---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:131157
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: عواقب ممنوعیت یادداشت های 500 روپیه و 1000 روپیه در مورد اقتصاد
    هند چیست؟
  sentences:
  - آیا باید در فیزیک و علوم کامپیوتر دو برابر کنم؟
  - چگونه اقتصاد هند پس از ممنوعیت 500 1000 یادداشت تحت تأثیر قرار گرفت؟
  - آیا آلمان در اجازه پناهندگان سوری به کشور خود اشتباه کرد؟
- source_sentence: بهترین شماره پشتیبانی فنی QuickBooks در نیویورک ، ایالات متحده
    کدام است؟
  sentences:
  - فناوری هایی که اکثر مردم از آنها نمی دانند چیست؟
  - بهترین شماره پشتیبانی QuickBooks در آرکانزاس چیست؟
  - چرا در مقایسه با طرف نزدیک ، دهانه های زیادی در قسمت دور ماه وجود دارد؟
- source_sentence: اقدامات احتیاطی ایمنی در مورد استفاده از اسلحه های پیشنهادی NRA
    در میشیگان چیست؟
  sentences:
  - پیروزی ترامپ چگونه بر کانادا تأثیر خواهد گذاشت؟
  - اقدامات احتیاطی ایمنی در مورد استفاده از اسلحه های پیشنهادی NRA در آیداهو چیست؟
  - مزایای خرید بیمه عمر چیست؟
- source_sentence: چرا این همه افراد ناراضی هستند؟
  sentences:
  - چرا آب نبات تافی آب شور در مغولستان وارد می شود؟
  - برای یک رابطه موفق از راه دور چه چیزی طول می کشد؟
  - چرا مردم ناراضی هستند؟
- source_sentence: برای تبدیل شدن به نویسنده برتر Quora ، چند بازدید و پاسخ لازم است؟
  sentences:
  - چگونه می توانم نویسنده برتر Quora شوم ، از صعود بیشتر و آمار بهتر استفاده کنم؟
  - چرا بسیاری از افرادی که سؤالاتی را در Quora ارسال می کنند ، ابتدا Google را بررسی
    می کنند؟
  - من به دنبال خرید دوچرخه جدید هستم.Suzuki Gixxer 155 یا Honda Hornet 160r.کدام
    یک را بخرید؟
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 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': 256, '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/validadted_allMiniLM_onV9f")
# Run inference
sentences = [
    'برای تبدیل شدن به نویسنده برتر Quora ، چند بازدید و پاسخ لازم است؟',
    'چگونه می توانم نویسنده برتر Quora شوم ، از صعود بیشتر و آمار بهتر استفاده کنم؟',
    'من به دنبال خرید دوچرخه جدید هستم.Suzuki Gixxer 155 یا Honda Hornet 160r.کدام یک را بخرید؟',
]
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]
```

<!--
### 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: 131,157 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: 11 tokens</li><li>mean: 44.91 tokens</li><li>max: 256 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 44.6 tokens</li><li>max: 154 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                        | positive                                                                                                                                            |
  |:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>وقتی سوال من به عنوان "این سوال ممکن است به ویرایش نیاز داشته باشد" چه کاری باید انجام دهم ، اما نمی توانم دلیل آن را پیدا کنم؟</code>  | <code>چرا سوال من به عنوان نیاز به پیشرفت مشخص شده است؟</code>                                                                                      |
  | <code>چگونه می توانید یک فایل رمزگذاری شده را با دانستن اینکه این یک فایل تصویری است بدون دانستن گسترش پرونده یا کلید ، رمزگشایی کنید؟</code> | <code>چگونه می توانید یک فایل رمزگذاری شده را رمزگشایی کنید و بدانید که این یک فایل تصویری است بدون اینکه از پسوند پرونده اطلاع داشته باشید؟</code> |
