---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8283932
- loss:MSELoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Through the Southern Gas Corridor pipeline, gas supply to the European
    Union increased from 8.1 billion cubic meters in 2021 to 11.4 billion cubic meters
    in 2022.
  sentences:
  - After this meeting, the monthly amount collected from prosecutors and investigators
    for the building was increased from 460 manats to 480 manats.
  - Məlik-Aslanov 1919-cu il fevralın 18-dək həm də müvəqqəti olaraq ticarət, sənaye
    və ərzaq nazirinin səlahiyyətlərini də yerinə yetirmişdi.
  - Üçüncü mərhələdə isə Şura hər bir layihə üzrə təqdim olunmuş ekspert rəyini, QHT-nin
    Şuranın maliyyə dəstəyi hesabına əvvəlki illərdə həyata keçirdiyi layihənin icra
    vəziyyətini və layihə idarəetmə təcrübəsini nəzərə alaraq yekun qərar qəbul edir.
- source_sentence: '“Azərbaycan Uşaqlar Birliyi”nin sədri Kəmalə Ağazadə isə məsələnin
    Elinanın deyil, digər şəxslərin üzərində fokuslanmasının doğru olmadığını bildirdi:
    “Elinanın intiharı ilə bağlı məsələ bu gün də sosial şəbəkələrdə xeyli müzakirə
    edilir, müxtəlif fikirlər bildirilir.'
  sentences:
  - 1952-ci ilin aprelindən başlayaraq, "Azərbaycan Kültür Dərnəyi" tərəfindən Ankarada
    aylıq "Azərbaycan" jurnalı nəşr olunur.
  - G. Məmmədovanın fikrincə abidənin konstruktiv həllinin analizi, kvadrat təməldən
    dairəvi dacili və səkkizbucaqlı xarici barabana keçidin yelkənlərlə təmin edilməsinə
    əsasən kilsəni təxminən VII-VIII əsrlərə aid etmək mümkündür.
  - However, a signature campaign was conducted in the country to hold a referendum
    on extending Nursultan Nazarbayev’s term, and nearly 5 million signatures were
    collected.
- source_sentence: Thus, we preserve our history, traditions, and culture, and we
    do a lot to support each other.
  sentences:
  - Belə ki, ara yoldan Bakıxanov küçəsinə çıxan “Mercedes”in sürücü Özal Quliyevin
    üstünlük nişanının tələbinə əməl etməməsi qəza ilə nəticələnib.
  - Bundan başqa, onun sözlərinə görə, OPEK+ razılaşması neft bazarının məhsul artıqlığından
    qurtulmasına kömək edib.
  - Onun fikrincə, İranın Azərbaycan vilayətləri də “Cənubi Azərbaycan” olmalıdır.
- source_sentence: It's true that, although Shahriyar, who is in the top four alongside
    Aronyan in the rankings, couldn't win this match.
  sentences:
  - After spending a year in exile, his father Sultan Abdul Hamid sent him to Istanbul
    along with his sisters Ayşe Sultan and Şadiye Sultan, and asked his brother Sultan
    Reşad to arrange their marriages.
  - Bu, ilk dəfədir ki ABŞ hərbi qüvvələri Rusiyanın keçən ay gizli olaraq raketlər
    yerləşdirilməsini ictimai şəkildə təsdiq edir.
  - He noted that the Supreme Court held seven sessions, thoroughly reviewed the lower
    court’s investigation, and upheld the death sentence.
- source_sentence: At the same time, it is no secret that Washington’s strategic plans
    for the Middle East include changing the current Iranian regime, which opposes
    Western interests in the region.
  sentences:
  - Sürücü Ə.Nəzərovla maşındakı digər sərnişinlər Rahim Mahmudov və Anar Bayramov
    isə müxtəlif dərəcəli bədən xəsarətləri ilə Lənkəran Mərkəzi Rayon Xəstəxanasına
    yerləşdirilib.
  - In addition, Turkey was demanding the territory that included the districts of
    Akhaltsikhe, Akhalkalaki, Alexandropol (Gyumri), Surmali, and Nakhchivan.
  - Bu vəziyyət kilsə meydanını düzəltdiyindən və qolları bərabər uzunluqda olan xaç
    planı aydınlaşmadığı üçün bu plan növü qapalı yunan xaçı planı adlandırılır.
