README.md

---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- dot_accuracy
- dot_accuracy_threshold
- dot_f1
- dot_f1_threshold
- dot_precision
- dot_recall
- dot_ap
- manhattan_accuracy
- manhattan_accuracy_threshold
- manhattan_f1
- manhattan_f1_threshold
- manhattan_precision
- manhattan_recall
- manhattan_ap
- euclidean_accuracy
- euclidean_accuracy_threshold
- euclidean_f1
- euclidean_f1_threshold
- euclidean_precision
- euclidean_recall
- euclidean_ap
- max_accuracy
- max_accuracy_threshold
- max_f1
- max_f1_threshold
- max_precision
- max_recall
- max_ap
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:410745
- loss:ContrastiveLoss
widget:
- source_sentence: وینچ
  sentences:
  - ترقه شکلاتی ( هفت ترقه ) ناریه پارس درجه 1 بسته 15 عددی ترقه شکلاتی ( هفت ترقه
    ) ناریه پارس درجه 1 بسته 15 عددی 10عدد ناریه ترقه شکلاتی هفت ترقه بار تازه بدون
    رطوبت وخرابی مارک معتبر نورافشانی
  - پارچه میکرو کجراه
  - Car winch-1500LBS-KARA وینچ خودرو آفرود ۶۸۰ کیلوگرم کارا ۱۵۰۰lbs وینچ خودرویی
    (جلو ماشینی) 1500LBS کارا (KARA)
- source_sentence: ' وسپا '
  sentences:
  - پولوشرت زرد وسپا
  - دوچرخه بند سقفی  لیفان X70 ایکس 70 آلومینیومی طرح منابو
  - دوچرخه ویوا Oxygen سایز 26 دوچرخه 26 ويوا OXYGEN دوچرخه کوهستان ویوا مدل OXYGEN
    سایز 26
- source_sentence: دوچرخه المپیا سایز 27 5
  sentences:
  - دوچرخه شهری المپیا کد 16220 سایز 16 دوچرخه شهری المپیا کد 16220 سایز 16 دوچرخه
    المپیا کد 16220 سایز 16 - OLYMPIA
  - لامپ اس ام دی خودرو مدل 8B بسته 2 عددی
  - قیمت کمپرس سنج موتور
- source_sentence: دچرخه ی
  sentences:
  - هیدروفیشیال ۷ کاره نیوفیس پلاس متور سنگین ۲۰۲۲
  - جامدادی کیوت
  - جعبه ی کادو ی رنگی
- source_sentence: هایومکس
  sentences:
  - انگشتر حدید صینی کد2439
  - ژل هایومکس ولومایزر 2 سی سی
  - دزدگیر پاناتک مدل P-CA501 دزدگیر پاناتک P-CA501-2 دزدگیر پاناتک مدل P-CA501-2
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy
      value: 0.8396327702184535
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7623803019523621
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.8951804502771806
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7234876751899719
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.8454428891975638
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9511359538406059
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9296495014804667
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.8127916913166371
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 18.16492462158203
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.8798154233377613
      name: Dot F1
    - type: dot_f1_threshold
      value: 17.596263885498047
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.82272025942101
      name: Dot Precision
    - type: dot_recall
      value: 0.9454261329486717
      name: Dot Recall
    - type: dot_ap
      value: 0.9138496334192171
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.8362584631565109
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 56.61064910888672
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.892930089729684
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 60.147003173828125
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.8403818109505502
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9524882798413271
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9274603777518026
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.8366528626832315
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 3.691666603088379
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.8933491652479936
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 3.691666603088379
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.8525051194539249