  | <code>احساس می کنم خودکشی می کنم ، چگونه باید با آن برخورد کنم؟</code>                                                                        | <code>احساس می کنم خودکشی می کنم.چه کاری باید انجام دهم؟</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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 12
- `learning_rate`: 5e-06
- `weight_decay`: 0.01
- `warmup_ratio`: 0.1
- `push_to_hub`: True
- `hub_model_id`: codersan/validadted_allMiniLM_onV9f
- `eval_on_start`: 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`: 12
- `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`: 5e-06
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `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`: 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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: codersan/validadted_allMiniLM_onV9f
- `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`: True
- `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      | 0     | -             |
| 0.0091 | 100   | 1.4865        |
| 0.0183 | 200   | 1.4429        |
| 0.0274 | 300   | 1.2725        |
| 0.0366 | 400   | 1.1602        |
| 0.0457 | 500   | 0.9429        |
| 0.0549 | 600   | 0.829         |
| 0.0640 | 700   | 0.7771        |
| 0.0732 | 800   | 0.6597        |
| 0.0823 | 900   | 0.5981        |
| 0.0915 | 1000  | 0.5826        |
| 0.1006 | 1100  | 0.5956        |
| 0.1098 | 1200  | 0.5254        |
| 0.1189 | 1300  | 0.5434        |
| 0.1281 | 1400  | 0.5495        |
| 0.1372 | 1500  | 0.4934        |
| 0.1464 | 1600  | 0.4684        |
| 0.1555 | 1700  | 0.4489        |
| 0.1647 | 1800  | 0.4401        |
| 0.1738 | 1900  | 0.4712        |
| 0.1830 | 2000  | 0.4407        |
| 0.1921 | 2100  | 0.4082        |
| 0.2013 | 2200  | 0.4384        |
| 0.2104 | 2300  | 0.3621        |
| 0.2196 | 2400  | 0.4423        |
| 0.2287 | 2500  | 0.4163        |
| 0.2379 | 2600  | 0.3769        |
| 0.2470 | 2700  | 0.3967        |
| 0.2562 | 2800  | 0.3812        |
| 0.2653 | 2900  | 0.3813        |
| 0.2745 | 3000  | 0.359         |
| 0.2836 | 3100  | 0.3454        |
| 0.2928 | 3200  | 0.3518        |
| 0.3019 | 3300  | 0.3306        |
| 0.3111 | 3400  | 0.3138        |
| 0.3202 | 3500  | 0.3416        |
| 0.3294 | 3600  | 0.3474        |
| 0.3385 | 3700  | 0.3153        |
| 0.3477 | 3800  | 0.2896        |
| 0.3568 | 3900  | 0.2737        |
| 0.3660 | 4000  | 0.3004        |
| 0.3751 | 4100  | 0.3109        |
| 0.3843 | 4200  | 0.2829        |
| 0.3934 | 4300  | 0.2729        |
| 0.4026 | 4400  | 0.2714        |
| 0.4117 | 4500  | 0.3014        |
| 0.4209 | 4600  | 0.27          |
| 0.4300 | 4700  | 0.3632        |
| 0.4392 | 4800  | 0.2571        |
| 0.4483 | 4900  | 0.2464        |
| 0.4575 | 5000  | 0.2681        |
| 0.4666 | 5100  | 0.2579        |
| 0.4758 | 5200  | 0.2377        |
| 0.4849 | 5300  | 0.2471        |
| 0.4941 | 5400  | 0.2625        |
| 0.5032 | 5500  | 0.2336        |
| 0.5124 | 5600  | 0.2553        |
| 0.5215 | 5700  | 0.2549        |
| 0.5306 | 5800  | 0.22          |
| 0.5398 | 5900  | 0.2682        |
| 0.5489 | 6000  | 0.2329        |
