---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:131157
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/LaBSE
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/LaBSE
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE). 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:** [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): 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_falabse_onV9e")
# 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, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 131,157 training samples
* Columns: anchor and positive
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 6 tokens
- mean: 15.78 tokens
- max: 86 tokens
| - min: 5 tokens
- mean: 15.52 tokens
- max: 57 tokens
|
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------|
| 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؟ | چرا سوال من به عنوان نیاز به پیشرفت مشخص شده است؟ |
| چگونه می توانید یک فایل رمزگذاری شده را با دانستن اینکه این یک فایل تصویری است بدون دانستن گسترش پرونده یا کلید ، رمزگشایی کنید؟ | 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؟ |
| احساس می کنم خودکشی می کنم ، چگونه باید با آن برخورد کنم؟ | احساس می کنم خودکشی می کنم.چه کاری باید انجام دهم؟ |
* Loss: [MultipleNegativesRankingLoss](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
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `push_to_hub`: True
- `hub_model_id`: codersan/validadted_falabse_onV9e
- `eval_on_start`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `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`: 5
- `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_falabse_onV9e
- `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
Click to expand
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0 | 0 | - |
| 0.0091 | 100 | 0.1276 |
| 0.0183 | 200 | 0.1092 |
| 0.0274 | 300 | 0.101 |
| 0.0366 | 400 | 0.0908 |
| 0.0457 | 500 | 0.0728 |
| 0.0549 | 600 | 0.0522 |
| 0.0640 | 700 | 0.0532 |
| 0.0732 | 800 | 0.0275 |
| 0.0823 | 900 | 0.0216 |
| 0.0915 | 1000 | 0.0212 |
| 0.1006 | 1100 | 0.0318 |
| 0.1098 | 1200 | 0.0328 |
| 0.1189 | 1300 | 0.0299 |
| 0.1281 | 1400 | 0.0412 |
| 0.1372 | 1500 | 0.0199 |
| 0.1464 | 1600 | 0.0118 |
| 0.1555 | 1700 | 0.034 |
| 0.1647 | 1800 | 0.0282 |
| 0.1738 | 1900 | 0.027 |
| 0.1830 | 2000 | 0.0153 |
| 0.1921 | 2100 | 0.0282 |
| 0.2013 | 2200 | 0.014 |
| 0.2104 | 2300 | 0.0221 |
| 0.2196 | 2400 | 0.0464 |