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:** 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("sentence_transformers_model_id")
# Run inference
sentences = [
    'At the same time, it is no secret that Washington’s strategic plans for the Middle East include changing the current Iranian regime, which opposes Western interests in the region.',
    'In addition, Turkey was demanding the territory that included the districts of Akhaltsikhe, Akhalkalaki, Alexandropol (Gyumri), Surmali, and Nakhchivan.',
    'Sürücü Ə.Nəzərovla maşındakı digər sərnişinlər Rahim Mahmudov və Anar Bayramov isə müxtəlif dərəcəli bədən xəsarətləri ilə Lənkəran Mərkəzi Rayon Xəstəxanasına yerləşdirilib.',
]
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: 8,283,932 training samples
* Columns: <code>sentence_0</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence_0                                                                       | label                                |
  |:--------|:---------------------------------------------------------------------------------|:-------------------------------------|
  | type    | string                                                                           | list                                 |
  | details | <ul><li>min: 4 tokens</li><li>mean: 29.8 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>size: 384 elements</li></ul> |
* Samples:
  | sentence_0                                                                                                                                                                                                                           | label                                                                                                                            |
  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------|
  | <code>“Biz “Hizbullah”a axan maliyyə dəstəyini dayandırmaq istəyirik və bu məqsədlə ABŞ hökuməti tutarlı məlumat qarşılığında 10 milyon dollaradək mükafat verməklə yanaşı digər tədbirlər də görəcək”, - Evanoff belə deyib.</code> | <code>[-0.022054675966501236, 0.0932646170258522, -0.01854480803012848, -0.025271562859416008, 0.028432276099920273, ...]</code> |
  | <code>Bu dövləti bu gün müxalifətdə olanlar quranda Əli Həsənovun harada nə işlə məşğul olduğu bəlli deyildi.</code>                                                                                                                 | <code>[-0.012831359170377254, 0.022371841594576836, -0.0271938294172287, 0.09667906910181046, 0.009270057082176208, ...]</code>  |
  | <code>APA-nın “Hürriyet” qəzetinə istinadən verdiyi məlumata görə, ABŞ Hərbi Hava Qüvvələrinn Komandanlığı ən son 1991-ci ildə Körfəz savaşında istifadə edilmiş B-52 təyyarələrinin Qətərə göndərildiyini açıqlayıb.</code>         | <code>[-0.01321476697921753, 0.06281372904777527, 0.005026344675570726, -0.004140781704336405, 0.04239720478653908, ...]</code>  |
* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)

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

- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `multi_dataset_batch_sampler`: round_robin

#### 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`: 64
- `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
- `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
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch  | Step   | Training Loss |
|:------:|:------:|:-------------:|
| 0.0039 | 500    | 0.0035        |
| 0.0077 | 1000   | 0.0024        |
| 0.0116 | 1500   | 0.0022        |
| 0.0155 | 2000   | 0.002         |
| 0.0193 | 2500   | 0.0019        |
| 0.0232 | 3000   | 0.0019        |
| 0.0270 | 3500   | 0.0018        |
| 0.0309 | 4000   | 0.0018        |
| 0.0348 | 4500   | 0.0017        |
| 0.0386 | 5000   | 0.0017        |
| 0.0425 | 5500   | 0.0016        |
| 0.0464 | 6000   | 0.0016        |
| 0.0502 | 6500   | 0.0016        |
| 0.0541 | 7000   | 0.0016        |
| 0.0579 | 7500   | 0.0015        |
| 0.0618 | 8000   | 0.0015        |
| 0.0657 | 8500   | 0.0015        |
| 0.0695 | 9000   | 0.0014        |
| 0.0734 | 9500   | 0.0014        |
| 0.0773 | 10000  | 0.0014        |
| 0.0811 | 10500  | 0.0013        |
| 0.0850 | 11000  | 0.0013        |
| 0.0888 | 11500  | 0.0013        |
| 0.0927 | 12000  | 0.0012        |
| 0.0966 | 12500  | 0.0012        |
| 0.1004 | 13000  | 0.0012        |
| 0.1043 | 13500  | 0.0012        |
| 0.1082 | 14000  | 0.0011        |
| 0.1120 | 14500  | 0.0011        |
| 0.1159 | 15000  | 0.0011        |
| 0.1197 | 15500  | 0.0011        |
