sentence-transformers safetensors xlm-roberta sentence-similarity feature-extraction generated_from_trainer dataset_size:166088 loss:MultipleNegativesRankingLoss arxiv:1908.10084 arxiv:1705.00652 base_model:intfloat/multilingual-e5-base base_model:finetune:intfloat/multilingual-e5-base autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: intfloat/multilingual-e5-base
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:166088
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: کدام یک از تجربیات بدی که در زندگی داشتید؟
  sentences:
  - چگونه برای اولین بار با پورنو آشنا شدید؟
  - آیا Urjit Patel برای فرماندار RBI مناسب است؟
  - برخی از تجربیات خوب و بد زندگی شما چه بود؟
- source_sentence: اگر مبلغی نامحدود داشته باشید ، تنها کاری که دوست دارید انجام دهید
    چیست؟
  sentences:
  - برای حمله به 29 نوامبر نوامبر بلافاصله توسط کشور ما علیه پاکستان باید چه اقداماتی
    انجام شود؟
  - آیا باید تصاویر را در رسانه های اجتماعی ارسال کنید؟
  - اگر مبلغ نامحدودی داشتید ، با زندگی خود چه می کنید؟
- source_sentence: آیا کاخ سفید توسط بردگان ساخته شده بود؟
  sentences:
  - آیا کاخ سفید کاملاً توسط بردگان ساخته شده است؟
  - آیا ظرفیت گرما همان گرمای خاص است؟
  - چه اتفاقی می افتد اگر نامزد ریاست جمهوری قبل از انتخابات نوامبر درگذشت؟
- source_sentence: چگونه می توانم موهای زائد را متوقف کنم؟
  sentences:
  - آیا WhatsApp هک کردن امکان پذیر است؟
  - چه فیلم هایی را به همه توصیه می کنید که تماشا کنند؟
  - چگونه می توانم موهای زائد را متوقف کنم؟
- source_sentence: معنی و هدف زندگی چیست؟
  sentences:
  - مراکز خرید در آینده چگونه خواهد بود؟
  - چگونه دونالد ترامپ علی رغم پیش بینی هایی که او شکست می خورد ، پیروز شد؟
  - معنی دقیق زندگی چیست؟
---

# 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 d13f1b27baf31030b7fd040960d60d909913633f -->
- **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/multilingual-e5-base-Fa-v3")
# Run inference
sentences = [
    'معنی و هدف زندگی چیست؟',
    'معنی دقیق زندگی چیست؟',
    'مراکز خرید در آینده چگونه خواهد بود؟',
]
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: 166,088 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: 5 tokens</li><li>mean: 15.57 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.84 tokens</li><li>max: 56 tokens</li></ul> |
* Samples:
  | anchor                                                                                   | positive                                                                                           |
  |:-----------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
  | <code>طالع بینی: من یک ماه و کلاه درپوش خورشید است ... این در مورد من چه می گوید؟</code> | <code>من یک برج سه گانه (خورشید ، ماه و صعود در برجستگی) هستم که این در مورد من چه می گوید؟</code> |
  | <code>چگونه می توانم یک زمین شناس خوب باشم؟</code>                                       | <code>چه کاری باید انجام دهم تا یک زمین شناس عالی باشم؟</code>                                     |
  | <code>چگونه می توانم نظرات YouTube خود را بخوانم و پیدا کنم؟</code>                      | <code>چگونه می توانم تمام نظرات YouTube خود را ببینم؟</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`: 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`: 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`: 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
| Epoch  | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0770 | 100  | 0.2123        |
| 0.1541 | 200  | 0.1124        |
| 0.2311 | 300  | 0.0804        |
| 0.3082 | 400  | 0.1067        |
| 0.3852 | 500  | 0.0981        |
| 0.4622 | 600  | 0.0936        |
| 0.5393 | 700  | 0.0957        |
| 0.6163 | 800  | 0.0848        |
| 0.6934 | 900  | 0.0924        |
| 0.7704 | 1000 | 0.0885        |
| 0.8475 | 1100 | 0.0852        |
| 0.9245 | 1200 | 0.1289        |
| 1.0008 | 1300 | 1.0585        |
| 1.0778 | 1400 | 0.0738        |
| 1.1549 | 1500 | 0.0677        |
| 1.2319 | 1600 | 0.0524        |
| 1.3089 | 1700 | 0.0713        |
| 1.3860 | 1800 | 0.0629        |
| 1.4630 | 1900 | 0.0634        |
| 1.5401 | 2000 | 0.0654        |
| 1.6171 | 2100 | 0.0596        |
| 1.6941 | 2200 | 0.0669        |
| 1.7712 | 2300 | 0.0633        |
| 1.8482 | 2400 | 0.0634        |
| 1.9253 | 2500 | 0.0968        |
| 2.0015 | 2600 | 0.9084        |
| 2.0786 | 2700 | 0.0582        |
| 2.1556 | 2800 | 0.0522        |
| 2.2327 | 2900 | 0.0438        |
| 2.3097 | 3000 | 0.0586        |
| 2.3867 | 3100 | 0.0514        |
| 2.4638 | 3200 | 0.0512        |
| 2.5408 | 3300 | 0.0537        |
| 2.6179 | 3400 | 0.0513        |
| 2.6949 | 3500 | 0.0561        |
| 2.7720 | 3600 | 0.0515        |
| 2.8490 | 3700 | 0.0549        |
| 2.9260 | 3800 | 0.0873        |


### 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: 3.2.0
- 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}
}
```

<!--
## Glossary

*Clearly define terms in order to be accessible across audiences.*
-->

<!--
## Model Card Authors

*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->

<!--
## Model Card Contact

*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->