---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:17702
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
widget:
- source_sentence: zonivan 100 mg 20 caps.
  sentences:
  - fuci-top 2% cream 15 gm
  - فاستاتينال 10/40مجم 7 قرص
  - زونيفان 100 مجم 20 كبسولة
- source_sentence: celeborg 100 mg 10 cap
  sentences:
  - celeborg 100mg 10 caps.
  - هيدرو زد كريم 25 جم
  - كومبليت زيت للشعر 60 مل
- source_sentence: jedcoflacin 200 mg 20 f.c.tab.
  sentences:
  - فوتوديرم ماكس حليب للجسم 100 اس بي اف 100 مل
  - zocozet 10/20mg 14 f.c. tab.
  - جيدكوفلاسين 200 مجم 20 قرص
- source_sentence: isolift cream 30ml
  sentences:
  - ايزوليفت كريم 30 مل
  - linezolid 600mg 7 f.c. tab.
  - تينسريليف 400 مجم 28 قرص
- source_sentence: stress formula 20 capsules
  sentences:
  - كورتيكوفيوسيديك كريم موضعي 30 جم
  - ستريس فورميولا 20 كبسول
  - silden 25 mg 10 f.c.tab.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 86741b4e3f5cb7765a600d3a3d55a0f6a6cb443d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **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: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("mohsayed/para_tr_enar_1")
# Run inference
sentences = [
    'stress formula 20 capsules',
    'ستريس فورميولا 20 كبسول',
    'كورتيكوفيوسيديك كريم موضعي 30 جم',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