| 0.1236 | 16000  | 0.0011        |
| 0.1275 | 16500  | 0.001         |
| 0.1313 | 17000  | 0.001         |
| 0.1352 | 17500  | 0.001         |
| 0.1391 | 18000  | 0.001         |
| 0.1429 | 18500  | 0.001         |
| 0.1468 | 19000  | 0.0009        |
| 0.1507 | 19500  | 0.0009        |
| 0.1545 | 20000  | 0.0009        |
| 0.1584 | 20500  | 0.0009        |
| 0.1622 | 21000  | 0.0009        |
| 0.1661 | 21500  | 0.0008        |
| 0.1700 | 22000  | 0.0008        |
| 0.1738 | 22500  | 0.0008        |
| 0.1777 | 23000  | 0.0008        |
| 0.1816 | 23500  | 0.0008        |
| 0.1854 | 24000  | 0.0008        |
| 0.1893 | 24500  | 0.0008        |
| 0.1931 | 25000  | 0.0008        |
| 0.1970 | 25500  | 0.0007        |
| 0.2009 | 26000  | 0.0007        |
| 0.2047 | 26500  | 0.0007        |
| 0.2086 | 27000  | 0.0007        |
| 0.2125 | 27500  | 0.0007        |
| 0.2163 | 28000  | 0.0007        |
| 0.2202 | 28500  | 0.0007        |
| 0.2240 | 29000  | 0.0007        |
| 0.2279 | 29500  | 0.0007        |
| 0.2318 | 30000  | 0.0007        |
| 0.2356 | 30500  | 0.0007        |
| 0.2395 | 31000  | 0.0007        |
| 0.2434 | 31500  | 0.0006        |
| 0.2472 | 32000  | 0.0006        |
| 0.2511 | 32500  | 0.0006        |
| 0.2550 | 33000  | 0.0006        |
| 0.2588 | 33500  | 0.0006        |
| 0.2627 | 34000  | 0.0006        |
| 0.2665 | 34500  | 0.0006        |
| 0.2704 | 35000  | 0.0006        |
| 0.2743 | 35500  | 0.0006        |
| 0.2781 | 36000  | 0.0006        |
| 0.2820 | 36500  | 0.0006        |
| 0.2859 | 37000  | 0.0006        |
| 0.2897 | 37500  | 0.0006        |
| 0.2936 | 38000  | 0.0006        |
| 0.2974 | 38500  | 0.0006        |
| 0.3013 | 39000  | 0.0006        |
| 0.3052 | 39500  | 0.0006        |
| 0.3090 | 40000  | 0.0006        |
| 0.3129 | 40500  | 0.0006        |
| 0.3168 | 41000  | 0.0006        |
| 0.3206 | 41500  | 0.0005        |
| 0.3245 | 42000  | 0.0005        |
| 0.3283 | 42500  | 0.0005        |
| 0.3322 | 43000  | 0.0005        |
| 0.3361 | 43500  | 0.0005        |
| 0.3399 | 44000  | 0.0005        |
| 0.3438 | 44500  | 0.0005        |
| 0.3477 | 45000  | 0.0005        |
| 0.3515 | 45500  | 0.0005        |
| 0.3554 | 46000  | 0.0005        |
| 0.3592 | 46500  | 0.0005        |
| 0.3631 | 47000  | 0.0005        |
| 0.3670 | 47500  | 0.0005        |
| 0.3708 | 48000  | 0.0005        |
| 0.3747 | 48500  | 0.0005        |
| 0.3786 | 49000  | 0.0005        |
| 0.3824 | 49500  | 0.0005        |
| 0.3863 | 50000  | 0.0005        |
| 0.3902 | 50500  | 0.0005        |
| 0.3940 | 51000  | 0.0005        |
| 0.3979 | 51500  | 0.0005        |
| 0.4017 | 52000  | 0.0005        |
| 0.4056 | 52500  | 0.0005        |
| 0.4095 | 53000  | 0.0005        |
| 0.4133 | 53500  | 0.0005        |
| 0.4172 | 54000  | 0.0005        |
| 0.4211 | 54500  | 0.0005        |
| 0.4249 | 55000  | 0.0005        |
| 0.4288 | 55500  | 0.0005        |
| 0.4326 | 56000  | 0.0005        |
| 0.4365 | 56500  | 0.0005        |
| 0.4404 | 57000  | 0.0005        |
| 0.4442 | 57500  | 0.0005        |
| 0.4481 | 58000  | 0.0005        |
| 0.4520 | 58500  | 0.0005        |
| 0.4558 | 59000  | 0.0005        |
| 0.4597 | 59500  | 0.0005        |
| 0.4635 | 60000  | 0.0005        |
| 0.4674 | 60500  | 0.0005        |
| 0.4713 | 61000  | 0.0005        |
| 0.4751 | 61500  | 0.0005        |
| 0.4790 | 62000  | 0.0005        |
| 0.4829 | 62500  | 0.0005        |
| 0.4867 | 63000  | 0.0005        |
| 0.4906 | 63500  | 0.0005        |
| 0.4944 | 64000  | 0.0005        |
| 0.4983 | 64500  | 0.0005        |
| 0.5022 | 65000  | 0.0005        |
| 0.5060 | 65500  | 0.0004        |
| 0.5099 | 66000  | 0.0004        |
| 0.5138 | 66500  | 0.0004        |
| 0.5176 | 67000  | 0.0004        |
| 0.5215 | 67500  | 0.0004        |
| 0.5254 | 68000  | 0.0004        |
| 0.5292 | 68500  | 0.0004        |
| 0.5331 | 69000  | 0.0004        |
| 0.5369 | 69500  | 0.0004        |
| 0.5408 | 70000  | 0.0004        |
| 0.5447 | 70500  | 0.0004        |
| 0.5485 | 71000  | 0.0004        |
| 0.5524 | 71500  | 0.0004        |
| 0.5563 | 72000  | 0.0004        |
| 0.5601 | 72500  | 0.0004        |
| 0.5640 | 73000  | 0.0004        |
| 0.5678 | 73500  | 0.0004        |
| 0.5717 | 74000  | 0.0004        |
| 0.5756 | 74500  | 0.0004        |
| 0.5794 | 75000  | 0.0004        |
| 0.5833 | 75500  | 0.0004        |
| 0.5872 | 76000  | 0.0004        |
| 0.5910 | 76500  | 0.0004        |
| 0.5949 | 77000  | 0.0004        |
| 0.5987 | 77500  | 0.0004        |
| 0.6026 | 78000  | 0.0004        |
| 0.6065 | 78500  | 0.0004        |
