---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1021596
- loss:MultipleNegativesRankingLoss
base_model: codersan/FaLabse
widget:
- source_sentence: Most women can't understand why this happens.
sentences:
- 'بیشتر زنان دلیل این کار را درک نمیکنند '
- ' سخت از خود در غضب بود که آن چه را به آسانی و صراحت میتوانست نزد خود تصمیم بگیرد،
قادر به بیان آن در حضور شاهزاده خانم تورسکی نیست. زیرا این زن در نظر او تجسم همان
نیروی بیدادگری بود که بر زندگی ظاهری او حکومت میکرد و مانع ابراز عشق و عفو و
نمایاندن احساساتش بود.'
- 'آقای تالبویز: چه روزهای خوشی، عجب روزهای خوشی!'
- source_sentence: to government offices, to the post office, and to the Governor's.
sentences:
- ناخوشی را تقویت میکند.
- به ادارات دولتی و اداره پست و سپس نزد استاندار رفت.
- اما به حال طبیعی نبود و در حالی که بازوی شوهرش را گرفته بود، گفتی که در عالم رؤیا
قدم بر میدارد.
- source_sentence: Even as she did so a sound checked her for an instant ' the unmistakable
bang of a window shutting, somewhere in Mrs Semprill's house.
sentences:
- در همین آن صدائی به گوشش رسید که بدون شک صدای بسته شدن پنجره خانه خانم سمپریل
بود!
- این کارم گذشتن از مرز بود.
- به همین دلیل هیچ کس بهتر از او برای تربیت مردی که حافظ تمامی خصوصیات نیک خانوادگی
باشد، وجود نداشت.
- source_sentence: 'It signifies God: done this day by my hand.'
sentences:
- معنی آن مهر این است که 3 خدا، امروز به دست من انجام شد.
- همه یکدیگر را بوسیدند
- این نشو نهی جادوگرهای تبه کاره
- source_sentence: If this were continued, the barricade was no longer tenable.
sentences:
- اگر این کار مداومت مییافت، سنگر قادر به مقاومت نمیبود.
- هر دو با هم به زمین میغلتیدند.
- خوب، در این لحظه او یک محافظ داشت.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on codersan/FaLabse
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [codersan/FaLabse](https://huggingface.co/codersan/FaLabse). 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:** [codersan/FaLabse](https://huggingface.co/codersan/FaLabse)
- **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/FaLabse_Mizan4")
# Run inference
sentences = [
'If this were continued, the barricade was no longer tenable.',
'اگر این کار مداومت می\u200cیافت، سنگر قادر به مقاومت نمی\u200cبود.',
'خوب، در این لحظه او یک محافظ داشت.',
]
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: 1,021,596 training samples
* Columns: anchor and positive
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 3 tokens
- mean: 16.37 tokens
- max: 85 tokens
