sentence-transformers safetensors camembert sentence-similarity feature-extraction generated_from_trainer dataset_size:5749 loss:CoSENTLoss de en es fr it nl pl pt ru zh dataset:PhilipMay/stsb_multi_mt arxiv:1908.10084 base_model:ymelka/camembert-cosmetic-finetuned base_model:finetune:ymelka/camembert-cosmetic-finetuned model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: ymelka/camembert-cosmetic-finetuned
datasets:
- PhilipMay/stsb_multi_mt
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:5749
- loss:CoSENTLoss
widget:
- source_sentence: Nous nous déplaçons "... par rapport au cadre de repos cosmique
    en mouvement ... à environ 371 km/s vers la constellation du Lion".
  sentences:
  - La dame a fait frire la viande panée dans de l'huile chaude.
  - Il n'y a pas d'alambic qui ne soit pas relatif à un autre objet.
  - Le joueur de basket-ball est sur le point de marquer des points pour son équipe.
- source_sentence: Le professeur Burkhauser a effectué des recherches approfondies
    sur les personnes qui sont pénalisées par l'augmentation du salaire minimum.
  sentences:
  - Un adolescent parle à une fille par le biais d'une webcam.
  - Une femme est en train de couper des oignons verts.
  - Les lois sur le salaire minimum nuisent le plus aux personnes les moins qualifiées
    et les moins productives.
- source_sentence: Bien que le terme "reine" puisse faire référence à la fois à la
    reine régente (souveraine) ou à la reine consort, le roi a toujours été le souverain.
  sentences:
  - Des moutons paissent dans le champ devant une rangée d'arbres.
  - Il y a une très bonne raison de ne pas appeler le conjoint de la Reine "Roi" -
    parce qu'il n'est pas le Roi.
  - Un groupe de personnes âgées pose autour d'une table à manger.
- source_sentence: Deux pygargues à tête blanche perchés sur une branche.
  sentences:
  - Un groupe de militaires joue dans un quintette de cuivres.
  - Deux aigles sont perchés sur une branche.
  - Un homme qui joue de la guitare sous la pluie.
- source_sentence: Un homme joue de la guitare.
  sentences:
  - Il est possible qu'un système solaire comme le nôtre existe en dehors d'une galaxie.
  - Un homme joue de la flûte.
  - Un homme est en train de manger une banane.
model-index:
- name: SentenceTransformer based on ymelka/camembert-cosmetic-finetuned
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: stsb fr dev
      type: stsb-fr-dev
    metrics:
    - type: pearson_cosine
      value: 0.6401461834329478
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.6661576168424006
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.7077411059971963
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.7104395816607704
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.6183470655093759
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.6339424060254548
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.18614455072383299
      name: Pearson Dot
    - type: spearman_dot
      value: 0.21677402345623561
      name: Spearman Dot
    - type: pearson_max
      value: 0.7077411059971963
      name: Pearson Max
    - type: spearman_max
      value: 0.7104395816607704
      name: Spearman Max
    - type: pearson_cosine
      value: 0.834390325106948
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8564941342147334
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8518548236293758
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.854193303324745
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8541012365072966
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8555434573522197
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4989804086580052
      name: Pearson Dot
    - type: spearman_dot
      value: 0.5094008186566353
      name: Spearman Dot
    - type: pearson_max
      value: 0.8541012365072966
      name: Pearson Max
    - type: spearman_max
      value: 0.8564941342147334
      name: Spearman Max
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: stsb fr test
      type: stsb-fr-test
    metrics:
    - type: pearson_cosine
      value: 0.7979696368103
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8219240068315988
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.8237827107867745
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8221440625680553
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.8230384709547542
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8218369251066925
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.4089365107737232
      name: Pearson Dot
    - type: spearman_dot
      value: 0.4588995887587045
      name: Spearman Dot
    - type: pearson_max
      value: 0.8237827107867745
      name: Pearson Max
    - type: spearman_max
      value: 0.8221440625680553
      name: Spearman Max
---