| 0.6103 | 79000  | 0.0004        |
| 0.6142 | 79500  | 0.0004        |
| 0.6181 | 80000  | 0.0004        |
| 0.6219 | 80500  | 0.0004        |
| 0.6258 | 81000  | 0.0004        |
| 0.6296 | 81500  | 0.0004        |
| 0.6335 | 82000  | 0.0004        |
| 0.6374 | 82500  | 0.0004        |
| 0.6412 | 83000  | 0.0004        |
| 0.6451 | 83500  | 0.0004        |
| 0.6490 | 84000  | 0.0004        |
| 0.6528 | 84500  | 0.0004        |
| 0.6567 | 85000  | 0.0004        |
| 0.6606 | 85500  | 0.0004        |
| 0.6644 | 86000  | 0.0004        |
| 0.6683 | 86500  | 0.0004        |
| 0.6721 | 87000  | 0.0004        |
| 0.6760 | 87500  | 0.0004        |
| 0.6799 | 88000  | 0.0004        |
| 0.6837 | 88500  | 0.0004        |
| 0.6876 | 89000  | 0.0004        |
| 0.6915 | 89500  | 0.0004        |
| 0.6953 | 90000  | 0.0004        |
| 0.6992 | 90500  | 0.0004        |
| 0.7030 | 91000  | 0.0004        |
| 0.7069 | 91500  | 0.0004        |
| 0.7108 | 92000  | 0.0004        |
| 0.7146 | 92500  | 0.0004        |
| 0.7185 | 93000  | 0.0004        |
| 0.7224 | 93500  | 0.0004        |
| 0.7262 | 94000  | 0.0004        |
| 0.7301 | 94500  | 0.0004        |
| 0.7339 | 95000  | 0.0004        |
| 0.7378 | 95500  | 0.0004        |
| 0.7417 | 96000  | 0.0004        |
| 0.7455 | 96500  | 0.0004        |
| 0.7494 | 97000  | 0.0004        |
| 0.7533 | 97500  | 0.0004        |
| 0.7571 | 98000  | 0.0004        |
| 0.7610 | 98500  | 0.0004        |
| 0.7649 | 99000  | 0.0004        |
| 0.7687 | 99500  | 0.0004        |
| 0.7726 | 100000 | 0.0004        |
| 0.7764 | 100500 | 0.0004        |
| 0.7803 | 101000 | 0.0004        |
| 0.7842 | 101500 | 0.0004        |
| 0.7880 | 102000 | 0.0004        |
| 0.7919 | 102500 | 0.0004        |
| 0.7958 | 103000 | 0.0004        |
| 0.7996 | 103500 | 0.0004        |
| 0.8035 | 104000 | 0.0004        |
| 0.8073 | 104500 | 0.0004        |
| 0.8112 | 105000 | 0.0004        |
| 0.8151 | 105500 | 0.0004        |
| 0.8189 | 106000 | 0.0004        |
| 0.8228 | 106500 | 0.0004        |
| 0.8267 | 107000 | 0.0004        |
| 0.8305 | 107500 | 0.0004        |
| 0.8344 | 108000 | 0.0004        |
| 0.8382 | 108500 | 0.0004        |
| 0.8421 | 109000 | 0.0004        |
| 0.8460 | 109500 | 0.0004        |
| 0.8498 | 110000 | 0.0004        |
| 0.8537 | 110500 | 0.0004        |
| 0.8576 | 111000 | 0.0004        |
| 0.8614 | 111500 | 0.0004        |
| 0.8653 | 112000 | 0.0004        |
| 0.8691 | 112500 | 0.0004        |
| 0.8730 | 113000 | 0.0004        |
| 0.8769 | 113500 | 0.0004        |
| 0.8807 | 114000 | 0.0004        |
| 0.8846 | 114500 | 0.0004        |
| 0.8885 | 115000 | 0.0004        |
| 0.8923 | 115500 | 0.0004        |
| 0.8962 | 116000 | 0.0004        |
| 0.9001 | 116500 | 0.0004        |
| 0.9039 | 117000 | 0.0004        |
| 0.9078 | 117500 | 0.0004        |
| 0.9116 | 118000 | 0.0004        |
| 0.9155 | 118500 | 0.0004        |
| 0.9194 | 119000 | 0.0004        |
| 0.9232 | 119500 | 0.0004        |
| 0.9271 | 120000 | 0.0004        |
| 0.9310 | 120500 | 0.0004        |
| 0.9348 | 121000 | 0.0004        |
| 0.9387 | 121500 | 0.0004        |
| 0.9425 | 122000 | 0.0004        |
| 0.9464 | 122500 | 0.0004        |
| 0.9503 | 123000 | 0.0004        |
| 0.9541 | 123500 | 0.0004        |
| 0.9580 | 124000 | 0.0004        |
| 0.9619 | 124500 | 0.0004        |
| 0.9657 | 125000 | 0.0004        |
| 0.9696 | 125500 | 0.0004        |
| 0.9734 | 126000 | 0.0004        |
| 0.9773 | 126500 | 0.0004        |
| 0.9812 | 127000 | 0.0004        |
| 0.9850 | 127500 | 0.0004        |
| 0.9889 | 128000 | 0.0004        |
| 0.9928 | 128500 | 0.0004        |
| 0.9966 | 129000 | 0.0004        |
| 1.0005 | 129500 | 0.0004        |
| 1.0043 | 130000 | 0.0004        |
| 1.0082 | 130500 | 0.0004        |
| 1.0121 | 131000 | 0.0004        |
| 1.0159 | 131500 | 0.0004        |
| 1.0198 | 132000 | 0.0004        |
| 1.0237 | 132500 | 0.0004        |
| 1.0275 | 133000 | 0.0004        |
| 1.0314 | 133500 | 0.0004        |
| 1.0353 | 134000 | 0.0004        |
| 1.0391 | 134500 | 0.0004        |
| 1.0430 | 135000 | 0.0004        |
| 1.0468 | 135500 | 0.0004        |
| 1.0507 | 136000 | 0.0004        |
| 1.0546 | 136500 | 0.0004        |
| 1.0584 | 137000 | 0.0004        |
| 1.0623 | 137500 | 0.0004        |
| 1.0662 | 138000 | 0.0004        |
| 1.0700 | 138500 | 0.0004        |
| 1.0739 | 139000 | 0.0004        |
| 1.0777 | 139500 | 0.0004        |
| 1.0816 | 140000 | 0.0004        |
| 1.0855 | 140500 | 0.0004        |
| 1.0893 | 141000 | 0.0004        |
| 1.0932 | 141500 | 0.0004        |
