sentence-transformers safetensors distilbert sentence-similarity feature-extraction generated_from_trainer dataset_size:32000 loss:OnlineContrastiveLoss arxiv:1908.10084 base_model:sentence-transformers/quora-distilbert-multilingual base_model:finetune:sentence-transformers/quora-distilbert-multilingual model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/quora-distilbert-multilingual
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:32000
- loss:OnlineContrastiveLoss
widget:
- source_sentence: پکیج بوتان مدل پارما دیجیتال Parma26RSi Digital
  sentences:
  - اسپیکر | باند اکتیو Yamaha DXR8
  - اسپری لکه بر فرش و مبلمان نانو
  - پکیج شوفاژ دیواری بوتان مدل Parma 26RSi Digital
- source_sentence: کپسول اسکور زینک تسنیم 30 عددی
  sentences:
  - لام مدل 7105 لب ویژن بسته 72عددی
  - کپسول اسکور زینک
  - پروتئین مو A برزیلی Protein Brazilian حجم ۸۰۰ میل
- source_sentence: شامپو بدن بچه فیروز مدل Guar حجم 300 میلی لیتر
  sentences:
  - کیف پول سخت افزاری ترزور تی Trezor T
  - شامپو بدن کودک فیروز 300ml
  - چسب پروتز مو سوپر تیپ
- source_sentence: خمیر دندان 3 کاره کلگیت Colgate حجم 50 میلی لیتر
  sentences:
  - خمیر دندان کلگیت Colgate مدل Triple Action
  - خمیر دندان زیرو سنستیو میسویک
  - خمیر دندان کلگیت تریپل اکشن Triple Action حجم 100ml
- source_sentence: رایزر گرافیک مدل 009s plus هشت خازنه
  sentences:
  - رادیاتور سمند EF7 بنزینی کوشش رادیاتور
  - رایزر گرافیک تبدیل PCI EXPRESS X1 به X16 مدل 009S
  - شامپو کودک حاوی عصاره اسطوخودوس فیروز200 میل
model-index:
- name: SentenceTransformer based on sentence-transformers/quora-distilbert-multilingual
  results:
  - task:
      type: binary-classification
      name: Binary Classification
    dataset:
      name: product matching binary
      type: product-matching-binary
    metrics:
    - type: cosine_accuracy
      value: 0.99075
      name: Cosine Accuracy
    - type: cosine_accuracy_threshold
      value: 0.7373077869415283
      name: Cosine Accuracy Threshold
    - type: cosine_f1
      value: 0.990789146128952
      name: Cosine F1
    - type: cosine_f1_threshold
      value: 0.7295311689376831
      name: Cosine F1 Threshold
    - type: cosine_precision
      value: 0.9900497512437811
      name: Cosine Precision
    - type: cosine_recall
      value: 0.9915296462381664
      name: Cosine Recall
    - type: cosine_ap
      value: 0.9989253864991785
      name: Cosine Ap
    - type: cosine_mcc
      value: 0.981500780471981
      name: Cosine Mcc
---

# SentenceTransformer based on sentence-transformers/quora-distilbert-multilingual

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/quora-distilbert-multilingual](https://huggingface.co/sentence-transformers/quora-distilbert-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/quora-distilbert-multilingual](https://huggingface.co/sentence-transformers/quora-distilbert-multilingual) <!-- at revision 2e32dee5140b6cbea86dd68195a25d463bfe071b -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 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: DistilBertModel 
  (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("melino2000/product-torob-matching")
# Run inference
sentences = [
    'رایزر گرافیک مدل 009s plus هشت خازنه',
    'رایزر گرافیک تبدیل PCI EXPRESS X1 به X16 مدل 009S',
    'شامپو کودک حاوی عصاره اسطوخودوس فیروز200 میل',
]
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

#### Binary Classification

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

| Metric                    | Value      |
|:--------------------------|:-----------|
| cosine_accuracy           | 0.9908     |
| cosine_accuracy_threshold | 0.7373     |
| cosine_f1                 | 0.9908     |
| cosine_f1_threshold       | 0.7295     |
| cosine_precision          | 0.99       |
| cosine_recall             | 0.9915     |
| **cosine_ap**             | **0.9989** |
| cosine_mcc                | 0.9815     |