| 0.5581 | 6100  | 0.2244        |
| 0.5672 | 6200  | 0.2458        |
| 0.5764 | 6300  | 0.1881        |
| 0.5855 | 6400  | 0.209         |
| 0.5947 | 6500  | 0.2103        |
| 0.6038 | 6600  | 0.1982        |
| 0.6130 | 6700  | 0.2023        |
| 0.6221 | 6800  | 0.2244        |
| 0.6313 | 6900  | 0.2051        |
| 0.6404 | 7000  | 0.224         |
| 0.6496 | 7100  | 0.2113        |
| 0.6587 | 7200  | 0.2386        |
| 0.6679 | 7300  | 0.1685        |
| 0.6770 | 7400  | 0.2092        |
| 0.6862 | 7500  | 0.1832        |
| 0.6953 | 7600  | 0.1957        |
| 0.7045 | 7700  | 0.2082        |
| 0.7136 | 7800  | 0.2213        |
| 0.7228 | 7900  | 0.177         |
| 0.7319 | 8000  | 0.196         |
| 0.7411 | 8100  | 0.2034        |
| 0.7502 | 8200  | 0.2017        |
| 0.7594 | 8300  | 0.1741        |
| 0.7685 | 8400  | 0.2092        |
| 0.7777 | 8500  | 0.1684        |
| 0.7868 | 8600  | 0.1874        |
| 0.7960 | 8700  | 0.1866        |
| 0.8051 | 8800  | 0.2291        |
| 0.8143 | 8900  | 0.1796        |
| 0.8234 | 9000  | 0.2036        |
| 0.8326 | 9100  | 0.2173        |
| 0.8417 | 9200  | 0.2074        |
| 0.8509 | 9300  | 0.1914        |
| 0.8600 | 9400  | 0.1639        |
| 0.8692 | 9500  | 0.1798        |
| 0.8783 | 9600  | 0.1926        |
| 0.8875 | 9700  | 0.1672        |
| 0.8966 | 9800  | 0.1727        |
| 0.9058 | 9900  | 0.189         |
| 0.9149 | 10000 | 0.2055        |
| 0.9241 | 10100 | 0.2043        |
| 0.9332 | 10200 | 0.1515        |
| 0.9424 | 10300 | 0.1675        |
| 0.9515 | 10400 | 0.1764        |
| 0.9607 | 10500 | 0.1709        |
| 0.9698 | 10600 | 0.1861        |
| 0.9790 | 10700 | 0.1928        |
| 0.9881 | 10800 | 0.1756        |
| 0.9973 | 10900 | 0.1611        |
| 1.0064 | 11000 | 0.1371        |
| 1.0156 | 11100 | 0.1499        |
| 1.0247 | 11200 | 0.2001        |
| 1.0339 | 11300 | 0.197         |
| 1.0430 | 11400 | 0.2035        |
| 1.0522 | 11500 | 0.1524        |
| 1.0613 | 11600 | 0.1988        |
| 1.0704 | 11700 | 0.1643        |
| 1.0796 | 11800 | 0.1488        |
| 1.0887 | 11900 | 0.1402        |
| 1.0979 | 12000 | 0.1501        |
| 1.1070 | 12100 | 0.1476        |
| 1.1162 | 12200 | 0.1703        |
| 1.1253 | 12300 | 0.1437        |
| 1.1345 | 12400 | 0.1684        |
| 1.1436 | 12500 | 0.1583        |
| 1.1528 | 12600 | 0.1554        |
| 1.1619 | 12700 | 0.1453        |
| 1.1711 | 12800 | 0.1592        |
| 1.1802 | 12900 | 0.1508        |
| 1.1894 | 13000 | 0.1585        |
| 1.1985 | 13100 | 0.1381        |
| 1.2077 | 13200 | 0.1442        |
| 1.2168 | 13300 | 0.183         |
| 1.2260 | 13400 | 0.1704        |
| 1.2351 | 13500 | 0.152         |
| 1.2443 | 13600 | 0.136         |
| 1.2534 | 13700 | 0.1596        |
| 1.2626 | 13800 | 0.151         |
| 1.2717 | 13900 | 0.1597        |
| 1.2809 | 14000 | 0.1547        |
| 1.2900 | 14100 | 0.1717        |
| 1.2992 | 14200 | 0.1037        |
| 1.3083 | 14300 | 0.1452        |
| 1.3175 | 14400 | 0.155         |
| 1.3266 | 14500 | 0.189         |
| 1.3358 | 14600 | 0.1384        |
| 1.3449 | 14700 | 0.1711        |
| 1.3541 | 14800 | 0.1255        |
| 1.3632 | 14900 | 0.1439        |
| 1.3724 | 15000 | 0.1583        |
| 1.3815 | 15100 | 0.1586        |