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9383038826782065
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9275301813554955
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.8396327702184535
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 56.61064910888672
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.8951804502771806
      name: Max F1
    - type: max_f1_threshold
      value: 60.147003173828125
      name: Max F1 Threshold
    - type: max_precision
      value: 0.8525051194539249
      name: Max Precision
    - type: max_recall
      value: 0.9524882798413271
      name: Max Recall
    - type: max_ap
      value: 0.9296495014804667
      name: Max Ap
    - type: cosine_accuracy
      value: 0.831416113411775
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7449432611465454
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.8897548675482456
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7427525520324707
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.8502039810530351
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9331650438754658
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9252554285491397
      name: Cosine Ap
    - type: dot_accuracy
      value: 0.8083437410986218
      name: Dot Accuracy
    - type: dot_accuracy_threshold
      value: 18.16763687133789
      name: Dot Accuracy Threshold
    - type: dot_f1
      value: 0.8761684843089249
      name: Dot F1
    - type: dot_f1_threshold
      value: 17.106109619140625
      name: Dot F1 Threshold
    - type: dot_precision
      value: 0.8156272661348803
      name: Dot Precision
    - type: dot_recall
      value: 0.9464178386825339
      name: Dot Recall
    - type: dot_ap
      value: 0.9078782883891188
      name: Dot Ap
    - type: manhattan_accuracy
      value: 0.827735051162383
      name: Manhattan Accuracy
    - type: manhattan_accuracy_threshold
      value: 53.94535446166992
      name: Manhattan Accuracy Threshold
    - type: manhattan_f1
      value: 0.887467671202069
      name: Manhattan F1
    - type: manhattan_f1_threshold
      value: 59.66460418701172
      name: Manhattan F1 Threshold
    - type: manhattan_precision
      value: 0.8336590260906306
      name: Manhattan Precision
    - type: manhattan_recall
      value: 0.9487017670393076
      name: Manhattan Recall
    - type: manhattan_ap
      value: 0.9230969972500983
      name: Manhattan Ap
    - type: euclidean_accuracy
      value: 0.8274282959749337
      name: Euclidean Accuracy
    - type: euclidean_accuracy_threshold
      value: 3.4869043827056885
      name: Euclidean Accuracy Threshold
    - type: euclidean_f1
      value: 0.8874656133173449
      name: Euclidean F1
    - type: euclidean_f1_threshold
      value: 3.7965426445007324
      name: Euclidean F1 Threshold
    - type: euclidean_precision
      value: 0.8363423648594751
      name: Euclidean Precision
    - type: euclidean_recall
      value: 0.9452458228152422
      name: Euclidean Recall
    - type: euclidean_ap
      value: 0.9231713715918721
      name: Euclidean Ap
    - type: max_accuracy
      value: 0.831416113411775
      name: Max Accuracy
    - type: max_accuracy_threshold
      value: 53.94535446166992
      name: Max Accuracy Threshold
    - type: max_f1
      value: 0.8897548675482456
      name: Max F1
    - type: max_f1_threshold
      value: 59.66460418701172
      name: Max F1 Threshold
    - type: max_precision
      value: 0.8502039810530351
      name: Max Precision
    - type: max_recall
      value: 0.9487017670393076
      name: Max Recall
    - type: max_ap
      value: 0.9252554285491397
      name: Max Ap
---