| 1.0971 | 142000 | 0.0004        |
| 1.1009 | 142500 | 0.0004        |
| 1.1048 | 143000 | 0.0004        |
| 1.1086 | 143500 | 0.0004        |
| 1.1125 | 144000 | 0.0004        |
| 1.1164 | 144500 | 0.0004        |
| 1.1202 | 145000 | 0.0004        |
| 1.1241 | 145500 | 0.0004        |
| 1.1280 | 146000 | 0.0004        |
| 1.1318 | 146500 | 0.0004        |
| 1.1357 | 147000 | 0.0004        |
| 1.1396 | 147500 | 0.0004        |
| 1.1434 | 148000 | 0.0004        |
| 1.1473 | 148500 | 0.0004        |
| 1.1511 | 149000 | 0.0004        |
| 1.1550 | 149500 | 0.0004        |
| 1.1589 | 150000 | 0.0004        |
| 1.1627 | 150500 | 0.0004        |
| 1.1666 | 151000 | 0.0004        |
| 1.1705 | 151500 | 0.0004        |
| 1.1743 | 152000 | 0.0004        |
| 1.1782 | 152500 | 0.0004        |
| 1.1820 | 153000 | 0.0004        |
| 1.1859 | 153500 | 0.0004        |
| 1.1898 | 154000 | 0.0004        |
| 1.1936 | 154500 | 0.0004        |
| 1.1975 | 155000 | 0.0004        |
| 1.2014 | 155500 | 0.0003        |
| 1.2052 | 156000 | 0.0003        |
| 1.2091 | 156500 | 0.0004        |
| 1.2129 | 157000 | 0.0003        |
| 1.2168 | 157500 | 0.0004        |
| 1.2207 | 158000 | 0.0003        |
| 1.2245 | 158500 | 0.0003        |
| 1.2284 | 159000 | 0.0003        |
| 1.2323 | 159500 | 0.0003        |
| 1.2361 | 160000 | 0.0003        |
| 1.2400 | 160500 | 0.0003        |
| 1.2438 | 161000 | 0.0003        |
| 1.2477 | 161500 | 0.0003        |
| 1.2516 | 162000 | 0.0003        |
| 1.2554 | 162500 | 0.0003        |
| 1.2593 | 163000 | 0.0003        |
| 1.2632 | 163500 | 0.0003        |
| 1.2670 | 164000 | 0.0003        |
| 1.2709 | 164500 | 0.0003        |
| 1.2748 | 165000 | 0.0003        |
| 1.2786 | 165500 | 0.0003        |
| 1.2825 | 166000 | 0.0003        |
| 1.2863 | 166500 | 0.0003        |
| 1.2902 | 167000 | 0.0003        |
| 1.2941 | 167500 | 0.0003        |
| 1.2979 | 168000 | 0.0003        |
| 1.3018 | 168500 | 0.0003        |
| 1.3057 | 169000 | 0.0003        |
| 1.3095 | 169500 | 0.0003        |
| 1.3134 | 170000 | 0.0003        |
| 1.3172 | 170500 | 0.0003        |
| 1.3211 | 171000 | 0.0003        |
| 1.3250 | 171500 | 0.0003        |
| 1.3288 | 172000 | 0.0003        |
| 1.3327 | 172500 | 0.0003        |
| 1.3366 | 173000 | 0.0003        |
| 1.3404 | 173500 | 0.0003        |
| 1.3443 | 174000 | 0.0003        |
| 1.3481 | 174500 | 0.0003        |
| 1.3520 | 175000 | 0.0003        |
| 1.3559 | 175500 | 0.0003        |
| 1.3597 | 176000 | 0.0003        |
| 1.3636 | 176500 | 0.0003        |
| 1.3675 | 177000 | 0.0003        |
| 1.3713 | 177500 | 0.0003        |
| 1.3752 | 178000 | 0.0003        |
| 1.3790 | 178500 | 0.0003        |
| 1.3829 | 179000 | 0.0003        |
| 1.3868 | 179500 | 0.0003        |
| 1.3906 | 180000 | 0.0003        |
| 1.3945 | 180500 | 0.0003        |
| 1.3984 | 181000 | 0.0003        |
| 1.4022 | 181500 | 0.0003        |
| 1.4061 | 182000 | 0.0003        |
| 1.4100 | 182500 | 0.0003        |
| 1.4138 | 183000 | 0.0003        |
| 1.4177 | 183500 | 0.0003        |
| 1.4215 | 184000 | 0.0003        |
| 1.4254 | 184500 | 0.0003        |
| 1.4293 | 185000 | 0.0003        |
| 1.4331 | 185500 | 0.0003        |
| 1.4370 | 186000 | 0.0003        |
| 1.4409 | 186500 | 0.0003        |
| 1.4447 | 187000 | 0.0003        |
| 1.4486 | 187500 | 0.0003        |
| 1.4524 | 188000 | 0.0003        |
| 1.4563 | 188500 | 0.0003        |
| 1.4602 | 189000 | 0.0003        |
| 1.4640 | 189500 | 0.0003        |
| 1.4679 | 190000 | 0.0003        |
| 1.4718 | 190500 | 0.0003        |
| 1.4756 | 191000 | 0.0003        |
| 1.4795 | 191500 | 0.0003        |
| 1.4833 | 192000 | 0.0003        |
| 1.4872 | 192500 | 0.0003        |
| 1.4911 | 193000 | 0.0003        |
| 1.4949 | 193500 | 0.0003        |
| 1.4988 | 194000 | 0.0003        |
| 1.5027 | 194500 | 0.0003        |
| 1.5065 | 195000 | 0.0003        |
| 1.5104 | 195500 | 0.0003        |
| 1.5143 | 196000 | 0.0003        |
| 1.5181 | 196500 | 0.0003        |
| 1.5220 | 197000 | 0.0003        |
| 1.5258 | 197500 | 0.0003        |
| 1.5297 | 198000 | 0.0003        |
| 1.5336 | 198500 | 0.0003        |
| 1.5374 | 199000 | 0.0003        |
| 1.5413 | 199500 | 0.0003        |
| 1.5452 | 200000 | 0.0003        |
| 1.5490 | 200500 | 0.0003        |
| 1.5529 | 201000 | 0.0003        |
| 1.5567 | 201500 | 0.0003        |
| 1.5606 | 202000 | 0.0003        |
| 1.5645 | 202500 | 0.0003        |
| 1.5683 | 203000 | 0.0003        |
| 1.5722 | 203500 | 0.0003        |
| 1.5761 | 204000 | 0.0003        |
| 1.5799 | 204500 | 0.0003        |
| 1.5838 | 205000 | 0.0003        |