| 0.2287 | 2500 | 0.0253 |
| 0.2379 | 2600 | 0.0176 |
| 0.2470 | 2700 | 0.0214 |
| 0.2562 | 2800 | 0.0203 |
| 0.2653 | 2900 | 0.0273 |
| 0.2745 | 3000 | 0.0235 |
| 0.2836 | 3100 | 0.0235 |
| 0.2928 | 3200 | 0.0202 |
| 0.3019 | 3300 | 0.014 |
| 0.3111 | 3400 | 0.0274 |
| 0.3202 | 3500 | 0.023 |
| 0.3294 | 3600 | 0.0233 |
| 0.3385 | 3700 | 0.0211 |
| 0.3477 | 3800 | 0.0164 |
| 0.3568 | 3900 | 0.0134 |
| 0.3660 | 4000 | 0.0152 |
| 0.3751 | 4100 | 0.0125 |
| 0.3843 | 4200 | 0.0216 |
| 0.3934 | 4300 | 0.0148 |
| 0.4026 | 4400 | 0.0339 |
| 0.4117 | 4500 | 0.0185 |
| 0.4209 | 4600 | 0.0226 |
| 0.4300 | 4700 | 0.0369 |
| 0.4392 | 4800 | 0.0178 |
| 0.4483 | 4900 | 0.0125 |
| 0.4575 | 5000 | 0.0172 |
| 0.4666 | 5100 | 0.0173 |
| 0.4758 | 5200 | 0.0098 |
| 0.4849 | 5300 | 0.0194 |
| 0.4941 | 5400 | 0.026 |
| 0.5032 | 5500 | 0.0164 |
| 0.5124 | 5600 | 0.0317 |
| 0.5215 | 5700 | 0.016 |
| 0.5306 | 5800 | 0.024 |
| 0.5398 | 5900 | 0.0224 |
| 0.5489 | 6000 | 0.0229 |
| 0.5581 | 6100 | 0.0124 |
| 0.5672 | 6200 | 0.0262 |
| 0.5764 | 6300 | 0.023 |
| 0.5855 | 6400 | 0.026 |
| 0.5947 | 6500 | 0.028 |
| 0.6038 | 6600 | 0.017 |
| 0.6130 | 6700 | 0.0103 |
| 0.6221 | 6800 | 0.0137 |
| 0.6313 | 6900 | 0.0198 |
| 0.6404 | 7000 | 0.0127 |
| 0.6496 | 7100 | 0.0125 |
| 0.6587 | 7200 | 0.0197 |
| 0.6679 | 7300 | 0.0209 |
| 0.6770 | 7400 | 0.0208 |
| 0.6862 | 7500 | 0.0149 |
| 0.6953 | 7600 | 0.017 |
| 0.7045 | 7700 | 0.0228 |
| 0.7136 | 7800 | 0.0161 |
| 0.7228 | 7900 | 0.015 |
| 0.7319 | 8000 | 0.0105 |
| 0.7411 | 8100 | 0.0147 |
| 0.7502 | 8200 | 0.0131 |
| 0.7594 | 8300 | 0.0144 |
| 0.7685 | 8400 | 0.0313 |
| 0.7777 | 8500 | 0.0118 |
| 0.7868 | 8600 | 0.0159 |
| 0.7960 | 8700 | 0.0213 |
| 0.8051 | 8800 | 0.0273 |
| 0.8143 | 8900 | 0.0256 |
| 0.8234 | 9000 | 0.0149 |
| 0.8326 | 9100 | 0.012 |
| 0.8417 | 9200 | 0.0294 |
| 0.8509 | 9300 | 0.0134 |
| 0.8600 | 9400 | 0.0138 |
| 0.8692 | 9500 | 0.0127 |
| 0.8783 | 9600 | 0.0325 |
| 0.8875 | 9700 | 0.0207 |
| 0.8966 | 9800 | 0.0174 |
| 0.9058 | 9900 | 0.0238 |
| 0.9149 | 10000 | 0.0256 |
| 0.9241 | 10100 | 0.0197 |
| 0.9332 | 10200 | 0.0178 |
| 0.9424 | 10300 | 0.0106 |
| 0.9515 | 10400 | 0.0224 |
| 0.9607 | 10500 | 0.0162 |
| 0.9698 | 10600 | 0.0178 |
| 0.9790 | 10700 | 0.0244 |
| 0.9881 | 10800 | 0.0223 |
| 0.9973 | 10900 | 0.0117 |
| 1.0064 | 11000 | 0.0261 |
| 1.0156 | 11100 | 0.02 |
| 1.0247 | 11200 | 0.0155 |
| 1.0339 | 11300 | 0.0193 |
