée, ne travaille que le matin.
  sentences:
  - On a été vraiment contrariés d’être placés dans cette chambre, c’était une expérience
    très frustrante.
  - L'hôtel fait un excellent travail en matière de gestion durable.
  - Aucun label ou certification n'est affiché pour rassurer sur leurs pratiques durables.
- source_sentence: Cet hôtel n'affiche pas de label environnemental comme Clef verte
    ou GreenGlobe
  sentences:
  - La sécurité est renforcée, ce qui est très rassurant.
  - L'engagement environnemental de ce camping est remarquable.
  - Bonne approche développement durable.
- source_sentence: Petit plus pour le caractère refuge LPO de l’hotel.
  sentences:
  - Je n'ai observé aucune initiative éthique notable dans cet hôtel.
  - Parking pratique avec un bon rapport qualité-prix.
  - L'établissement met en place des protocoles de sécurité au travail qui garantissent
    un environnement sain pour tous
model-index:
- name: MPNet base trained on AllNLI triplets
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli dev
      type: all-nli-dev
    metrics:
    - type: cosine_accuracy
      value: 1.0
      name: Cosine Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: all nli test
      type: all-nli-test
    metrics:
    - type: cosine_accuracy
      value: 1.0
      name: Cosine Accuracy
---

# MPNet base trained on AllNLI triplets

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [pyrac/rse_engagement_des_collaborateurs](https://huggingface.co/pyrac/rse_engagement_des_collaborateurs). 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:** [pyrac/rse_engagement_des_collaborateurs](https://huggingface.co/pyrac/rse_engagement_des_collaborateurs) <!-- at revision 2cc60df0949f5141e2d7185cb0bccc45e5689ecc -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## 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("pyrac/rse_gestion_durable")
# Run inference
sentences = [
    'Petit plus pour le caractère refuge LPO de l’hotel.',
    "L'établissement met en place des protocoles de sécurité au travail qui garantissent un environnement sain pour tous",
    'Parking pratique avec un bon rapport qualité-prix.',
]
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.*
-->

## Evaluation

### Metrics

#### Triplet

* Datasets: `all-nli-dev` and `all-nli-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | all-nli-dev | all-nli-test |
|:--------------------|:------------|:-------------|
| **cosine_accuracy** | **1.0**     | **1.0**      |

<!--
## 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: 132,020 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 22.06 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 19.57 tokens</li><li>max: 81 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.83 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                       | positive                                                                                | negative                                                                                                    |
  |:-----------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------|
  | <code>Engagement RSE palpable, mais trop de règles vertes imposées.</code>                                                   | <code>Les informations sur leurs pratiques responsables sont quasi inexistantes.</code> | <code>Cette chambre était extrêmement décevante, elle ne correspondait absolument pas à nos besoins.</code> |
  | <code>Je suis déçu qu'aucun label environnemental comme Clef verte ne soit visible dans cet hôtel</code>                     | <code>La mise en avant de leurs pratiques éthiques est impressionnante.</code>          | <code>Accès mal indiqué et compliqué.</code>                                                                |
  | <code>Le bien-être des employés est clairement une priorité ici avec des pratiques conformes aux dispositions légales</code> | <code>Ils ne sont pas aussi transparents qu'ils le prétendent.</code>                   | <code>La chambre était trop vieille et usée, ça a gâché notre séjour.</code>                                |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Evaluation Dataset

#### Unnamed Dataset

* Size: 16,502 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          | negative                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | string                                                                            |
  | details | <ul><li>min: 4 tokens</li><li>mean: 21.43 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.94 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.08 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                              | positive                                                                | negative                                                                                                     |
  |:------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------|
  | <code>J'ai trouvé que cet hôtel avec le label Clef verte est un bel exemple d'engagement environnemental</code>                     | <code>personnels non-formés et mal payés, sous-traitance à gogo</code>  | <code>Pas assez d'espace pour les manœuvres, surtout en heures de pointe.</code>                             |
  | <code>Je ne vois pas de résultats concrets de leur engagement écologique.</code>                                                    | <code>L'hôtel manque de transparence sur ses engagements en RSE.</code> | <code>On nous a placé dans une chambre qui ne correspondait vraiment pas à ce que l’on avait réservé.</code> |
  | <code>Les conditions de sécurité au travail sont irréprochables et l'environnement est sain pour les employés et les clients</code> | <code>RSE exemplaire, mais règles environnementales oppressives.</code> | <code>Vraiment déçu d’avoir eu cette chambre, ce n’était pas du tout ce qu’on s’attendait.</code>            |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
  ```json
  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates

#### 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`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_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`: 1
- `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`: True
- `fp16`: False
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | Validation Loss | all-nli-dev_cosine_accuracy | all-nli-test_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|
| 0.0485 | 100  | 4.2915        | 4.1299          | 1.0                         | -                            |
| 0.0969 | 200  | 4.1578        | 4.1253          | 1.0                         | -                            |
| 0.1454 | 300  | 4.1509        | 4.1237          | 1.0                         | -                            |
| 0.1939 | 400  | 4.1465        | 4.1006          | 1.0                         | -                            |
| 0.2424 | 500  | 4.1224        | 4.0881          | 1.0                         | -                            |
| 0.2908 | 600  | 4.1065        | 4.0597          | 1.0                         | -                            |
| 0.3393 | 700  | 4.0901        | 4.0488          | 1.0                         | -                            |
| 0.3878 | 800  | 4.0862        | 4.0355          | 1.0                         | -                            |
| 0.4363 | 900  | 4.0732        | 4.0352          | 1.0                         | -                            |
| 0.4847 | 1000 | 4.0681        | 4.0271          | 1.0                         | -                            |
| 0.5332 | 1100 | 4.0574        | 4.0270          | 1.0                         | -                            |
| 0.5817 | 1200 | 4.0583        | 4.0235          | 1.0                         | -                            |
| 0.6302 | 1300 | 4.0566        | 4.0180          | 1.0                         | -                            |
| 0.6786 | 1400 | 4.048         | 4.0180          | 1.0                         | -                            |
| 0.7271 | 1500 | 4.046         | 4.0105          | 1.0                         | -                            |
| 0.7756 | 1600 | 4.0403        | 4.0128          | 1.0                         | -                            |
| 0.8240 | 1700 | 4.0471        | 4.0084          | 1.0                         | -                            |
| 0.8725 | 1800 | 4.0455        | 4.0082          | 1.0                         | -                            |
| 0.9210 | 1900 | 4.0328        | 4.0051          | 1.0                         | -                            |
| 0.9695 | 2000 | 4.0417        | 4.0033          | 1.0                         | -                            |
| -1     | -1   | -             | -               | -                           | 1.0                          |


### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0

## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

<!--
## 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.*
-->