| 1.5876 | 205500 | 0.0003        |
| 1.5915 | 206000 | 0.0003        |
| 1.5954 | 206500 | 0.0003        |
| 1.5992 | 207000 | 0.0003        |
| 1.6031 | 207500 | 0.0003        |
| 1.6070 | 208000 | 0.0003        |
| 1.6108 | 208500 | 0.0003        |
| 1.6147 | 209000 | 0.0003        |
| 1.6185 | 209500 | 0.0003        |
| 1.6224 | 210000 | 0.0003        |
| 1.6263 | 210500 | 0.0003        |
| 1.6301 | 211000 | 0.0003        |
| 1.6340 | 211500 | 0.0003        |
| 1.6379 | 212000 | 0.0003        |
| 1.6417 | 212500 | 0.0003        |
| 1.6456 | 213000 | 0.0003        |
| 1.6495 | 213500 | 0.0003        |
| 1.6533 | 214000 | 0.0003        |
| 1.6572 | 214500 | 0.0003        |
| 1.6610 | 215000 | 0.0003        |
| 1.6649 | 215500 | 0.0003        |
| 1.6688 | 216000 | 0.0003        |
| 1.6726 | 216500 | 0.0003        |
| 1.6765 | 217000 | 0.0003        |
| 1.6804 | 217500 | 0.0003        |
| 1.6842 | 218000 | 0.0003        |
| 1.6881 | 218500 | 0.0003        |
| 1.6919 | 219000 | 0.0003        |
| 1.6958 | 219500 | 0.0003        |
| 1.6997 | 220000 | 0.0003        |
| 1.7035 | 220500 | 0.0003        |
| 1.7074 | 221000 | 0.0003        |
| 1.7113 | 221500 | 0.0003        |
| 1.7151 | 222000 | 0.0003        |
| 1.7190 | 222500 | 0.0003        |
| 1.7228 | 223000 | 0.0003        |
| 1.7267 | 223500 | 0.0003        |
| 1.7306 | 224000 | 0.0003        |
| 1.7344 | 224500 | 0.0003        |
| 1.7383 | 225000 | 0.0003        |
| 1.7422 | 225500 | 0.0003        |
| 1.7460 | 226000 | 0.0003        |
| 1.7499 | 226500 | 0.0003        |
| 1.7537 | 227000 | 0.0003        |
| 1.7576 | 227500 | 0.0003        |
| 1.7615 | 228000 | 0.0003        |
| 1.7653 | 228500 | 0.0003        |
| 1.7692 | 229000 | 0.0003        |
| 1.7731 | 229500 | 0.0003        |
| 1.7769 | 230000 | 0.0003        |
| 1.7808 | 230500 | 0.0003        |
| 1.7847 | 231000 | 0.0003        |
| 1.7885 | 231500 | 0.0003        |
| 1.7924 | 232000 | 0.0003        |
| 1.7962 | 232500 | 0.0003        |
| 1.8001 | 233000 | 0.0003        |
| 1.8040 | 233500 | 0.0003        |
| 1.8078 | 234000 | 0.0003        |
| 1.8117 | 234500 | 0.0003        |
| 1.8156 | 235000 | 0.0003        |
| 1.8194 | 235500 | 0.0003        |
| 1.8233 | 236000 | 0.0003        |
| 1.8271 | 236500 | 0.0003        |
| 1.8310 | 237000 | 0.0003        |
| 1.8349 | 237500 | 0.0003        |
| 1.8387 | 238000 | 0.0003        |
| 1.8426 | 238500 | 0.0003        |
| 1.8465 | 239000 | 0.0003        |
| 1.8503 | 239500 | 0.0003        |
| 1.8542 | 240000 | 0.0003        |
| 1.8580 | 240500 | 0.0003        |
| 1.8619 | 241000 | 0.0003        |
| 1.8658 | 241500 | 0.0003        |
| 1.8696 | 242000 | 0.0003        |
| 1.8735 | 242500 | 0.0003        |
| 1.8774 | 243000 | 0.0003        |
| 1.8812 | 243500 | 0.0003        |
| 1.8851 | 244000 | 0.0003        |
| 1.8889 | 244500 | 0.0003        |
| 1.8928 | 245000 | 0.0003        |
| 1.8967 | 245500 | 0.0003        |
| 1.9005 | 246000 | 0.0003        |
| 1.9044 | 246500 | 0.0003        |
| 1.9083 | 247000 | 0.0003        |
| 1.9121 | 247500 | 0.0003        |
| 1.9160 | 248000 | 0.0003        |
| 1.9199 | 248500 | 0.0003        |
| 1.9237 | 249000 | 0.0003        |
| 1.9276 | 249500 | 0.0003        |
| 1.9314 | 250000 | 0.0003        |
| 1.9353 | 250500 | 0.0003        |
| 1.9392 | 251000 | 0.0003        |
| 1.9430 | 251500 | 0.0003        |
| 1.9469 | 252000 | 0.0003        |
| 1.9508 | 252500 | 0.0003        |
| 1.9546 | 253000 | 0.0003        |
| 1.9585 | 253500 | 0.0003        |
| 1.9623 | 254000 | 0.0003        |
| 1.9662 | 254500 | 0.0003        |
| 1.9701 | 255000 | 0.0003        |
| 1.9739 | 255500 | 0.0003        |
| 1.9778 | 256000 | 0.0003        |
| 1.9817 | 256500 | 0.0003        |
| 1.9855 | 257000 | 0.0003        |
| 1.9894 | 257500 | 0.0003        |
| 1.9932 | 258000 | 0.0003        |
| 1.9971 | 258500 | 0.0003        |
| 2.0010 | 259000 | 0.0003        |
| 2.0048 | 259500 | 0.0003        |
| 2.0087 | 260000 | 0.0003        |
| 2.0126 | 260500 | 0.0003        |
| 2.0164 | 261000 | 0.0003        |
| 2.0203 | 261500 | 0.0003        |
| 2.0242 | 262000 | 0.0003        |
| 2.0280 | 262500 | 0.0003        |
| 2.0319 | 263000 | 0.0003        |
| 2.0357 | 263500 | 0.0003        |
| 2.0396 | 264000 | 0.0003        |
| 2.0435 | 264500 | 0.0003        |
| 2.0473 | 265000 | 0.0003        |
| 2.0512 | 265500 | 0.0003        |
| 2.0551 | 266000 | 0.0003        |
| 2.0589 | 266500 | 0.0003        |
| 2.0628 | 267000 | 0.0003        |
| 2.0666 | 267500 | 0.0003        |
| 2.0705 | 268000 | 0.0003        |