| 1.0430 | 11400 | 0.0312 |
| 1.0522 | 11500 | 0.0222 |
| 1.0613 | 11600 | 0.0302 |
| 1.0704 | 11700 | 0.0126 |
| 1.0796 | 11800 | 0.0123 |
| 1.0887 | 11900 | 0.0064 |
| 1.0979 | 12000 | 0.0083 |
| 1.1070 | 12100 | 0.0143 |
| 1.1162 | 12200 | 0.0181 |
| 1.1253 | 12300 | 0.0311 |
| 1.1345 | 12400 | 0.0097 |
| 1.1436 | 12500 | 0.0083 |
| 1.1528 | 12600 | 0.0125 |
| 1.1619 | 12700 | 0.0169 |
| 1.1711 | 12800 | 0.0192 |
| 1.1802 | 12900 | 0.0086 |
| 1.1894 | 13000 | 0.0171 |
| 1.1985 | 13100 | 0.0108 |
| 1.2077 | 13200 | 0.0079 |
| 1.2168 | 13300 | 0.0304 |
| 1.2260 | 13400 | 0.0134 |
| 1.2351 | 13500 | 0.0124 |
| 1.2443 | 13600 | 0.0057 |
| 1.2534 | 13700 | 0.0174 |
| 1.2626 | 13800 | 0.0195 |
| 1.2717 | 13900 | 0.0164 |
| 1.2809 | 14000 | 0.0115 |
| 1.2900 | 14100 | 0.0152 |
| 1.2992 | 14200 | 0.004 |
| 1.3083 | 14300 | 0.0183 |
| 1.3175 | 14400 | 0.0106 |
| 1.3266 | 14500 | 0.0196 |
| 1.3358 | 14600 | 0.006 |
| 1.3449 | 14700 | 0.0144 |
| 1.3541 | 14800 | 0.0051 |
| 1.3632 | 14900 | 0.004 |
| 1.3724 | 15000 | 0.0091 |
| 1.3815 | 15100 | 0.0054 |
| 1.3907 | 15200 | 0.0115 |
| 1.3998 | 15300 | 0.0156 |
| 1.4090 | 15400 | 0.0069 |
| 1.4181 | 15500 | 0.0133 |
| 1.4273 | 15600 | 0.0177 |
| 1.4364 | 15700 | 0.0063 |
| 1.4456 | 15800 | 0.0065 |
| 1.4547 | 15900 | 0.0101 |
| 1.4639 | 16000 | 0.0025 |
| 1.4730 | 16100 | 0.0098 |
| 1.4822 | 16200 | 0.0058 |
| 1.4913 | 16300 | 0.0098 |
| 1.5005 | 16400 | 0.0053 |
| 1.5096 | 16500 | 0.0052 |
| 1.5188 | 16600 | 0.0136 |
| 1.5279 | 16700 | 0.0095 |
| 1.5371 | 16800 | 0.0111 |
| 1.5462 | 16900 | 0.0088 |
| 1.5554 | 17000 | 0.0086 |
| 1.5645 | 17100 | 0.0098 |
| 1.5737 | 17200 | 0.0111 |
| 1.5828 | 17300 | 0.0059 |
| 1.5919 | 17400 | 0.02 |
| 1.6011 | 17500 | 0.0102 |
| 1.6102 | 17600 | 0.004 |
| 1.6194 | 17700 | 0.0029 |
| 1.6285 | 17800 | 0.0116 |
| 1.6377 | 17900 | 0.0031 |
| 1.6468 | 18000 | 0.0064 |
| 1.6560 | 18100 | 0.0094 |
| 1.6651 | 18200 | 0.0121 |
| 1.6743 | 18300 | 0.0087 |
| 1.6834 | 18400 | 0.0075 |
| 1.6926 | 18500 | 0.0052 |
| 1.7017 | 18600 | 0.0105 |
| 1.7109 | 18700 | 0.0111 |
| 1.7200 | 18800 | 0.0074 |
| 1.7292 | 18900 | 0.0038 |
| 1.7383 | 19000 | 0.0073 |
| 1.7475 | 19100 | 0.0042 |
| 1.7566 | 19200 | 0.0047 |
| 1.7658 | 19300 | 0.0177 |
| 1.7749 | 19400 | 0.005 |
| 1.7841 | 19500 | 0.0062 |
| 1.7932 | 19600 | 0.0081 |
| 1.8024 | 19700 | 0.007 |
| 1.8115 | 19800 | 0.0123 |
| 1.8207 | 19900 | 0.0076 |
| 1.8298 | 20000 | 0.006 |