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## Training Details

### Training Dataset

#### Unnamed Dataset

* Size: 32,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                        | sentence2                                                                         | label                                           |
  |:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                           | string                                                                            | int                                             |
  | details | <ul><li>min: 6 tokens</li><li>mean: 19.0 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 19.06 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>0: ~49.40%</li><li>1: ~50.60%</li></ul> |
* Samples:
  | sentence1                                                                    | sentence2                                             | label          |
  |:-----------------------------------------------------------------------------|:------------------------------------------------------|:---------------|
  | <code>پرینتر چندکاره لیزری HP LaserJet Pro M130a</code>                      | <code>پرینتر لیزری سه کاره اچ پی HP M130a</code>      | <code>1</code> |
  | <code>قرص روکشدار مولتی دیلی دکتر گیل 60 عددی داروسازی رازان فارمدیان</code> | <code>قرص مولتی دیلی دکتر گیل</code>                  | <code>1</code> |
  | <code>خمیردندان کلگیت 3 کاره Triple Action | 100 میل</code>                  | <code>خمیر دندان کولگیت مدل 3 کاره حجم 100 میل</code> | <code>1</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

### Evaluation Dataset

#### Unnamed Dataset

* Size: 8,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence1                                                                         | sentence2                                                                         | label                                           |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
  | type    | string                                                                            | string                                                                            | int                                             |
  | details | <ul><li>min: 5 tokens</li><li>mean: 19.24 tokens</li><li>max: 69 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 18.76 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>0: ~50.00%</li><li>1: ~50.00%</li></ul> |
* Samples:
  | sentence1                                                                     | sentence2                                                                    | label          |
  |:------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------|
  | <code>مایکرو فر 36 لیتری ناسا الکتریک مدل NS-2024</code>                      | <code>سرویس کاور روتختی تک نفره ایکیا مدل Ikea BRUNKRISSLA 404.907.23</code> | <code>0</code> |
  | <code>کنسول بازی نینتندو سوییچ سفید - Nintendo Switch OLED Model white</code> | <code>NINTENDO SWITCH OLED (Neon Red & Neon Blue)</code>                     | <code>1</code> |
  | <code>خمیر دندان کرست مدل Complete 7</code>                                   | <code>قلمو سرگرد 2122 پارس آرت (32400_107700 تومان)</code>                   | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `warmup_ratio`: 0.1
- `fp16`: 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`: 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`: 1.0
- `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`: 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`: 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 | product-matching-binary_cosine_ap |
|:-----:|:----:|:-------------:|:---------------:|:---------------------------------:|
| -1    | -1   | -             | -               | 0.9436                            |
| 0.1   | 100  | 0.637         | -               | -                                 |
| 0.2   | 200  | 0.1303        | -               | -                                 |
| 0.25  | 250  | -             | 0.0785          | 0.9961                            |
| 0.3   | 300  | 0.1378        | -               | -                                 |
| 0.4   | 400  | 0.1191        | -               | -                                 |
| 0.5   | 500  | 0.0949        | 0.0723          | 0.9963                            |
| 0.6   | 600  | 0.1016        | -               | -                                 |
| 0.7   | 700  | 0.0694        | -               | -                                 |
| 0.75  | 750  | -             | 0.0464          | 0.9974                            |
| 0.8   | 800  | 0.0619        | -               | -                                 |
| 0.9   | 900  | 0.0543        | -               | -                                 |
| 1.0   | 1000 | 0.0658        | 0.0394          | 0.9981                            |
| 1.1   | 1100 | 0.0326        | -               | -                                 |
| 1.2   | 1200 | 0.0176        | -               | -                                 |
| 1.25  | 1250 | -             | 0.0387          | 0.9980                            |
| 1.3   | 1300 | 0.0237        | -               | -                                 |
| 1.4   | 1400 | 0.0219        | -               | -                                 |
| 1.5   | 1500 | 0.0115        | 0.0259          | 0.9983                            |
| 1.6   | 1600 | 0.0218        | -               | -                                 |
| 1.7   | 1700 | 0.0235        | -               | -                                 |
| 1.75  | 1750 | -             | 0.0230          | 0.9988                            |
| 1.8   | 1800 | 0.0319        | -               | -                                 |
| 1.9   | 1900 | 0.0127        | -               | -                                 |
| 2.0   | 2000 | 0.015         | 0.0285          | 0.9987                            |
| 2.099 | 2100 | 0.0121        | -               | -                                 |
| 2.199 | 2200 | 0.0091        | -               | -                                 |
| 2.249 | 2250 | -             | 0.0217          | 0.9986                            |
| 2.299 | 2300 | 0.0107        | -               | -                                 |
| 2.399 | 2400 | 0.009         | -               | -                                 |
| 2.499 | 2500 | 0.0043        | 0.0224          | 0.9989                            |
| 2.599 | 2600 | 0.0028        | -               | -                                 |
| 2.699 | 2700 | 0.0026        | -               | -                                 |
| 2.749 | 2750 | -             | 0.0248          | 0.9989                            |
| 2.799 | 2800 | 0.0024        | -               | -                                 |
| 2.899 | 2900 | 0.0067        | -               | -                                 |
| 2.999 | 3000 | 0.0088        | 0.0225          | 0.9989                            |
| -1    | -1   | -             | -               | 0.9989                            |


### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- 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",
}
```

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