# SentenceTransformer based on ymelka/camembert-cosmetic-finetuned

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [ymelka/camembert-cosmetic-finetuned](https://huggingface.co/ymelka/camembert-cosmetic-finetuned) on the [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) dataset. 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:** [ymelka/camembert-cosmetic-finetuned](https://huggingface.co/ymelka/camembert-cosmetic-finetuned) <!-- at revision cd4cb90f9388340c5f02740130efd30336c08905 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- **Languages:** de, en, es, fr, it, nl, pl, pt, ru, zh
<!-- - **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': 512, 'do_lower_case': False}) with Transformer model: CamembertModel 
  (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("ymelka/camembert-cosmetic-similarity")
# Run inference
sentences = [
    'Un homme joue de la guitare.',
    'Un homme est en train de manger une banane.',
    'Un homme joue de la flûte.',
]
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]
```

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## Evaluation

### Metrics

#### Semantic Similarity
* Dataset: `stsb-fr-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.6401     |
| **spearman_cosine** | **0.6662** |
| pearson_manhattan   | 0.7077     |
| spearman_manhattan  | 0.7104     |
| pearson_euclidean   | 0.6183     |
| spearman_euclidean  | 0.6339     |
| pearson_dot         | 0.1861     |
| spearman_dot        | 0.2168     |
| pearson_max         | 0.7077     |
| spearman_max        | 0.7104     |

#### Semantic Similarity
* Dataset: `stsb-fr-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.8344     |
| **spearman_cosine** | **0.8565** |
| pearson_manhattan   | 0.8519     |
| spearman_manhattan  | 0.8542     |
| pearson_euclidean   | 0.8541     |
| spearman_euclidean  | 0.8555     |
| pearson_dot         | 0.499      |
| spearman_dot        | 0.5094     |
| pearson_max         | 0.8541     |
| spearman_max        | 0.8565     |

#### Semantic Similarity
* Dataset: `stsb-fr-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| pearson_cosine      | 0.798      |
| **spearman_cosine** | **0.8219** |
| pearson_manhattan   | 0.8238     |
| spearman_manhattan  | 0.8221     |
| pearson_euclidean   | 0.823      |
| spearman_euclidean  | 0.8218     |
| pearson_dot         | 0.4089     |
| spearman_dot        | 0.4589     |
| pearson_max         | 0.8238     |
| spearman_max        | 0.8221     |

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## Training Details

### Training Dataset

#### PhilipMay/stsb_multi_mt

* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                         | score                                                         |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                           | string                                                                            | float                                                         |
  | details | <ul><li>min: 6 tokens</li><li>mean: 11.1 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.04 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.7</li><li>max: 5.0</li></ul> |
* Samples:
  | sentence1                                                  | sentence2                                                            | score                          |
  |:-----------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------|
  | <code>Un avion est en train de décoller.</code>            | <code>Un avion est en train de décoller.</code>                      | <code>5.0</code>               |
  | <code>Un homme joue d'une grande flûte.</code>             | <code>Un homme joue de la flûte.</code>                              | <code>3.799999952316284</code> |
  | <code>Un homme étale du fromage râpé sur une pizza.</code> | <code>Un homme étale du fromage râpé sur une pizza non cuite.</code> | <code>3.799999952316284</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

#### PhilipMay/stsb_multi_mt

* Dataset: [PhilipMay/stsb_multi_mt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | score                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                          |
  | details | <ul><li>min: 6 tokens</li><li>mean: 17.45 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.35 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.36</li><li>max: 5.0</li></ul> |
* Samples:
  | sentence1                                                                | sentence2                                                                   | score             |
  |:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:------------------|
  | <code>Un homme avec un casque de sécurité est en train de danser.</code> | <code>Un homme portant un casque de sécurité est en train de danser.</code> | <code>5.0</code>  |
  | <code>Un jeune enfant monte à cheval.</code>                             | <code>Un enfant monte à cheval.</code>                                      | <code>4.75</code> |
  | <code>Un homme donne une souris à un serpent.</code>                     | <code>L'homme donne une souris au serpent.</code>                           | <code>5.0</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
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `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`: 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`: 2e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `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`: 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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | stsb-fr-dev_spearman_cosine | stsb-fr-test_spearman_cosine |
|:------:|:----:|:-------------:|:------:|:---------------------------:|:----------------------------:|
| 0      | 0    | -             | -      | 0.6661                      | -                            |
| 0.2778 | 100  | 4.9452        | 4.4417 | 0.7733                      | -                            |
| 0.5556 | 200  | 4.667         | 4.4273 | 0.7986                      | -                            |
| 0.8333 | 300  | 4.4904        | 4.3058 | 0.8338                      | -                            |
| 1.1111 | 400  | 4.1679        | 4.2723 | 0.8491                      | -                            |
| 1.3889 | 500  | 4.138         | 4.3575 | 0.8464                      | -                            |
| 1.6667 | 600  | 4.5737        | 4.3427 | 0.8479                      | -                            |
| 1.9444 | 700  | 4.3086        | 4.4455 | 0.8510                      | -                            |
| 2.2222 | 800  | 3.8711        | 4.4135 | 0.8590                      | -                            |
| 2.5    | 900  | 4.064         | 4.4775 | 0.8567                      | -                            |
| 2.7778 | 1000 | 4.2255        | 4.4733 | 0.8565                      | -                            |
| 3.0    | 1080 | -             | -      | -                           | 0.8219                       |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.2
- 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},
}
```

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