| 1.8390 | 20100 | 0.0077 |
| 1.8481 | 20200 | 0.0071 |
| 1.8573 | 20300 | 0.0054 |
| 1.8664 | 20400 | 0.0065 |
| 1.8756 | 20500 | 0.0104 |
| 1.8847 | 20600 | 0.0099 |
| 1.8939 | 20700 | 0.0094 |
| 1.9030 | 20800 | 0.0068 |
| 1.9122 | 20900 | 0.012 |
| 1.9213 | 21000 | 0.0098 |
| 1.9305 | 21100 | 0.0164 |
| 1.9396 | 21200 | 0.0052 |
| 1.9488 | 21300 | 0.0131 |
| 1.9579 | 21400 | 0.0065 |
| 1.9671 | 21500 | 0.0079 |
| 1.9762 | 21600 | 0.0042 |
| 1.9854 | 21700 | 0.0245 |
| 1.9945 | 21800 | 0.007 |
| 2.0037 | 21900 | 0.0061 |
| 2.0128 | 22000 | 0.0087 |
| 2.0220 | 22100 | 0.0095 |
| 2.0311 | 22200 | 0.0114 |
| 2.0403 | 22300 | 0.0178 |
| 2.0494 | 22400 | 0.0116 |
| 2.0586 | 22500 | 0.0055 |
| 2.0677 | 22600 | 0.0142 |
| 2.0769 | 22700 | 0.0055 |
| 2.0860 | 22800 | 0.0027 |
| 2.0952 | 22900 | 0.0036 |
| 2.1043 | 23000 | 0.0072 |
| 2.1134 | 23100 | 0.0088 |
| 2.1226 | 23200 | 0.0125 |
| 2.1317 | 23300 | 0.0076 |
| 2.1409 | 23400 | 0.0037 |
| 2.1500 | 23500 | 0.0034 |
| 2.1592 | 23600 | 0.0082 |
| 2.1683 | 23700 | 0.0074 |
| 2.1775 | 23800 | 0.0118 |
| 2.1866 | 23900 | 0.0066 |
| 2.1958 | 24000 | 0.0081 |
| 2.2049 | 24100 | 0.0031 |
| 2.2141 | 24200 | 0.0084 |
| 2.2232 | 24300 | 0.013 |
| 2.2324 | 24400 | 0.0081 |
| 2.2415 | 24500 | 0.0034 |
| 2.2507 | 24600 | 0.0018 |
| 2.2598 | 24700 | 0.0177 |
| 2.2690 | 24800 | 0.0075 |
| 2.2781 | 24900 | 0.0051 |
| 2.2873 | 25000 | 0.007 |
| 2.2964 | 25100 | 0.0077 |
| 2.3056 | 25200 | 0.0038 |
| 2.3147 | 25300 | 0.0092 |
| 2.3239 | 25400 | 0.0082 |
| 2.3330 | 25500 | 0.0039 |
| 2.3422 | 25600 | 0.0092 |
| 2.3513 | 25700 | 0.0022 |
| 2.3605 | 25800 | 0.003 |
| 2.3696 | 25900 | 0.0038 |
| 2.3788 | 26000 | 0.0017 |
| 2.3879 | 26100 | 0.0045 |
| 2.3971 | 26200 | 0.0069 |
| 2.4062 | 26300 | 0.003 |
| 2.4154 | 26400 | 0.0054 |
| 2.4245 | 26500 | 0.0111 |
| 2.4337 | 26600 | 0.002 |
| 2.4428 | 26700 | 0.0023 |
| 2.4520 | 26800 | 0.0039 |
| 2.4611 | 26900 | 0.003 |
| 2.4703 | 27000 | 0.0045 |
| 2.4794 | 27100 | 0.0007 |
| 2.4886 | 27200 | 0.0053 |
| 2.4977 | 27300 | 0.0038 |
| 2.5069 | 27400 | 0.0023 |
| 2.5160 | 27500 | 0.0059 |
| 2.5252 | 27600 | 0.0028 |
| 2.5343 | 27700 | 0.007 |
| 2.5435 | 27800 | 0.0052 |
| 2.5526 | 27900 | 0.006 |
| 2.5618 | 28000 | 0.0042 |
| 2.5709 | 28100 | 0.0064 |
| 2.5801 | 28200 | 0.0025 |
| 2.5892 | 28300 | 0.0119 |
| 2.5984 | 28400 | 0.0057 |
| 2.6075 | 28500 | 0.0053 |
| 2.6167 | 28600 | 0.0031 |