| - min: 3 tokens
- mean: 18.63 tokens
- max: 81 tokens
|
* Samples:
| anchor | positive |
|:-------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------|
| They arose to obey. | دختران برای اطاعت امر پدر از جا برخاستند. |
| You'll know it all in time | همه چیز را بم وقع خواهی دانست. |
| She is in hysterics up there, and moans and says that we have been 'shamed and disgraced. | او هر لحظه گرفتار یک وضع است، زارزار گریه میکند. میگوید به ما توهین کردهاند، حیثیتمان را لکهدار نمودند. |
* 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`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `push_to_hub`: True
- `hub_model_id`: codersan/FaLabse_Mizan4
- `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`: 32
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 1
- `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`: True
- `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/FaLabse_Mizan4
- `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 | - |
| 0.0031 | 100 | 0.1023 |
| 0.0063 | 200 | 0.1162 |
| 0.0094 | 300 | 0.0976 |
| **0.0125** | **400** | **0.088** |
| 0.0157 | 500 | 0.0691 |
| 0.0188 | 600 | 0.0678 |
| 0.0219 | 700 | 0.082 |
| 0.0251 | 800 | 0.08 |
| 0.0282 | 900 | 0.0758 |
| 0.0313 | 1000 | 0.0763 |
| 0.0345 | 1100 | 0.0786 |
| 0.0376 | 1200 | 0.0666 |
| 0.0407 | 1300 | 0.0722 |
| 0.0439 | 1400 | 0.0638 |
| 0.0470 | 1500 | 0.0615 |
| 0.0501 | 1600 | 0.0623 |
| 0.0532 | 1700 | 0.0639 |
| 0.0564 | 1800 | 0.0692 |
| 0.0595 | 1900 | 0.0625 |
| 0.0626 | 2000 | 0.0774 |
| 0.0658 | 2100 | 0.06 |
| 0.0689 | 2200 | 0.0543 |
| 0.0720 | 2300 | 0.0611 |
| 0.0752 | 2400 | 0.0697 |
| 0.0783 | 2500 | 0.0703 |
| 0.0814 | 2600 | 0.058 |
| 0.0846 | 2700 | 0.075 |
| 0.0877 | 2800 | 0.062 |
| 0.0908 | 2900 | 0.0756 |
| 0.0940 | 3000 | 0.0668 |
| 0.0971 | 3100 | 0.054 |
| 0.1002 | 3200 | 0.0626 |
| 0.1034 | 3300 | 0.0645 |
| 0.1065 | 3400 | 0.0714 |
| 0.1096 | 3500 | 0.0644 |
| 0.1128 | 3600 | 0.0693 |
| 0.1159 | 3700 | 0.0734 |
| 0.1190 | 3800 | 0.0622 |
| 0.1222 | 3900 | 0.0741 |
| 0.1253 | 4000 | 0.0761 |
| 0.1284 | 4100 | 0.0582 |
| 0.1316 | 4200 | 0.0804 |
| 0.1347 | 4300 | 0.0708 |
| 0.1378 | 4400 | 0.0734 |
| 0.1410 | 4500 | 0.0709 |
| 0.1441 | 4600 | 0.0759 |
| 0.1472 | 4700 | 0.085 |
| 0.1504 | 4800 | 0.0573 |
| 0.1535 | 4900 | 0.056 |
| 0.1566 | 5000 | 0.0601 |
| 0.1597 | 5100 | 0.0596 |
| 0.1629 | 5200 | 0.079 |
| 0.1660 | 5300 | 0.0679 |
| 0.1691 | 5400 | 0.0553 |
| 0.1723 | 5500 | 0.0677 |
