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:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
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: Les membres de l'équipe ont été attentifs et très professionnels
    dans leur manière de gérer nos besoins.
  sentences:
  - C’était tout à fait correct, mais pas très chaleureux.
  - Une chambre mal située, très loin de tout, et vraiment pas agréable.
  - Le service était moyen, ni excellent ni vraiment mauvais.
- source_sentence: Les employés étaient réactifs et nous ont aidés à chaque étape
    du processus.
  sentences:
  - Leur manque d’implication et d’organisation était frustrant.
  - L’expérience avec le personnel était moyenne, sans surprise.
  - Manque de signalisation dans le parking.
- source_sentence: Les employés ont dépassé nos attentes en termes de professionnalisme
    et de réactivité.
  sentences:
  - Parking mal entretenu, sale et peu accueillant.
  - Nous avons trouvé les collaborateurs peu investis dans leur travail.
  - Le personnel était super gentil et efficace, une vraie bonne surprise.
- source_sentence: Les employés semblaient démotivés et peu impliqués dans leur travail.
  sentences:
  - La chambre n'était pas adaptée à nos attentes, c’était frustrant.
  - L'équipe semblait désorganisée et peu concernée par les besoins des clients.
  - Le service était présent mais manquait de chaleur humaine.
- source_sentence: On aurait aimé plus d’implication de la part des employés, c’était
    moyen.
  sentences:
  - Rien de particulier à dire, le service était correct.
  - On a été forcé d’accepter cette chambre, ce n'était pas du tout ce qu’on avait
    demandé.
  - Le service était assez médiocre, mais pas désastreux non plus.
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 [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). 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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
- **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_engagement_des_collaborateurs")
# Run inference
sentences = [
    'On aurait aimé plus d’implication de la part des employés, c’était moyen.',
    'Le service était assez médiocre, mais pas désastreux non plus.',
    "On a été forcé d’accepter cette chambre, ce n'était pas du tout ce qu’on avait demandé.",
]
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: 11 tokens</li><li>mean: 18.6 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 16.93 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.87 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | anchor                                                                              | positive                                                                                              | negative                                                                           |
  |:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
  | <code>L’équipe était tellement sympa et réactive, c’était vraiment agréable.</code> | <code>Nous avons été agréablement surpris par l’attention portée aux détails par le personnel.</code> | <code>Très bon service de navette depuis le parking.</code>                        |
  | <code>C’était un service classique, ni particulièrement bon ni mauvais.</code>      | <code>Le sourire et la disponibilité des employés ont illuminé notre séjour.</code>                   | <code>Cette chambre nous a totalement déçus, c’était tout sauf confortable.</code> |
  | <code>Le service était correct, rien de plus.</code>                                | <code>Les employés étaient lents et mal organisés, cela a beaucoup gêné notre séjour.</code>          | <code>La sécurité est assurée avec un gardien présent.</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: 10 tokens</li><li>mean: 18.49 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 16.97 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.86 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
  | anchor                                                                              | positive                                                                                  | negative                                                                              |
  |:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | <code>Les employés semblaient démotivés et peu impliqués dans leur travail.</code>  | <code>L'équipe semblait désorganisée et peu concernée par les besoins des clients.</code> | <code>La chambre n'était pas adaptée à nos attentes, c’était frustrant.</code>        |
  | <code>Le service était correct, mais il manquait un peu de chaleur humaine.</code>  | <code>Un service qui n’a pas marqué mais qui reste acceptable.</code>                     | <code>Une chambre que nous n’avions pas demandée, c'était une vraie déception.</code> |
  | <code>Les employés semblaient désintéressés, ça a un peu gâché l’expérience.</code> | <code>Le service était fonctionnel, mais pas très personnalisé.</code>                    | <code>Stationnement facile et rapide, un plaisir.</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  | 5.2586        | 4.1160          | 1.0                         | -                            |
| 0.0969 | 200  | 4.1542        | 4.1071          | 1.0                         | -                            |
| 0.1454 | 300  | 4.1483        | 4.1009          | 1.0                         | -                            |
| 0.1939 | 400  | 4.1327        | 4.0772          | 1.0                         | -                            |
| 0.2424 | 500  | 4.1122        | 4.0561          | 1.0                         | -                            |
| 0.2908 | 600  | 4.1027        | 4.0457          | 1.0                         | -                            |
| 0.3393 | 700  | 4.0877        | 4.0345          | 1.0                         | -                            |
| 0.3878 | 800  | 4.0863        | 4.0216          | 1.0                         | -                            |
| 0.4363 | 900  | 4.0785        | 4.0196          | 1.0                         | -                            |
| 0.4847 | 1000 | 4.0661        | 4.0182          | 1.0                         | -                            |
| 0.5332 | 1100 | 4.0637        | 4.0163          | 1.0                         | -                            |
| 0.5817 | 1200 | 4.0606        | 4.0130          | 1.0                         | -                            |
| 0.6302 | 1300 | 4.0601        | 4.0086          | 1.0                         | -                            |
| 0.6786 | 1400 | 4.0516        | 4.0037          | 1.0                         | -                            |
| 0.7271 | 1500 | 4.0472        | 4.0015          | 1.0                         | -                            |
| 0.7756 | 1600 | 4.0465        | 4.0008          | 1.0                         | -                            |
| 0.8240 | 1700 | 4.0421        | 4.0007          | 1.0                         | -                            |
| 0.8725 | 1800 | 4.0463        | 3.9944          | 1.0                         | -                            |
| 0.9210 | 1900 | 4.035         | 3.9919          | 1.0                         | -                            |
| 0.9695 | 2000 | 4.0408        | 3.9909          | 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.*
-->