README.md

---
base_model: sentence-transformers/stsb-xlm-r-multilingual
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:15642
- loss:CosineSimilarityLoss
widget:
- source_sentence: Certificat d'admission universitaire
  sentences:
  - شهادة القبول الجامعي
  - شهادة الحياة الفردية
  - Film Production Center Creation Permit
- source_sentence: دبلوم الدراسات الجامعية التقنية
  sentences:
  - Wind Energy Equipment Registration Certificate
  - Industrial Safety Standards Compliance Certificate
  - Virtual Reality Technologies Import License
- source_sentence: شهادة المطابقة للمعايير المغربية
  sentences:
  - Certificate of Good Conduct
  - Commercial Lease Contract
  - Marriage Contract Document
- source_sentence: رخصة استغلال مركز دراسات الطاقة المتجددة
  sentences:
  - Permis d'importation de matériel médical
  - Permis d'exploitation d'un centre d'études des énergies renouvelables
  - Autorisation d'exercer une activité dans le domaine des énergies renouvelables
- source_sentence: Certificat de qualification en conception de systèmes intelligents
  sentences:
  - شهادة فحص منشآت الطاقة
  - Permis de création d'une centrale électrique éolienne
  - رخصة صناعية
model-index:
- name: SentenceTransformer based on sentence-transformers/stsb-xlm-r-multilingual
  results:
  - task:
      type: semantic-similarity
      name: Semantic Similarity
    dataset:
      name: eval
      type: eval
    metrics:
    - type: pearson_cosine
      value: 0.9932857106529867
      name: Pearson Cosine
    - type: spearman_cosine
      value: 0.8659282642227534
      name: Spearman Cosine
    - type: pearson_manhattan
      value: 0.9872002912590794
      name: Pearson Manhattan
    - type: spearman_manhattan
      value: 0.8659382004848898
      name: Spearman Manhattan
    - type: pearson_euclidean
      value: 0.9873391899791255
      name: Pearson Euclidean
    - type: spearman_euclidean
      value: 0.8659392197992224
      name: Spearman Euclidean
    - type: pearson_dot
      value: 0.9762599450100259
      name: Pearson Dot
    - type: spearman_dot
      value: 0.8656650063924476
      name: Spearman Dot
    - type: pearson_max
      value: 0.9932857106529867
      name: Pearson Max
    - type: spearman_max
      value: 0.8659392197992224
      name: Spearman Max
---

# SentenceTransformer based on sentence-transformers/stsb-xlm-r-multilingual

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual). 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/stsb-xlm-r-multilingual](https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual) <!-- at revision e33c331c9f771a2d5ee0b434a970d22281e3fc3e -->
- **Maximum Sequence Length:** 128 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': 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("amahdaouy/xlmrsim-mar_2ep")
# Run inference
sentences = [
    'Certificat de qualification en conception de systèmes intelligents',
    'رخصة صناعية',
    "Permis de création d'une centrale électrique éolienne",
]
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

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

| Metric             | Value      |
|:-------------------|:-----------|
| pearson_cosine     | 0.9933     |
| spearman_cosine    | 0.8659     |
| pearson_manhattan  | 0.9872     |
| spearman_manhattan | 0.8659     |
| pearson_euclidean  | 0.9873     |
| spearman_euclidean | 0.8659     |
| pearson_dot        | 0.9763     |
| spearman_dot       | 0.8657     |
| pearson_max        | 0.9933     |
| **spearman_max**   | **0.8659** |

<!--
## 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: 15,642 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                        | sentence_1                                                                        | label                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            | float                                                         |
  | details | <ul><li>min: 4 tokens</li><li>mean: 10.38 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.47 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul> |
* Samples:
  | sentence_0                                                                      | sentence_1                                                                    | label            |
  |:--------------------------------------------------------------------------------|:------------------------------------------------------------------------------|:-----------------|
  | <code>License to Produce and Distribute TV Programs</code>                      | <code>Licence de production et de distribution de programmes télévisés</code> | <code>1.0</code> |
  | <code>Certificat de qualification en conception de systèmes intelligents</code> | <code>رخصة صناعية</code>                                                      | <code>0.0</code> |
  | <code>عقد الشراء المشترك</code>                                                 | <code>Shared Purchase Act</code>                                              | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
  ```json
  {
      "loss_fct": "torch.nn.modules.loss.MSELoss"
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 2
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 32
- `per_device_eval_batch_size`: 32
- `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
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `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`: 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
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | Training Loss | eval_spearman_max |
|:------:|:----:|:-------------:|:-----------------:|
| 0.2045 | 100  | -             | 0.8638            |
| 0.4090 | 200  | -             | 0.8651            |
| 0.6135 | 300  | -             | 0.8655            |
| 0.8180 | 400  | -             | 0.8659            |
| 1.0    | 489  | -             | 0.8659            |
| 1.0225 | 500  | 0.0221        | 0.8659            |
| 1.2270 | 600  | -             | 0.8659            |
| 1.4315 | 700  | -             | 0.8660            |
| 1.6360 | 800  | -             | 0.8659            |
| 1.8405 | 900  | -             | 0.8659            |
| 2.0    | 978  | -             | 0.8659            |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.1.0
- Transformers: 4.44.2
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.0
- 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",
}
```

<!--
## 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.*
-->