| 0.1754 | 5600 | 0.0795 |
| 0.1785 | 5700 | 0.0779 |
| 0.1817 | 5800 | 0.0599 |
| 0.1848 | 5900 | 0.0667 |
| 0.1879 | 6000 | 0.064 |
| 0.1911 | 6100 | 0.0637 |
| 0.1942 | 6200 | 0.0747 |
| 0.1973 | 6300 | 0.0829 |
| 0.2005 | 6400 | 0.0589 |
| 0.2036 | 6500 | 0.0623 |
| 0.2067 | 6600 | 0.0589 |
| 0.2099 | 6700 | 0.0648 |
| 0.2130 | 6800 | 0.0527 |
| 0.2161 | 6900 | 0.0519 |
| 0.2193 | 7000 | 0.0668 |
| 0.2224 | 7100 | 0.0729 |
| 0.2255 | 7200 | 0.0627 |
| 0.2287 | 7300 | 0.0539 |
| 0.2318 | 7400 | 0.055 |
| 0.2349 | 7500 | 0.0663 |
| 0.2381 | 7600 | 0.0589 |
| 0.2412 | 7700 | 0.0555 |
| 0.2443 | 7800 | 0.0875 |
| 0.2475 | 7900 | 0.055 |
| 0.2506 | 8000 | 0.0584 |
| 0.2537 | 8100 | 0.0607 |
| 0.2569 | 8200 | 0.0551 |
| 0.2600 | 8300 | 0.0527 |
| 0.2631 | 8400 | 0.0773 |
| 0.2662 | 8500 | 0.0696 |
| 0.2694 | 8600 | 0.062 |
| 0.2725 | 8700 | 0.0716 |
| 0.2756 | 8800 | 0.06 |
| 0.2788 | 8900 | 0.0536 |
| 0.2819 | 9000 | 0.0604 |
| 0.2850 | 9100 | 0.0563 |
| 0.2882 | 9200 | 0.0734 |
| 0.2913 | 9300 | 0.0714 |
| 0.2944 | 9400 | 0.0658 |
| 0.2976 | 9500 | 0.0623 |
| 0.3007 | 9600 | 0.0713 |
| 0.3038 | 9700 | 0.0674 |
| 0.3070 | 9800 | 0.0708 |
| 0.3101 | 9900 | 0.0579 |
| 0.3132 | 10000 | 0.0616 |
| 0.3164 | 10100 | 0.0653 |
| 0.3195 | 10200 | 0.0614 |
| 0.3226 | 10300 | 0.0626 |
| 0.3258 | 10400 | 0.0611 |
| 0.3289 | 10500 | 0.0521 |
| 0.3320 | 10600 | 0.056 |
| 0.3352 | 10700 | 0.0761 |
| 0.3383 | 10800 | 0.0629 |
| 0.3414 | 10900 | 0.0658 |
| 0.3446 | 11000 | 0.0576 |
| 0.3477 | 11100 | 0.0483 |
| 0.3508 | 11200 | 0.0654 |
| 0.3540 | 11300 | 0.0602 |
| 0.3571 | 11400 | 0.065 |
| 0.3602 | 11500 | 0.0787 |
| 0.3634 | 11600 | 0.0634 |
| 0.3665 | 11700 | 0.0678 |
| 0.3696 | 11800 | 0.0758 |
| 0.3727 | 11900 | 0.0637 |
| 0.3759 | 12000 | 0.0577 |
| 0.3790 | 12100 | 0.0572 |
| 0.3821 | 12200 | 0.0614 |
| 0.3853 | 12300 | 0.0685 |
| 0.3884 | 12400 | 0.0641 |
| 0.3915 | 12500 | 0.0583 |
| 0.3947 | 12600 | 0.0502 |
| 0.3978 | 12700 | 0.0481 |
| 0.4009 | 12800 | 0.0546 |
| 0.4041 | 12900 | 0.0664 |
| 0.4072 | 13000 | 0.0699 |
| 0.4103 | 13100 | 0.0513 |
| 0.4135 | 13200 | 0.0423 |
| 0.4166 | 13300 | 0.0554 |
| 0.4197 | 13400 | 0.0592 |
| 0.4229 | 13500 | 0.0457 |
| 0.4260 | 13600 | 0.0612 |
| 0.4291 | 13700 | 0.0507 |
| 0.4323 | 13800 | 0.0592 |
| 0.4354 | 13900 | 0.0566 |
| 0.4385 | 14000 | 0.0806 |
| 0.4417 | 14100 | 0.0648 |
| 0.4448 | 14200 | 0.0535 |
| 0.4479 | 14300 | 0.0748 |
| 0.4511 | 14400 | 0.0488 |