| 2.6258 | 28700 | 0.005 |
| 2.6349 | 28800 | 0.0055 |
| 2.6441 | 28900 | 0.0018 |
| 2.6532 | 29000 | 0.0031 |
| 2.6624 | 29100 | 0.0085 |
| 2.6715 | 29200 | 0.003 |
| 2.6807 | 29300 | 0.0043 |
| 2.6898 | 29400 | 0.0031 |
| 2.6990 | 29500 | 0.002 |
| 2.7081 | 29600 | 0.0045 |
| 2.7173 | 29700 | 0.0086 |
| 2.7264 | 29800 | 0.0031 |
| 2.7356 | 29900 | 0.0034 |
| 2.7447 | 30000 | 0.0032 |
| 2.7539 | 30100 | 0.0013 |
| 2.7630 | 30200 | 0.0042 |
| 2.7722 | 30300 | 0.0043 |
| 2.7813 | 30400 | 0.0025 |
| 2.7905 | 30500 | 0.0039 |
| 2.7996 | 30600 | 0.0038 |
| 2.8088 | 30700 | 0.0044 |
| 2.8179 | 30800 | 0.0058 |
| 2.8271 | 30900 | 0.0016 |
| 2.8362 | 31000 | 0.0037 |
| 2.8454 | 31100 | 0.0034 |
| 2.8545 | 31200 | 0.0044 |
| 2.8637 | 31300 | 0.0057 |
| 2.8728 | 31400 | 0.0061 |
| 2.8820 | 31500 | 0.0082 |
| 2.8911 | 31600 | 0.0037 |
| 2.9003 | 31700 | 0.0049 |
| 2.9094 | 31800 | 0.0058 |
| 2.9186 | 31900 | 0.0046 |
| 2.9277 | 32000 | 0.0042 |
| 2.9369 | 32100 | 0.0087 |
| 2.9460 | 32200 | 0.0029 |
| 2.9552 | 32300 | 0.0068 |
| 2.9643 | 32400 | 0.006 |
| 2.9735 | 32500 | 0.0037 |
| 2.9826 | 32600 | 0.0096 |
| 2.9918 | 32700 | 0.0079 |
| 3.0009 | 32800 | 0.002 |
| 3.0101 | 32900 | 0.0049 |
| 3.0192 | 33000 | 0.0046 |
| 3.0284 | 33100 | 0.0031 |
| 3.0375 | 33200 | 0.0091 |
| 3.0467 | 33300 | 0.0103 |
| 3.0558 | 33400 | 0.003 |
| 3.0650 | 33500 | 0.0036 |
| 3.0741 | 33600 | 0.004 |
| 3.0833 | 33700 | 0.0024 |
| 3.0924 | 33800 | 0.0014 |
| 3.1016 | 33900 | 0.0048 |
| 3.1107 | 34000 | 0.0044 |
| 3.1199 | 34100 | 0.0045 |
| 3.1290 | 34200 | 0.0081 |
| 3.1382 | 34300 | 0.0014 |
| 3.1473 | 34400 | 0.0014 |
| 3.1565 | 34500 | 0.0051 |
| 3.1656 | 34600 | 0.0029 |
| 3.1747 | 34700 | 0.0099 |
| 3.1839 | 34800 | 0.0007 |
| 3.1930 | 34900 | 0.0074 |
| 3.2022 | 35000 | 0.0006 |
| 3.2113 | 35100 | 0.0033 |
| 3.2205 | 35200 | 0.0054 |
| 3.2296 | 35300 | 0.0053 |
| 3.2388 | 35400 | 0.0033 |
| 3.2479 | 35500 | 0.0009 |
| 3.2571 | 35600 | 0.0056 |
| 3.2662 | 35700 | 0.0076 |
| 3.2754 | 35800 | 0.0018 |
| 3.2845 | 35900 | 0.0059 |
| 3.2937 | 36000 | 0.002 |
| 3.3028 | 36100 | 0.0025 |
| 3.3120 | 36200 | 0.0044 |
| 3.3211 | 36300 | 0.0034 |
| 3.3303 | 36400 | 0.0028 |
| 3.3394 | 36500 | 0.0031 |
| 3.3486 | 36600 | 0.0026 |
| 3.3577 | 36700 | 0.0011 |
| 3.3669 | 36800 | 0.0007 |
| 3.3760 | 36900 | 0.0016 |
| 3.3852 | 37000 | 0.0028 |
| 3.3943 | 37100 | 0.0013 |
| 3.4035 | 37200 | 0.0023 |
| 3.4126 | 37300 | 0.0027 |