| 1.3907 | 15200 | 0.1502        |
| 1.3998 | 15300 | 0.1199        |
| 1.4090 | 15400 | 0.1362        |
| 1.4181 | 15500 | 0.1502        |
| 1.4273 | 15600 | 0.191         |
| 1.4364 | 15700 | 0.1495        |
| 1.4456 | 15800 | 0.1313        |
| 1.4547 | 15900 | 0.1429        |
| 1.4639 | 16000 | 0.1004        |
| 1.4730 | 16100 | 0.1267        |
| 1.4822 | 16200 | 0.1382        |
| 1.4913 | 16300 | 0.1535        |
| 1.5005 | 16400 | 0.1328        |
| 1.5096 | 16500 | 0.1268        |
| 1.5188 | 16600 | 0.1819        |
| 1.5279 | 16700 | 0.133         |
| 1.5371 | 16800 | 0.1503        |
| 1.5462 | 16900 | 0.1217        |
| 1.5554 | 17000 | 0.1414        |
| 1.5645 | 17100 | 0.1413        |
| 1.5737 | 17200 | 0.124         |
| 1.5828 | 17300 | 0.1111        |
| 1.5919 | 17400 | 0.1641        |
| 1.6011 | 17500 | 0.1217        |
| 1.6102 | 17600 | 0.1148        |
| 1.6194 | 17700 | 0.1452        |
| 1.6285 | 17800 | 0.1245        |
| 1.6377 | 17900 | 0.1184        |
| 1.6468 | 18000 | 0.1333        |
| 1.6560 | 18100 | 0.1421        |
| 1.6651 | 18200 | 0.1243        |
| 1.6743 | 18300 | 0.1173        |
| 1.6834 | 18400 | 0.117         |
| 1.6926 | 18500 | 0.1145        |
| 1.7017 | 18600 | 0.1365        |
| 1.7109 | 18700 | 0.1404        |
| 1.7200 | 18800 | 0.1254        |
| 1.7292 | 18900 | 0.1131        |
| 1.7383 | 19000 | 0.1503        |
| 1.7475 | 19100 | 0.1429        |
| 1.7566 | 19200 | 0.1057        |
| 1.7658 | 19300 | 0.1221        |
| 1.7749 | 19400 | 0.1034        |
| 1.7841 | 19500 | 0.1154        |
| 1.7932 | 19600 | 0.1106        |
| 1.8024 | 19700 | 0.1568        |
| 1.8115 | 19800 | 0.1332        |
| 1.8207 | 19900 | 0.1238        |
| 1.8298 | 20000 | 0.1321        |
| 1.8390 | 20100 | 0.1629        |
| 1.8481 | 20200 | 0.135         |
| 1.8573 | 20300 | 0.1097        |
| 1.8664 | 20400 | 0.1233        |
| 1.8756 | 20500 | 0.1198        |
| 1.8847 | 20600 | 0.1151        |
| 1.8939 | 20700 | 0.1206        |
| 1.9030 | 20800 | 0.1295        |
| 1.9122 | 20900 | 0.126         |
| 1.9213 | 21000 | 0.147         |
| 1.9305 | 21100 | 0.1316        |
| 1.9396 | 21200 | 0.1019        |
| 1.9488 | 21300 | 0.1328        |
| 1.9579 | 21400 | 0.1127        |
| 1.9671 | 21500 | 0.1416        |
| 1.9762 | 21600 | 0.1428        |
| 1.9854 | 21700 | 0.1481        |
| 1.9945 | 21800 | 0.1169        |
| 2.0037 | 21900 | 0.1005        |
| 2.0128 | 22000 | 0.1114        |
| 2.0220 | 22100 | 0.1301        |
| 2.0311 | 22200 | 0.1554        |
| 2.0403 | 22300 | 0.1623        |
| 2.0494 | 22400 | 0.1153        |
| 2.0586 | 22500 | 0.1152        |
| 2.0677 | 22600 | 0.1406        |
| 2.0769 | 22700 | 0.1196        |
| 2.0860 | 22800 | 0.1172        |
| 2.0952 | 22900 | 0.1153        |
| 2.1043 | 23000 | 0.1126        |
| 2.1134 | 23100 | 0.1157        |
| 2.1226 | 23200 | 0.1102        |
| 2.1317 | 23300 | 0.1102        |
| 2.1409 | 23400 | 0.1198        |
| 2.1500 | 23500 | 0.1241        |
| 2.1592 | 23600 | 0.1124        |
| 2.1683 | 23700 | 0.1172        |
| 2.1775 | 23800 | 0.1161        |
| 2.1866 | 23900 | 0.1162        |
| 2.1958 | 24000 | 0.1209        |
| 2.2049 | 24100 | 0.1039        |