| 0.4542 | 14500 | 0.0539 |
| 0.4573 | 14600 | 0.0597 |
| 0.4605 | 14700 | 0.065 |
| 0.4636 | 14800 | 0.0594 |
| 0.4667 | 14900 | 0.05 |
| 0.4699 | 15000 | 0.0488 |
| 0.4730 | 15100 | 0.0537 |
| 0.4761 | 15200 | 0.0396 |
| 0.4792 | 15300 | 0.0616 |
| 0.4824 | 15400 | 0.0605 |
| 0.4855 | 15500 | 0.0599 |
| 0.4886 | 15600 | 0.0616 |
| 0.4918 | 15700 | 0.0731 |
| 0.4949 | 15800 | 0.0654 |
| 0.4980 | 15900 | 0.0463 |
| 0.5012 | 16000 | 0.0463 |
| 0.5043 | 16100 | 0.0594 |
| 0.5074 | 16200 | 0.0575 |
| 0.5106 | 16300 | 0.056 |
| 0.5137 | 16400 | 0.0542 |
| 0.5168 | 16500 | 0.052 |
| 0.5200 | 16600 | 0.0438 |
| 0.5231 | 16700 | 0.0675 |
| 0.5262 | 16800 | 0.0619 |
| 0.5294 | 16900 | 0.0515 |
| 0.5325 | 17000 | 0.0575 |
| 0.5356 | 17100 | 0.0568 |
| 0.5388 | 17200 | 0.0508 |
| 0.5419 | 17300 | 0.059 |
| 0.5450 | 17400 | 0.0505 |
| 0.5482 | 17500 | 0.0582 |
| 0.5513 | 17600 | 0.0574 |
| 0.5544 | 17700 | 0.0613 |
| 0.5576 | 17800 | 0.048 |
| 0.5607 | 17900 | 0.0553 |
| 0.5638 | 18000 | 0.0571 |
| 0.5670 | 18100 | 0.0543 |
| 0.5701 | 18200 | 0.0484 |
| 0.5732 | 18300 | 0.0763 |
| 0.5764 | 18400 | 0.056 |
| 0.5795 | 18500 | 0.0533 |
| 0.5826 | 18600 | 0.044 |
| 0.5857 | 18700 | 0.0515 |
| 0.5889 | 18800 | 0.0516 |
| 0.5920 | 18900 | 0.0586 |
| 0.5951 | 19000 | 0.0523 |
| 0.5983 | 19100 | 0.0733 |
| 0.6014 | 19200 | 0.0453 |
| 0.6045 | 19300 | 0.0663 |
| 0.6077 | 19400 | 0.0381 |
| 0.6108 | 19500 | 0.0568 |
| 0.6139 | 19600 | 0.0492 |
| 0.6171 | 19700 | 0.0489 |
| 0.6202 | 19800 | 0.0575 |
| 0.6233 | 19900 | 0.0642 |
| 0.6265 | 20000 | 0.0535 |
| 0.6296 | 20100 | 0.0598 |
| 0.6327 | 20200 | 0.0569 |
| 0.6359 | 20300 | 0.0513 |
| 0.6390 | 20400 | 0.0515 |
| 0.6421 | 20500 | 0.053 |
| 0.6453 | 20600 | 0.0569 |
| 0.6484 | 20700 | 0.0372 |
| 0.6515 | 20800 | 0.0464 |
| 0.6547 | 20900 | 0.0522 |
| 0.6578 | 21000 | 0.0427 |
| 0.6609 | 21100 | 0.0584 |
| 0.6641 | 21200 | 0.0616 |
| 0.6672 | 21300 | 0.0552 |
| 0.6703 | 21400 | 0.0509 |
| 0.6735 | 21500 | 0.0439 |
| 0.6766 | 21600 | 0.0762 |
| 0.6797 | 21700 | 0.0539 |
| 0.6829 | 21800 | 0.0475 |
| 0.6860 | 21900 | 0.0557 |
| 0.6891 | 22000 | 0.0421 |
| 0.6922 | 22100 | 0.0471 |
| 0.6954 | 22200 | 0.0398 |
| 0.6985 | 22300 | 0.0521 |
| 0.7016 | 22400 | 0.0472 |
| 0.7048 | 22500 | 0.0579 |
| 0.7079 | 22600 | 0.0539 |
| 0.7110 | 22700 | 0.0527 |
| 0.7142 | 22800 | 0.0677 |
| 0.7173 | 22900 | 0.0509 |
| 0.7204 | 23000 | 0.0478 |
| 0.7236 | 23100 | 0.0593 |