</details>
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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 17,702 training samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 10.29 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.42 tokens</li><li>max: 25 tokens</li></ul> |
* Samples:
  | sentence1                                                 | sentence2                                               |
  |:----------------------------------------------------------|:--------------------------------------------------------|
  | <code>azelast plus 125 / 50 mcg nasal spray 25 ml</code>  | <code>azelast plus 125/50 mcg nasal spray 25 ml</code>  |
  | <code>ticanase plus 125 / 50 mcg nasal spray 15 ml</code> | <code>ticanase plus 125/50 mcg nasal spray 15 ml</code> |
  | <code>nasostop 0.1% adult nasal drops 15 ml</code>        | <code>nasostop 0.1%  adult nasal drops 15 ml</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: 1,771 evaluation samples
* Columns: <code>sentence1</code> and <code>sentence2</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 12.13 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 12.44 tokens</li><li>max: 26 tokens</li></ul> |
* Samples:
  | sentence1                                                 | sentence2                                    |
  |:----------------------------------------------------------|:---------------------------------------------|
  | <code>calcibella fortified liquid chocolate 200 gm</code> | <code>كالسيبيلا شيكولاته سائلة 200 جم</code> |
  | <code>glaryl 4 mg 30 tab</code>                           | <code>glaryl 4mg 30 tab.</code>              |
  | <code>pixefresh mouth spray 60 ml</code>                  | <code>بيكسيفريش بخاخ للفم 60 مل</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`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 15
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: 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
- `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`: 15
- `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`: True
- `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}
- `tp_size`: 0
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch       | Step      | Training Loss | Validation Loss |
|:-----------:|:---------:|:-------------:|:---------------:|
| 0.0903      | 100       | 1.123         | -               |
| 0.1807      | 200       | 0.2605        | -               |
| 0.2710      | 300       | 0.1432        | -               |
| 0.3613      | 400       | 0.1151        | -               |
| 0.4517      | 500       | 0.09          | -               |
| 0.5420      | 600       | 0.0666        | -               |
| 0.6323      | 700       | 0.0534        | -               |
| 0.7227      | 800       | 0.0593        | -               |
| 0.8130      | 900       | 0.0484        | -               |
| 0.9033      | 1000      | 0.0652        | 0.0302          |
| 0.9937      | 1100      | 0.0441        | -               |
| 1.0840      | 1200      | 0.0333        | -               |
| 1.1743      | 1300      | 0.0395        | -               |
| 1.2647      | 1400      | 0.0357        | -               |
| 1.3550      | 1500      | 0.0351        | -               |
| 1.4453      | 1600      | 0.0338        | -               |
| 1.5357      | 1700      | 0.0365        | -               |
| 1.6260      | 1800      | 0.0518        | -               |
| 1.7164      | 1900      | 0.0426        | -               |
| 1.8067      | 2000      | 0.0312        | 0.0234          |
| 1.8970      | 2100      | 0.041         | -               |
| 1.9874      | 2200      | 0.0401        | -               |
| 2.0777      | 2300      | 0.0177        | -               |
| 2.1680      | 2400      | 0.0216        | -               |
| 2.2584      | 2500      | 0.0203        | -               |
| 2.3487      | 2600      | 0.0184        | -               |
| 2.4390      | 2700      | 0.0203        | -               |
| 2.5294      | 2800      | 0.024         | -               |
| 2.6197      | 2900      | 0.0154        | -               |
| 2.7100      | 3000      | 0.0292        | 0.0147          |
| 2.8004      | 3100      | 0.025         | -               |
| 2.8907      | 3200      | 0.02          | -               |
| 2.9810      | 3300      | 0.0187        | -               |
| 3.0714      | 3400      | 0.0264        | -               |
| 3.1617      | 3500      | 0.0153        | -               |
| 3.2520      | 3600      | 0.01          | -               |
| 3.3424      | 3700      | 0.0156        | -               |
| 3.4327      | 3800      | 0.014         | -               |
| 3.5230      | 3900      | 0.027         | -               |
| 3.6134      | 4000      | 0.014         | 0.0093          |
| 3.7037      | 4100      | 0.0134        | -               |
| 3.7940      | 4200      | 0.0127        | -               |
| 3.8844      | 4300      | 0.0223        | -               |
| 3.9747      | 4400      | 0.0137        | -               |
| 4.0650      | 4500      | 0.01          | -               |
| 4.1554      | 4600      | 0.0135        | -               |
| 4.2457      | 4700      | 0.0082        | -               |
| 4.3360      | 4800      | 0.013         | -               |
| 4.4264      | 4900      | 0.0075        | -               |
| 4.5167      | 5000      | 0.0064        | 0.0060          |
| 4.6070      | 5100      | 0.0113        | -               |
| 4.6974      | 5200      | 0.0109        | -               |
| 4.7877      | 5300      | 0.0116        | -               |
| 4.8780      | 5400      | 0.0105        | -               |
| 4.9684      | 5500      | 0.0074        | -               |
| 5.0587      | 5600      | 0.0084        | -               |
| 5.1491      | 5700      | 0.0111        | -               |
| 5.2394      | 5800      | 0.0027        | -               |
| 5.3297      | 5900      | 0.0066        | -               |
| 5.4201      | 6000      | 0.0064        | 0.0045          |
| 5.5104      | 6100      | 0.0044        | -               |
| 5.6007      | 6200      | 0.0096        | -               |
| 5.6911      | 6300      | 0.0065        | -               |
| 5.7814      | 6400      | 0.0093        | -               |
| 5.8717      | 6500      | 0.0136        | -               |
| 5.9621      | 6600      | 0.0214        | -               |
| 6.0524      | 6700      | 0.0054        | -               |