| 2.2141 | 24200 | 0.1183        |
| 2.2232 | 24300 | 0.1155        |
| 2.2324 | 24400 | 0.1168        |
| 2.2415 | 24500 | 0.1116        |
| 2.2507 | 24600 | 0.1173        |
| 2.2598 | 24700 | 0.1321        |
| 2.2690 | 24800 | 0.1217        |
| 2.2781 | 24900 | 0.1153        |
| 2.2873 | 25000 | 0.1464        |
| 2.2964 | 25100 | 0.101         |
| 2.3056 | 25200 | 0.1042        |
| 2.3147 | 25300 | 0.1382        |
| 2.3239 | 25400 | 0.1489        |
| 2.3330 | 25500 | 0.1187        |
| 2.3422 | 25600 | 0.1184        |
| 2.3513 | 25700 | 0.0971        |
| 2.3605 | 25800 | 0.0986        |
| 2.3696 | 25900 | 0.1114        |
| 2.3788 | 26000 | 0.1175        |
| 2.3879 | 26100 | 0.1136        |
| 2.3971 | 26200 | 0.1251        |
| 2.4062 | 26300 | 0.1097        |
| 2.4154 | 26400 | 0.1123        |
| 2.4245 | 26500 | 0.1446        |
| 2.4337 | 26600 | 0.1282        |
| 2.4428 | 26700 | 0.0988        |
| 2.4520 | 26800 | 0.1172        |
| 2.4611 | 26900 | 0.0903        |
| 2.4703 | 27000 | 0.1049        |
| 2.4794 | 27100 | 0.1043        |
| 2.4886 | 27200 | 0.1081        |
| 2.4977 | 27300 | 0.1265        |
| 2.5069 | 27400 | 0.1131        |
| 2.5160 | 27500 | 0.1403        |
| 2.5252 | 27600 | 0.1033        |
| 2.5343 | 27700 | 0.1175        |
| 2.5435 | 27800 | 0.1247        |
| 2.5526 | 27900 | 0.1115        |
| 2.5618 | 28000 | 0.1173        |
| 2.5709 | 28100 | 0.1209        |
| 2.5801 | 28200 | 0.0894        |
| 2.5892 | 28300 | 0.1238        |
| 2.5984 | 28400 | 0.1011        |
| 2.6075 | 28500 | 0.0976        |
| 2.6167 | 28600 | 0.0968        |
| 2.6258 | 28700 | 0.1065        |
| 2.6349 | 28800 | 0.1011        |
| 2.6441 | 28900 | 0.0975        |
| 2.6532 | 29000 | 0.1291        |
| 2.6624 | 29100 | 0.1118        |
| 2.6715 | 29200 | 0.0983        |
| 2.6807 | 29300 | 0.1119        |
| 2.6898 | 29400 | 0.0728        |
| 2.6990 | 29500 | 0.1241        |
| 2.7081 | 29600 | 0.1045        |
| 2.7173 | 29700 | 0.1186        |
| 2.7264 | 29800 | 0.1037        |
| 2.7356 | 29900 | 0.129         |
| 2.7447 | 30000 | 0.0921        |
| 2.7539 | 30100 | 0.1006        |
| 2.7630 | 30200 | 0.1068        |
| 2.7722 | 30300 | 0.099         |
| 2.7813 | 30400 | 0.0949        |
| 2.7905 | 30500 | 0.1066        |
| 2.7996 | 30600 | 0.1025        |
| 2.8088 | 30700 | 0.1148        |
| 2.8179 | 30800 | 0.1164        |
| 2.8271 | 30900 | 0.1147        |
| 2.8362 | 31000 | 0.1298        |
| 2.8454 | 31100 | 0.1245        |
| 2.8545 | 31200 | 0.087         |
| 2.8637 | 31300 | 0.1115        |
| 2.8728 | 31400 | 0.1129        |
| 2.8820 | 31500 | 0.1121        |
| 2.8911 | 31600 | 0.0985        |
| 2.9003 | 31700 | 0.1094        |
| 2.9094 | 31800 | 0.1296        |
| 2.9186 | 31900 | 0.1149        |
| 2.9277 | 32000 | 0.1146        |
| 2.9369 | 32100 | 0.1147        |
| 2.9460 | 32200 | 0.1045        |
| 2.9552 | 32300 | 0.0962        |
| 2.9643 | 32400 | 0.1065        |
| 2.9735 | 32500 | 0.1169        |
| 2.9826 | 32600 | 0.1162        |
| 2.9918 | 32700 | 0.1134        |

</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: 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}
}
```

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