| 2.0744 | 268500 | 0.0003        |
| 2.0782 | 269000 | 0.0003        |
| 2.0821 | 269500 | 0.0003        |
| 2.0860 | 270000 | 0.0003        |
| 2.0898 | 270500 | 0.0003        |
| 2.0937 | 271000 | 0.0003        |
| 2.0975 | 271500 | 0.0003        |
| 2.1014 | 272000 | 0.0003        |
| 2.1053 | 272500 | 0.0003        |
| 2.1091 | 273000 | 0.0003        |
| 2.1130 | 273500 | 0.0003        |
| 2.1169 | 274000 | 0.0003        |
| 2.1207 | 274500 | 0.0003        |
| 2.1246 | 275000 | 0.0003        |
| 2.1284 | 275500 | 0.0003        |
| 2.1323 | 276000 | 0.0003        |
| 2.1362 | 276500 | 0.0003        |
| 2.1400 | 277000 | 0.0003        |
| 2.1439 | 277500 | 0.0003        |
| 2.1478 | 278000 | 0.0003        |
| 2.1516 | 278500 | 0.0003        |
| 2.1555 | 279000 | 0.0003        |
| 2.1594 | 279500 | 0.0003        |
| 2.1632 | 280000 | 0.0003        |
| 2.1671 | 280500 | 0.0003        |
| 2.1709 | 281000 | 0.0003        |
| 2.1748 | 281500 | 0.0003        |
| 2.1787 | 282000 | 0.0003        |
| 2.1825 | 282500 | 0.0003        |
| 2.1864 | 283000 | 0.0003        |
| 2.1903 | 283500 | 0.0003        |
| 2.1941 | 284000 | 0.0003        |
| 2.1980 | 284500 | 0.0003        |
| 2.2018 | 285000 | 0.0003        |
| 2.2057 | 285500 | 0.0003        |
| 2.2096 | 286000 | 0.0003        |
| 2.2134 | 286500 | 0.0003        |
| 2.2173 | 287000 | 0.0003        |
| 2.2212 | 287500 | 0.0003        |
| 2.2250 | 288000 | 0.0003        |
| 2.2289 | 288500 | 0.0003        |
| 2.2327 | 289000 | 0.0003        |
| 2.2366 | 289500 | 0.0003        |
| 2.2405 | 290000 | 0.0003        |
| 2.2443 | 290500 | 0.0003        |
| 2.2482 | 291000 | 0.0003        |
| 2.2521 | 291500 | 0.0003        |
| 2.2559 | 292000 | 0.0003        |
| 2.2598 | 292500 | 0.0003        |
| 2.2636 | 293000 | 0.0003        |
| 2.2675 | 293500 | 0.0003        |
| 2.2714 | 294000 | 0.0003        |
| 2.2752 | 294500 | 0.0003        |
| 2.2791 | 295000 | 0.0003        |
| 2.2830 | 295500 | 0.0003        |
| 2.2868 | 296000 | 0.0003        |
| 2.2907 | 296500 | 0.0003        |
| 2.2946 | 297000 | 0.0003        |
| 2.2984 | 297500 | 0.0003        |
| 2.3023 | 298000 | 0.0003        |
| 2.3061 | 298500 | 0.0003        |
| 2.3100 | 299000 | 0.0003        |
| 2.3139 | 299500 | 0.0003        |
| 2.3177 | 300000 | 0.0003        |
| 2.3216 | 300500 | 0.0003        |
| 2.3255 | 301000 | 0.0003        |
| 2.3293 | 301500 | 0.0003        |
| 2.3332 | 302000 | 0.0003        |
| 2.3370 | 302500 | 0.0003        |
| 2.3409 | 303000 | 0.0003        |
| 2.3448 | 303500 | 0.0003        |
| 2.3486 | 304000 | 0.0003        |
| 2.3525 | 304500 | 0.0003        |
| 2.3564 | 305000 | 0.0003        |
| 2.3602 | 305500 | 0.0003        |
| 2.3641 | 306000 | 0.0003        |
| 2.3679 | 306500 | 0.0003        |
| 2.3718 | 307000 | 0.0003        |
| 2.3757 | 307500 | 0.0003        |
| 2.3795 | 308000 | 0.0003        |
| 2.3834 | 308500 | 0.0003        |
| 2.3873 | 309000 | 0.0003        |
| 2.3911 | 309500 | 0.0003        |
| 2.3950 | 310000 | 0.0003        |
| 2.3989 | 310500 | 0.0003        |
| 2.4027 | 311000 | 0.0003        |
| 2.4066 | 311500 | 0.0003        |
| 2.4104 | 312000 | 0.0003        |
| 2.4143 | 312500 | 0.0003        |
| 2.4182 | 313000 | 0.0003        |
| 2.4220 | 313500 | 0.0003        |
| 2.4259 | 314000 | 0.0003        |
| 2.4298 | 314500 | 0.0003        |
| 2.4336 | 315000 | 0.0003        |
| 2.4375 | 315500 | 0.0003        |
| 2.4413 | 316000 | 0.0003        |
| 2.4452 | 316500 | 0.0003        |
| 2.4491 | 317000 | 0.0003        |
| 2.4529 | 317500 | 0.0003        |
| 2.4568 | 318000 | 0.0003        |
| 2.4607 | 318500 | 0.0003        |
| 2.4645 | 319000 | 0.0003        |
| 2.4684 | 319500 | 0.0003        |
| 2.4722 | 320000 | 0.0003        |
| 2.4761 | 320500 | 0.0003        |
| 2.4800 | 321000 | 0.0003        |
| 2.4838 | 321500 | 0.0003        |
| 2.4877 | 322000 | 0.0003        |
| 2.4916 | 322500 | 0.0003        |
| 2.4954 | 323000 | 0.0003        |
| 2.4993 | 323500 | 0.0003        |
| 2.5031 | 324000 | 0.0003        |
| 2.5070 | 324500 | 0.0003        |
| 2.5109 | 325000 | 0.0003        |
| 2.5147 | 325500 | 0.0003        |
| 2.5186 | 326000 | 0.0003        |
| 2.5225 | 326500 | 0.0003        |
| 2.5263 | 327000 | 0.0003        |
| 2.5302 | 327500 | 0.0003        |
| 2.5341 | 328000 | 0.0003        |
| 2.5379 | 328500 | 0.0003        |
| 2.5418 | 329000 | 0.0003        |
| 2.5456 | 329500 | 0.0003        |
| 2.5495 | 330000 | 0.0003        |
| 2.5534 | 330500 | 0.0003        |
| 2.5572 | 331000 | 0.0003        |
| 2.5611 | 331500 | 0.0003        |