# 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 bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 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: 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("DashReza7/sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2_FINETUNED_on_torob_data_v4")
# Run inference
sentences = [
    'هایومکس',
    'ژل هایومکس ولومایزر 2 سی سی',
    'دزدگیر پاناتک مدل P-CA501 دزدگیر پاناتک P-CA501-2 دزدگیر پاناتک مدل P-CA501-2',
]
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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### Out-of-Scope Use

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## Evaluation

### Metrics

#### Binary Classification

* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.8396     |
| cosine_accuracy_threshold    | 0.7624     |
| cosine_f1                    | 0.8952     |
| cosine_f1_threshold          | 0.7235     |
| cosine_precision             | 0.8454     |
| cosine_recall                | 0.9511     |
| cosine_ap                    | 0.9296     |
| dot_accuracy                 | 0.8128     |
| dot_accuracy_threshold       | 18.1649    |
| dot_f1                       | 0.8798     |
| dot_f1_threshold             | 17.5963    |
| dot_precision                | 0.8227     |
| dot_recall                   | 0.9454     |
| dot_ap                       | 0.9138     |
| manhattan_accuracy           | 0.8363     |
| manhattan_accuracy_threshold | 56.6106    |
| manhattan_f1                 | 0.8929     |
| manhattan_f1_threshold       | 60.147     |
| manhattan_precision          | 0.8404     |
| manhattan_recall             | 0.9525     |
| manhattan_ap                 | 0.9275     |
| euclidean_accuracy           | 0.8367     |
| euclidean_accuracy_threshold | 3.6917     |
| euclidean_f1                 | 0.8933     |
| euclidean_f1_threshold       | 3.6917     |
| euclidean_precision          | 0.8525     |
| euclidean_recall             | 0.9383     |
| euclidean_ap                 | 0.9275     |
| max_accuracy                 | 0.8396     |
| max_accuracy_threshold       | 56.6106    |
| max_f1                       | 0.8952     |
| max_f1_threshold             | 60.147     |
| max_precision                | 0.8525     |
| max_recall                   | 0.9525     |
| **max_ap**                   | **0.9296** |

#### Binary Classification

* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

| Metric                       | Value      |
|:-----------------------------|:-----------|
| cosine_accuracy              | 0.8314     |
| cosine_accuracy_threshold    | 0.7449     |
| cosine_f1                    | 0.8898     |
| cosine_f1_threshold          | 0.7428     |
| cosine_precision             | 0.8502     |
| cosine_recall                | 0.9332     |
| cosine_ap                    | 0.9253     |
| dot_accuracy                 | 0.8083     |
| dot_accuracy_threshold       | 18.1676    |
| dot_f1                       | 0.8762     |
| dot_f1_threshold             | 17.1061    |
| dot_precision                | 0.8156     |
| dot_recall                   | 0.9464     |
| dot_ap                       | 0.9079     |
| manhattan_accuracy           | 0.8277     |
| manhattan_accuracy_threshold | 53.9454    |
| manhattan_f1                 | 0.8875     |
| manhattan_f1_threshold       | 59.6646    |
| manhattan_precision          | 0.8337     |
| manhattan_recall             | 0.9487     |
| manhattan_ap                 | 0.9231     |
| euclidean_accuracy           | 0.8274     |
| euclidean_accuracy_threshold | 3.4869     |
| euclidean_f1                 | 0.8875     |
| euclidean_f1_threshold       | 3.7965     |
| euclidean_precision          | 0.8363     |
| euclidean_recall             | 0.9452     |
| euclidean_ap                 | 0.9232     |
| max_accuracy                 | 0.8314     |
| max_accuracy_threshold       | 53.9454    |
| max_f1                       | 0.8898     |
| max_f1_threshold             | 59.6646    |
| max_precision                | 0.8502     |
| max_recall                   | 0.9487     |
| **max_ap**                   | **0.9253** |

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: 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`: 128
- `per_device_eval_batch_size`: 128
- `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`: 0.0
- `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`: 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`: 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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch  | Step | Training Loss | loss   | max_ap |
|:------:|:----:|:-------------:|:------:|:------:|
| None   | 0    | -             | -      | 0.8131 |
| 0.1558 | 500  | 0.0262        | -      | -      |
| 0.3116 | 1000 | 0.0184        | -      | -      |
| 0.4674 | 1500 | 0.0173        | -      | -      |
| 0.6232 | 2000 | 0.0164        | 0.0155 | 0.9253 |
| 0.7791 | 2500 | 0.016         | -      | -      |
| 0.9349 | 3000 | 0.0155        | -      | -      |
| 1.0    | 3209 | -             | -      | 0.9296 |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.4
- PyTorch: 2.4.0+cu121
- Accelerate: 0.32.1
- Datasets: 2.21.0
- 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",
}
```

#### ContrastiveLoss
```bibtex
@inproceedings{hadsell2006dimensionality,
    author={Hadsell, R. and Chopra, S. and LeCun, Y.},
    booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, 
    title={Dimensionality Reduction by Learning an Invariant Mapping}, 
    year={2006},
    volume={2},
    number={},
    pages={1735-1742},
    doi={10.1109/CVPR.2006.100}
}
```

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