| 6.1427      | 6800      | 0.0028        | -               |
| 6.2331      | 6900      | 0.008         | -               |
| 6.3234      | 7000      | 0.0115        | 0.0021          |
| 6.4137      | 7100      | 0.0045        | -               |
| 6.5041      | 7200      | 0.0053        | -               |
| 6.5944      | 7300      | 0.0083        | -               |
| 6.6847      | 7400      | 0.0081        | -               |
| 6.7751      | 7500      | 0.0035        | -               |
| 6.8654      | 7600      | 0.0081        | -               |
| 6.9557      | 7700      | 0.0063        | -               |
| 7.0461      | 7800      | 0.0056        | -               |
| 7.1364      | 7900      | 0.0034        | -               |
| 7.2267      | 8000      | 0.0069        | 0.0025          |
| 7.3171      | 8100      | 0.0026        | -               |
| 7.4074      | 8200      | 0.0047        | -               |
| 7.4977      | 8300      | 0.0034        | -               |
| 7.5881      | 8400      | 0.0052        | -               |
| 7.6784      | 8500      | 0.0081        | -               |
| 7.7687      | 8600      | 0.0023        | -               |
| 7.8591      | 8700      | 0.004         | -               |
| 7.9494      | 8800      | 0.004         | -               |
| 8.0397      | 8900      | 0.003         | -               |
| 8.1301      | 9000      | 0.0032        | 0.0031          |
| 8.2204      | 9100      | 0.0054        | -               |
| 8.3107      | 9200      | 0.0058        | -               |
| 8.4011      | 9300      | 0.0044        | -               |
| 8.4914      | 9400      | 0.0029        | -               |
| 8.5818      | 9500      | 0.0039        | -               |
| 8.6721      | 9600      | 0.0033        | -               |
| 8.7624      | 9700      | 0.0061        | -               |
| 8.8528      | 9800      | 0.0029        | -               |
| 8.9431      | 9900      | 0.0037        | -               |
| 9.0334      | 10000     | 0.0024        | 0.0020          |
| 9.1238      | 10100     | 0.0046        | -               |
| 9.2141      | 10200     | 0.0037        | -               |
| 9.3044      | 10300     | 0.0041        | -               |
| 9.3948      | 10400     | 0.0064        | -               |
| 9.4851      | 10500     | 0.0058        | -               |
| 9.5754      | 10600     | 0.0058        | -               |
| 9.6658      | 10700     | 0.0031        | -               |
| 9.7561      | 10800     | 0.0015        | -               |
| 9.8464      | 10900     | 0.0037        | -               |
| 9.9368      | 11000     | 0.0045        | 0.0013          |
| 10.0271     | 11100     | 0.0038        | -               |
| 10.1174     | 11200     | 0.0027        | -               |
| 10.2078     | 11300     | 0.0061        | -               |
| 10.2981     | 11400     | 0.0046        | -               |
| 10.3884     | 11500     | 0.0028        | -               |
| 10.4788     | 11600     | 0.0021        | -               |
| 10.5691     | 11700     | 0.0029        | -               |
| 10.6594     | 11800     | 0.005         | -               |
| 10.7498     | 11900     | 0.002         | -               |
| 10.8401     | 12000     | 0.0058        | 0.0012          |
| 10.9304     | 12100     | 0.003         | -               |
| 11.0208     | 12200     | 0.0005        | -               |
| 11.1111     | 12300     | 0.0022        | -               |
| 11.2014     | 12400     | 0.0046        | -               |
| 11.2918     | 12500     | 0.0028        | -               |
| 11.3821     | 12600     | 0.0016        | -               |
| 11.4724     | 12700     | 0.0026        | -               |
| 11.5628     | 12800     | 0.0025        | -               |
| 11.6531     | 12900     | 0.0009        | -               |
| 11.7435     | 13000     | 0.0022        | 0.0014          |
| 11.8338     | 13100     | 0.0021        | -               |
| 11.9241     | 13200     | 0.0018        | -               |
| 12.0145     | 13300     | 0.0032        | -               |
| 12.1048     | 13400     | 0.0024        | -               |
| 12.1951     | 13500     | 0.0029        | -               |
| 12.2855     | 13600     | 0.0009        | -               |
| 12.3758     | 13700     | 0.0009        | -               |
| 12.4661     | 13800     | 0.002         | -               |
| 12.5565     | 13900     | 0.0026        | -               |
| 12.6468     | 14000     | 0.0008        | 0.0011          |
| 12.7371     | 14100     | 0.0016        | -               |
| 12.8275     | 14200     | 0.0012        | -               |
| 12.9178     | 14300     | 0.0009        | -               |
| 13.0081     | 14400     | 0.0013        | -               |
| 13.0985     | 14500     | 0.0013        | -               |
| 13.1888     | 14600     | 0.004         | -               |
| 13.2791     | 14700     | 0.0006        | -               |
| 13.3695     | 14800     | 0.0025        | -               |
| 13.4598     | 14900     | 0.0004        | -               |
| 13.5501     | 15000     | 0.0021        | 0.0010          |
| 13.6405     | 15100     | 0.0023        | -               |
| 13.7308     | 15200     | 0.0054        | -               |
| 13.8211     | 15300     | 0.0014        | -               |
| 13.9115     | 15400     | 0.0028        | -               |
| 14.0018     | 15500     | 0.0008        | -               |
| 14.0921     | 15600     | 0.0006        | -               |
| 14.1825     | 15700     | 0.0015        | -               |
| 14.2728     | 15800     | 0.0004        | -               |
| 14.3631     | 15900     | 0.005         | -               |
| **14.4535** | **16000** | **0.0009**    | **0.0011**      |
| 14.5438     | 16100     | 0.0022        | -               |
| 14.6341     | 16200     | 0.0015        | -               |
| 14.7245     | 16300     | 0.0021        | -               |
| 14.8148     | 16400     | 0.0012        | -               |
| 14.9051     | 16500     | 0.0005        | -               |
| 14.9955     | 16600     | 0.0019        | -               |

* The bold row denotes the saved checkpoint.
</details>

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.0.2
- Transformers: 4.50.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- Tokenizers: 0.21.1

## 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",
}
```

#### 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}
}
```

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