| 3.4218 | 37400 | 0.0037 |
| 3.4309 | 37500 | 0.005 |
| 3.4401 | 37600 | 0.0027 |
| 3.4492 | 37700 | 0.0007 |
| 3.4584 | 37800 | 0.0041 |
| 3.4675 | 37900 | 0.0017 |
| 3.4767 | 38000 | 0.0011 |
| 3.4858 | 38100 | 0.0021 |
| 3.4950 | 38200 | 0.0031 |
| 3.5041 | 38300 | 0.0011 |
| 3.5133 | 38400 | 0.0035 |
| 3.5224 | 38500 | 0.0005 |
| 3.5316 | 38600 | 0.0074 |
| 3.5407 | 38700 | 0.0017 |
| 3.5499 | 38800 | 0.0056 |
| 3.5590 | 38900 | 0.001 |
| 3.5682 | 39000 | 0.0055 |
| 3.5773 | 39100 | 0.0021 |
| 3.5865 | 39200 | 0.0037 |
| 3.5956 | 39300 | 0.0056 |
| 3.6048 | 39400 | 0.0044 |
| 3.6139 | 39500 | 0.0026 |
| 3.6231 | 39600 | 0.0026 |
| 3.6322 | 39700 | 0.0033 |
| 3.6414 | 39800 | 0.0008 |
| 3.6505 | 39900 | 0.0034 |
| 3.6597 | 40000 | 0.0029 |
| 3.6688 | 40100 | 0.0029 |
| 3.6780 | 40200 | 0.0022 |
| 3.6871 | 40300 | 0.0032 |
| 3.6962 | 40400 | 0.0006 |
| 3.7054 | 40500 | 0.0013 |
| 3.7145 | 40600 | 0.0084 |
| 3.7237 | 40700 | 0.0012 |
| 3.7328 | 40800 | 0.0015 |
| 3.7420 | 40900 | 0.0015 |
| 3.7511 | 41000 | 0.0014 |
| 3.7603 | 41100 | 0.0021 |
| 3.7694 | 41200 | 0.0015 |
| 3.7786 | 41300 | 0.0008 |
| 3.7877 | 41400 | 0.0018 |
| 3.7969 | 41500 | 0.0019 |
| 3.8060 | 41600 | 0.0044 |
| 3.8152 | 41700 | 0.004 |
| 3.8243 | 41800 | 0.0015 |
| 3.8335 | 41900 | 0.0023 |
| 3.8426 | 42000 | 0.0019 |
| 3.8518 | 42100 | 0.0031 |
| 3.8609 | 42200 | 0.0032 |
| 3.8701 | 42300 | 0.0012 |
| 3.8792 | 42400 | 0.0077 |
| 3.8884 | 42500 | 0.0052 |
| 3.8975 | 42600 | 0.0023 |
| 3.9067 | 42700 | 0.0023 |
| 3.9158 | 42800 | 0.0034 |
| 3.9250 | 42900 | 0.0035 |
| 3.9341 | 43000 | 0.0043 |
| 3.9433 | 43100 | 0.0018 |
| 3.9524 | 43200 | 0.003 |
| 3.9616 | 43300 | 0.0053 |
| 3.9707 | 43400 | 0.0018 |
| 3.9799 | 43500 | 0.0051 |
| 3.9890 | 43600 | 0.004 |
| 3.9982 | 43700 | 0.001 |
| 4.0073 | 43800 | 0.0025 |
| 4.0165 | 43900 | 0.0021 |
| 4.0256 | 44000 | 0.0028 |
| 4.0348 | 44100 | 0.0058 |
| 4.0439 | 44200 | 0.0071 |
| 4.0531 | 44300 | 0.003 |
| 4.0622 | 44400 | 0.0018 |
| 4.0714 | 44500 | 0.0032 |
| 4.0805 | 44600 | 0.001 |
| 4.0897 | 44700 | 0.0006 |
| 4.0988 | 44800 | 0.0017 |
| 4.1080 | 44900 | 0.0014 |
| 4.1171 | 45000 | 0.0047 |
| 4.1263 | 45100 | 0.0031 |
| 4.1354 | 45200 | 0.001 |
| 4.1446 | 45300 | 0.0012 |
| 4.1537 | 45400 | 0.0027 |
| 4.1629 | 45500 | 0.0015 |
| 4.1720 | 45600 | 0.0085 |
| 4.1812 | 45700 | 0.0006 |
| 4.1903 | 45800 | 0.0027 |
| 4.1995 | 45900 | 0.0035 |
