---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:142964
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-small
widget:
- source_sentence: نمونه هایی از تئوری های توطئه ها که به نظر می رسد درست است؟
  sentences:
  - آیا نظریه های توطئه ای وجود دارد که احتمالاً صادق است؟نظریه های توطئه ای که معلوم
    شد درست است؟
  - بازیگران پانتومیم در حال اجرا بر روی صحنه هستند.
  - چرا میل الکترون فلورین کمتر از کلر است ، در حالی که فلورین الکترونگاتیو ترین عنصر
    است؟
- source_sentence: بهترین و بدون درد برای کشتن خودم چیست؟
  sentences:
  - بهترین استراتژی ها برای آماده سازی برای GMAT چیست؟
  - آیا ارزش دارد دو سال برای NIT کاهش یابد؟
  - بدون درد ترین روش برای خودکشی چیست؟
- source_sentence: چه کاری باید انجام دهم در حالی که B-Tech را در مهندسی مکانیک برای
    چشم انداز بهتر شغلی دنبال می کنم؟
  sentences:
  - چگونه می توانیم مشاوره کسب و کار را شروع کنیم؟
  - فرصت های شغلی در شرکت ها پس از M.Tech در مهندسی هوافضا با B.Tech در مهندسی مکانیک
    چیست؟
  - آیا روانپزشکی یک شبه علوم است؟
- source_sentence: چرا گربه ها وقتی خیار را در مقابل آن قرار می دهید می ترسند؟
  sentences:
  - چرا گربه ها از خیار ترسیده اند؟
  - هک در زندگی روزمره چیست؟
  - چگونه می توانم به سرعت وزن خود را افزایش دهم؟
- source_sentence: مرزهای صفحه چیست؟برخی از انواع چیست؟
  sentences:
  - مرزهای صفحه چیست؟
  - اتانول چند ایزومر دارد؟
  - چه سؤالاتی در مورد Quora پرسیده نشده است؟
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on intfloat/multilingual-e5-small

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small). 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:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) <!-- at revision c007d7ef6fd86656326059b28395a7a03a7c5846 -->
- **Maximum Sequence Length:** 512 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': 512, '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})
  (2): Normalize()
)
```

## 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("codersan/newfa_e5small_7")
# Run inference
sentences = [
    'مرزهای صفحه چیست؟برخی از انواع چیست؟',
    'مرزهای صفحه چیست؟',
    'اتانول چند ایزومر دارد؟',
]
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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You can finetune this model on your own dataset.

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 142,964 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                            | positive                                                                          |
  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                            |
  | details | <ul><li>min: 6 tokens</li><li>mean: 16.39 tokens</li><li>max: 90 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.68 tokens</li><li>max: 57 tokens</li></ul> |
* Samples:
  | anchor                                                                       | positive                                                           |
  |:-----------------------------------------------------------------------------|:-------------------------------------------------------------------|
  | <code>گاو یونجه می خورد</code>                                               | <code>گاو در حال چریدن است</code>                                  |
  | <code>ماشینی به شکلی خطرناک از روی دختری می‌پرد.</code>                      | <code>دختر با بی‌احتیاطی روی ماشین می‌پرد.</code>                  |
  | <code>چگونه می توانم کارتهای هدیه iTunes رایگان را در هند دریافت کنم؟</code> | <code>چگونه می توانم کارتهای هدیه iTunes رایگان دریافت کنم؟</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

- `per_device_train_batch_size`: 64
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `max_grad_norm`: 0.2
- `num_train_epochs`: 2
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 8
- `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`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 0.2
- `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`: 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 |
|:------:|:----:|:-------------:|
| 0.0448 | 100  | 0.2696        |
| 0.0895 | 200  | 0.0953        |
| 0.1343 | 300  | 0.094         |
| 0.1791 | 400  | 0.0722        |
| 0.2238 | 500  | 0.0719        |
| 0.2686 | 600  | 0.0693        |
| 0.3133 | 700  | 0.079         |
| 0.3581 | 800  | 0.0711        |
| 0.4029 | 900  | 0.0699        |
| 0.4476 | 1000 | 0.0612        |
| 0.4924 | 1100 | 0.0759        |
| 0.5372 | 1200 | 0.0704        |
| 0.5819 | 1300 | 0.0663        |
| 0.6267 | 1400 | 0.0612        |
| 0.6714 | 1500 | 0.0717        |
| 0.7162 | 1600 | 0.0665        |
| 0.7610 | 1700 | 0.0629        |
| 0.8057 | 1800 | 0.0631        |
| 0.8505 | 1900 | 0.0619        |
| 0.8953 | 2000 | 0.0636        |
| 0.9400 | 2100 | 0.0616        |
| 0.9848 | 2200 | 0.0575        |
| 1.0295 | 2300 | 0.0596        |
| 1.0743 | 2400 | 0.0589        |
| 1.1191 | 2500 | 0.061         |
| 1.1638 | 2600 | 0.0507        |
| 1.2086 | 2700 | 0.0464        |
| 1.2534 | 2800 | 0.0442        |
| 1.2981 | 2900 | 0.055         |
| 1.3429 | 3000 | 0.0586        |
| 1.3876 | 3100 | 0.0555        |
| 1.4324 | 3200 | 0.0473        |
| 1.4772 | 3300 | 0.0471        |
| 1.5219 | 3400 | 0.0579        |
| 1.5667 | 3500 | 0.0499        |
| 1.6115 | 3600 | 0.0477        |
| 1.6562 | 3700 | 0.0558        |
| 1.7010 | 3800 | 0.0534        |
| 1.7457 | 3900 | 0.0538        |
| 1.7905 | 4000 | 0.0543        |
| 1.8353 | 4100 | 0.047         |
| 1.8800 | 4200 | 0.0532        |
| 1.9248 | 4300 | 0.0567        |
| 1.9696 | 4400 | 0.0474        |


### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.3.1
- Transformers: 4.47.0
- PyTorch: 2.5.1+cu121
- Accelerate: 1.2.1
- Datasets: 4.0.0
- 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",
}
```

#### 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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