| 2.5650 | 332000 | 0.0003        |
| 2.5688 | 332500 | 0.0003        |
| 2.5727 | 333000 | 0.0003        |
| 2.5765 | 333500 | 0.0003        |
| 2.5804 | 334000 | 0.0003        |
| 2.5843 | 334500 | 0.0003        |
| 2.5881 | 335000 | 0.0003        |
| 2.5920 | 335500 | 0.0003        |
| 2.5959 | 336000 | 0.0003        |
| 2.5997 | 336500 | 0.0003        |
| 2.6036 | 337000 | 0.0003        |
| 2.6074 | 337500 | 0.0003        |
| 2.6113 | 338000 | 0.0003        |
| 2.6152 | 338500 | 0.0003        |
| 2.6190 | 339000 | 0.0003        |
| 2.6229 | 339500 | 0.0003        |
| 2.6268 | 340000 | 0.0003        |
| 2.6306 | 340500 | 0.0003        |
| 2.6345 | 341000 | 0.0003        |
| 2.6383 | 341500 | 0.0003        |
| 2.6422 | 342000 | 0.0003        |
| 2.6461 | 342500 | 0.0003        |
| 2.6499 | 343000 | 0.0003        |
| 2.6538 | 343500 | 0.0003        |
| 2.6577 | 344000 | 0.0003        |
| 2.6615 | 344500 | 0.0003        |
| 2.6654 | 345000 | 0.0003        |
| 2.6693 | 345500 | 0.0003        |
| 2.6731 | 346000 | 0.0003        |
| 2.6770 | 346500 | 0.0003        |
| 2.6808 | 347000 | 0.0003        |
| 2.6847 | 347500 | 0.0003        |
| 2.6886 | 348000 | 0.0003        |
| 2.6924 | 348500 | 0.0003        |
| 2.6963 | 349000 | 0.0003        |
| 2.7002 | 349500 | 0.0003        |
| 2.7040 | 350000 | 0.0003        |
| 2.7079 | 350500 | 0.0003        |
| 2.7117 | 351000 | 0.0003        |
| 2.7156 | 351500 | 0.0003        |
| 2.7195 | 352000 | 0.0003        |
| 2.7233 | 352500 | 0.0003        |
| 2.7272 | 353000 | 0.0003        |
| 2.7311 | 353500 | 0.0003        |
| 2.7349 | 354000 | 0.0003        |
| 2.7388 | 354500 | 0.0003        |
| 2.7426 | 355000 | 0.0003        |
| 2.7465 | 355500 | 0.0003        |
| 2.7504 | 356000 | 0.0003        |
| 2.7542 | 356500 | 0.0003        |
| 2.7581 | 357000 | 0.0003        |
| 2.7620 | 357500 | 0.0003        |
| 2.7658 | 358000 | 0.0003        |
| 2.7697 | 358500 | 0.0003        |
| 2.7736 | 359000 | 0.0003        |
| 2.7774 | 359500 | 0.0003        |
| 2.7813 | 360000 | 0.0003        |
| 2.7851 | 360500 | 0.0003        |
| 2.7890 | 361000 | 0.0003        |
| 2.7929 | 361500 | 0.0003        |
| 2.7967 | 362000 | 0.0003        |
| 2.8006 | 362500 | 0.0003        |
| 2.8045 | 363000 | 0.0003        |
| 2.8083 | 363500 | 0.0003        |
| 2.8122 | 364000 | 0.0003        |
| 2.8160 | 364500 | 0.0003        |
| 2.8199 | 365000 | 0.0003        |
| 2.8238 | 365500 | 0.0003        |
| 2.8276 | 366000 | 0.0003        |
| 2.8315 | 366500 | 0.0003        |
| 2.8354 | 367000 | 0.0003        |
| 2.8392 | 367500 | 0.0003        |
| 2.8431 | 368000 | 0.0003        |
| 2.8469 | 368500 | 0.0003        |
| 2.8508 | 369000 | 0.0003        |
| 2.8547 | 369500 | 0.0003        |
| 2.8585 | 370000 | 0.0003        |
| 2.8624 | 370500 | 0.0003        |
| 2.8663 | 371000 | 0.0003        |
| 2.8701 | 371500 | 0.0003        |
| 2.8740 | 372000 | 0.0003        |
| 2.8778 | 372500 | 0.0003        |
| 2.8817 | 373000 | 0.0003        |
| 2.8856 | 373500 | 0.0003        |
| 2.8894 | 374000 | 0.0003        |
| 2.8933 | 374500 | 0.0003        |
| 2.8972 | 375000 | 0.0003        |
| 2.9010 | 375500 | 0.0003        |
| 2.9049 | 376000 | 0.0003        |
| 2.9088 | 376500 | 0.0003        |
| 2.9126 | 377000 | 0.0003        |
| 2.9165 | 377500 | 0.0003        |
| 2.9203 | 378000 | 0.0003        |
| 2.9242 | 378500 | 0.0003        |
| 2.9281 | 379000 | 0.0003        |
| 2.9319 | 379500 | 0.0003        |
| 2.9358 | 380000 | 0.0003        |
| 2.9397 | 380500 | 0.0003        |
| 2.9435 | 381000 | 0.0003        |
| 2.9474 | 381500 | 0.0003        |
| 2.9512 | 382000 | 0.0003        |
| 2.9551 | 382500 | 0.0003        |
| 2.9590 | 383000 | 0.0003        |
| 2.9628 | 383500 | 0.0003        |
| 2.9667 | 384000 | 0.0003        |
| 2.9706 | 384500 | 0.0003        |
| 2.9744 | 385000 | 0.0003        |
| 2.9783 | 385500 | 0.0003        |
| 2.9821 | 386000 | 0.0003        |
| 2.9860 | 386500 | 0.0003        |
| 2.9899 | 387000 | 0.0003        |
| 2.9937 | 387500 | 0.0003        |
| 2.9976 | 388000 | 0.0003        |

</details>

### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.5.1+cu121
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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",
}
```

#### MSELoss
```bibtex
@inproceedings{reimers-2020-multilingual-sentence-bert,
    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2020",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2004.09813",
}
```

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