| 4.2086 | 46000 | 0.0022 |
| 4.2177 | 46100 | 0.0029 |
| 4.2269 | 46200 | 0.0019 |
| 4.2360 | 46300 | 0.0045 |
| 4.2452 | 46400 | 0.0005 |
| 4.2543 | 46500 | 0.0039 |
| 4.2635 | 46600 | 0.0045 |
| 4.2726 | 46700 | 0.001 |
| 4.2818 | 46800 | 0.0028 |
| 4.2909 | 46900 | 0.0023 |
| 4.3001 | 47000 | 0.0014 |
| 4.3092 | 47100 | 0.0017 |
| 4.3184 | 47200 | 0.0024 |
| 4.3275 | 47300 | 0.0021 |
| 4.3367 | 47400 | 0.0017 |
| 4.3458 | 47500 | 0.0025 |
| 4.3550 | 47600 | 0.0015 |
| 4.3641 | 47700 | 0.0004 |
| 4.3733 | 47800 | 0.0011 |
| 4.3824 | 47900 | 0.0005 |
| 4.3916 | 48000 | 0.0028 |
| 4.4007 | 48100 | 0.0009 |
| 4.4099 | 48200 | 0.001 |
| 4.4190 | 48300 | 0.002 |
| 4.4282 | 48400 | 0.0053 |
| 4.4373 | 48500 | 0.0008 |
| 4.4465 | 48600 | 0.0006 |
| 4.4556 | 48700 | 0.0044 |
| 4.4648 | 48800 | 0.0005 |
| 4.4739 | 48900 | 0.0019 |
| 4.4831 | 49000 | 0.0016 |
| 4.4922 | 49100 | 0.0018 |
| 4.5014 | 49200 | 0.0008 |
| 4.5105 | 49300 | 0.0013 |
| 4.5197 | 49400 | 0.001 |
| 4.5288 | 49500 | 0.0046 |
| 4.5380 | 49600 | 0.0009 |
| 4.5471 | 49700 | 0.0051 |
| 4.5563 | 49800 | 0.0017 |
| 4.5654 | 49900 | 0.0021 |
| 4.5746 | 50000 | 0.0051 |
| 4.5837 | 50100 | 0.0014 |
| 4.5929 | 50200 | 0.0057 |
| 4.6020 | 50300 | 0.0036 |
| 4.6112 | 50400 | 0.0027 |
| 4.6203 | 50500 | 0.0009 |
| 4.6295 | 50600 | 0.0037 |
| 4.6386 | 50700 | 0.0004 |
| 4.6478 | 50800 | 0.0024 |
| 4.6569 | 50900 | 0.0015 |
| 4.6661 | 51000 | 0.0026 |
| 4.6752 | 51100 | 0.0022 |
| 4.6844 | 51200 | 0.0023 |
| 4.6935 | 51300 | 0.0007 |
| 4.7027 | 51400 | 0.0008 |
| 4.7118 | 51500 | 0.0032 |
| 4.7210 | 51600 | 0.0031 |
| 4.7301 | 51700 | 0.0014 |
| 4.7392 | 51800 | 0.0014 |
| 4.7484 | 51900 | 0.001 |
| 4.7575 | 52000 | 0.0011 |
| 4.7667 | 52100 | 0.0009 |
| 4.7758 | 52200 | 0.0007 |
| 4.7850 | 52300 | 0.0026 |
| 4.7941 | 52400 | 0.0008 |
| 4.8033 | 52500 | 0.0028 |
| 4.8124 | 52600 | 0.0019 |
| 4.8216 | 52700 | 0.0016 |
| 4.8307 | 52800 | 0.002 |
| 4.8399 | 52900 | 0.0008 |
| 4.8490 | 53000 | 0.0025 |
| 4.8582 | 53100 | 0.0008 |
| 4.8673 | 53200 | 0.0025 |
| 4.8765 | 53300 | 0.0039 |
| 4.8856 | 53400 | 0.0079 |
| 4.8948 | 53500 | 0.0016 |
| 4.9039 | 53600 | 0.0014 |
| 4.9131 | 53700 | 0.0018 |
| 4.9222 | 53800 | 0.002 |
| 4.9314 | 53900 | 0.0049 |
| 4.9405 | 54000 | 0.0012 |
| 4.9497 | 54100 | 0.0033 |
| 4.9588 | 54200 | 0.0027 |
| 4.9680 | 54300 | 0.004 |
| 4.9771 | 54400 | 0.0011 |
| 4.9863 | 54500 | 0.006 |
| 4.9954 | 54600 | 0.0017 |