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codersan
/
newfa_e5base2

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:142964
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files
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newfa_e5base2
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---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:142964
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-base
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-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base). 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:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision 835193815a3936a24a0ee7dc9e3d48c1fbb19c55 -->
- **Maximum Sequence Length:** 512 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': 512, '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})
  (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_e5base2")
# 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, 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.*
-->

<!--
## 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: 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`: 32
- `learning_rate`: 2e-05
- `weight_decay`: 0.01
- `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`: 32
- `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`: 2e-05
- `weight_decay`: 0.01
- `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`: 0.0
- `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
<details><summary>Click to expand</summary>

| Epoch  | Step  | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0224 | 100   | 0.0821        |
| 0.0448 | 200   | 0.0455        |
| 0.0671 | 300   | 0.0408        |
| 0.0895 | 400   | 0.0461        |
| 0.1119 | 500   | 0.0418        |
| 0.1343 | 600   | 0.0449        |
| 0.1567 | 700   | 0.0314        |
| 0.1791 | 800   | 0.0252        |
| 0.2014 | 900   | 0.0254        |
| 0.2238 | 1000  | 0.0341        |
| 0.2462 | 1100  | 0.0239        |
| 0.2686 | 1200  | 0.0308        |
| 0.2910 | 1300  | 0.0415        |
| 0.3133 | 1400  | 0.0386        |
| 0.3357 | 1500  | 0.027         |
| 0.3581 | 1600  | 0.0369        |
| 0.3805 | 1700  | 0.0346        |
| 0.4029 | 1800  | 0.0301        |
| 0.4252 | 1900  | 0.03          |
| 0.4476 | 2000  | 0.0179        |
| 0.4700 | 2100  | 0.035         |
| 0.4924 | 2200  | 0.0327        |
| 0.5148 | 2300  | 0.033         |
| 0.5372 | 2400  | 0.0272        |
| 0.5595 | 2500  | 0.0318        |
| 0.5819 | 2600  | 0.025         |
| 0.6043 | 2700  | 0.023         |
| 0.6267 | 2800  | 0.0294        |
| 0.6491 | 2900  | 0.0337        |
| 0.6714 | 3000  | 0.0274        |
| 0.6938 | 3100  | 0.0223        |
| 0.7162 | 3200  | 0.0384        |
| 0.7386 | 3300  | 0.0217        |
| 0.7610 | 3400  | 0.032         |
| 0.7833 | 3500  | 0.0309        |
| 0.8057 | 3600  | 0.024         |
| 0.8281 | 3700  | 0.0273        |
| 0.8505 | 3800  | 0.0245        |
| 0.8729 | 3900  | 0.0268        |
| 0.8953 | 4000  | 0.0322        |
| 0.9176 | 4100  | 0.0271        |
| 0.9400 | 4200  | 0.0316        |
| 0.9624 | 4300  | 0.0179        |
| 0.9848 | 4400  | 0.0294        |
| 1.0072 | 4500  | 0.0283        |
| 1.0295 | 4600  | 0.0171        |
| 1.0519 | 4700  | 0.017         |
| 1.0743 | 4800  | 0.0197        |
| 1.0967 | 4900  | 0.0215        |
| 1.1191 | 5000  | 0.02          |
| 1.1415 | 5100  | 0.0144        |
| 1.1638 | 5200  | 0.015         |
| 1.1862 | 5300  | 0.0084        |
| 1.2086 | 5400  | 0.0115        |
| 1.2310 | 5500  | 0.0143        |
| 1.2534 | 5600  | 0.0129        |
| 1.2757 | 5700  | 0.0165        |
| 1.2981 | 5800  | 0.0168        |
| 1.3205 | 5900  | 0.0233        |
| 1.3429 | 6000  | 0.0156        |
| 1.3653 | 6100  | 0.0207        |
| 1.3876 | 6200  | 0.0149        |
| 1.4100 | 6300  | 0.0134        |
| 1.4324 | 6400  | 0.0108        |
| 1.4548 | 6500  | 0.0118        |
| 1.4772 | 6600  | 0.0173        |
| 1.4996 | 6700  | 0.0171        |
| 1.5219 | 6800  | 0.0168        |
| 1.5443 | 6900  | 0.0144        |
| 1.5667 | 7000  | 0.0111        |
| 1.5891 | 7100  | 0.0117        |
| 1.6115 | 7200  | 0.0122        |
| 1.6338 | 7300  | 0.0143        |
| 1.6562 | 7400  | 0.0151        |
| 1.6786 | 7500  | 0.0152        |
| 1.7010 | 7600  | 0.012         |
| 1.7234 | 7700  | 0.0177        |
| 1.7457 | 7800  | 0.0172        |
| 1.7681 | 7900  | 0.016         |
| 1.7905 | 8000  | 0.0141        |
| 1.8129 | 8100  | 0.0112        |
| 1.8353 | 8200  | 0.011         |
| 1.8577 | 8300  | 0.0132        |
| 1.8800 | 8400  | 0.0127        |
| 1.9024 | 8500  | 0.0188        |
| 1.9248 | 8600  | 0.0196        |
| 1.9472 | 8700  | 0.0106        |
| 1.9696 | 8800  | 0.0108        |
| 1.9919 | 8900  | 0.0172        |
| 2.0143 | 9000  | 0.0116        |
| 2.0367 | 9100  | 0.0089        |
| 2.0591 | 9200  | 0.0096        |
| 2.0815 | 9300  | 0.0142        |
| 2.1038 | 9400  | 0.0112        |
| 2.1262 | 9500  | 0.0103        |
| 2.1486 | 9600  | 0.0077        |
| 2.1710 | 9700  | 0.0082        |
| 2.1934 | 9800  | 0.0066        |
| 2.2158 | 9900  | 0.0106        |
| 2.2381 | 10000 | 0.0072        |
| 2.2605 | 10100 | 0.0085        |
| 2.2829 | 10200 | 0.0085        |
| 2.3053 | 10300 | 0.015         |
| 2.3277 | 10400 | 0.0113        |
| 2.3500 | 10500 | 0.0118        |
| 2.3724 | 10600 | 0.0123        |
| 2.3948 | 10700 | 0.0071        |
| 2.4172 | 10800 | 0.0087        |
| 2.4396 | 10900 | 0.0056        |
| 2.4620 | 11000 | 0.0091        |
| 2.4843 | 11100 | 0.0116        |
| 2.5067 | 11200 | 0.0123        |
| 2.5291 | 11300 | 0.0108        |
| 2.5515 | 11400 | 0.0078        |
| 2.5739 | 11500 | 0.0072        |
| 2.5962 | 11600 | 0.0084        |
| 2.6186 | 11700 | 0.0066        |
| 2.6410 | 11800 | 0.0115        |
| 2.6634 | 11900 | 0.0088        |
| 2.6858 | 12000 | 0.008         |
| 2.7081 | 12100 | 0.0095        |
| 2.7305 | 12200 | 0.0108        |
| 2.7529 | 12300 | 0.0113        |
| 2.7753 | 12400 | 0.0086        |
| 2.7977 | 12500 | 0.0096        |
| 2.8201 | 12600 | 0.0093        |
| 2.8424 | 12700 | 0.0076        |
| 2.8648 | 12800 | 0.006         |
| 2.8872 | 12900 | 0.0124        |
| 2.9096 | 13000 | 0.0131        |
| 2.9320 | 13100 | 0.0103        |
| 2.9543 | 13200 | 0.0063        |
| 2.9767 | 13300 | 0.0067        |
| 2.9991 | 13400 | 0.0117        |

</details>

### 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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