| 0.7267 | 23200 | 0.0419 |
| 0.7298 | 23300 | 0.0576 |
| 0.7330 | 23400 | 0.0485 |
| 0.7361 | 23500 | 0.0544 |
| 0.7392 | 23600 | 0.0537 |
| 0.7424 | 23700 | 0.0481 |
| 0.7455 | 23800 | 0.0597 |
| 0.7486 | 23900 | 0.0464 |
| 0.7518 | 24000 | 0.0537 |
| 0.7549 | 24100 | 0.0508 |
| 0.7580 | 24200 | 0.045 |
| 0.7612 | 24300 | 0.0337 |
| 0.7643 | 24400 | 0.0478 |
| 0.7674 | 24500 | 0.0495 |
| 0.7706 | 24600 | 0.0427 |
| 0.7737 | 24700 | 0.0596 |
| 0.7768 | 24800 | 0.0468 |
| 0.7800 | 24900 | 0.0404 |
| 0.7831 | 25000 | 0.0467 |
| 0.7862 | 25100 | 0.0514 |
| 0.7894 | 25200 | 0.0462 |
| 0.7925 | 25300 | 0.0401 |
| 0.7956 | 25400 | 0.0539 |
| 0.7987 | 25500 | 0.0541 |
| 0.8019 | 25600 | 0.0639 |
| 0.8050 | 25700 | 0.0392 |
| 0.8081 | 25800 | 0.0466 |
| 0.8113 | 25900 | 0.0543 |
| 0.8144 | 26000 | 0.0507 |
| 0.8175 | 26100 | 0.0465 |
| 0.8207 | 26200 | 0.0386 |
| 0.8238 | 26300 | 0.0606 |
| 0.8269 | 26400 | 0.0558 |
| 0.8301 | 26500 | 0.0488 |
| 0.8332 | 26600 | 0.0556 |
| 0.8363 | 26700 | 0.047 |
| 0.8395 | 26800 | 0.0548 |
| 0.8426 | 26900 | 0.0423 |
| 0.8457 | 27000 | 0.0529 |
| 0.8489 | 27100 | 0.0513 |
| 0.8520 | 27200 | 0.0432 |
| 0.8551 | 27300 | 0.0605 |
| 0.8583 | 27400 | 0.0448 |
| 0.8614 | 27500 | 0.0508 |
| 0.8645 | 27600 | 0.0578 |
| 0.8677 | 27700 | 0.0409 |
| 0.8708 | 27800 | 0.0487 |
| 0.8739 | 27900 | 0.058 |
| 0.8771 | 28000 | 0.0461 |
| 0.8802 | 28100 | 0.0389 |
| 0.8833 | 28200 | 0.0427 |
| 0.8865 | 28300 | 0.0473 |
| 0.8896 | 28400 | 0.061 |
| 0.8927 | 28500 | 0.0423 |
| 0.8958 | 28600 | 0.0435 |
| 0.8990 | 28700 | 0.0389 |
| 0.9021 | 28800 | 0.0466 |
| 0.9052 | 28900 | 0.042 |
| 0.9084 | 29000 | 0.0466 |
| 0.9115 | 29100 | 0.0412 |
| 0.9146 | 29200 | 0.0444 |
| 0.9178 | 29300 | 0.059 |
| 0.9209 | 29400 | 0.0466 |
| 0.9240 | 29500 | 0.0381 |
| 0.9272 | 29600 | 0.0408 |
| 0.9303 | 29700 | 0.0557 |
| 0.9334 | 29800 | 0.0567 |
| 0.9366 | 29900 | 0.0537 |
| 0.9397 | 30000 | 0.041 |
| 0.9428 | 30100 | 0.0383 |
| 0.9460 | 30200 | 0.0412 |
| 0.9491 | 30300 | 0.0489 |
| 0.9522 | 30400 | 0.046 |
| 0.9554 | 30500 | 0.0525 |
| 0.9585 | 30600 | 0.0493 |
| 0.9616 | 30700 | 0.0485 |
| 0.9648 | 30800 | 0.0532 |
| 0.9679 | 30900 | 0.0446 |
| 0.9710 | 31000 | 0.0372 |
| 0.9742 | 31100 | 0.0472 |
| 0.9773 | 31200 | 0.0399 |
| 0.9804 | 31300 | 0.0402 |
| 0.9836 | 31400 | 0.0372 |
| 0.9867 | 31500 | 0.0497 |
| 0.9898 | 31600 | 0.0432 |
| 0.9930 | 31700 | 0.0382 |
| 0.9961 | 31800 | 0.0475 |
| 0.9992 | 31900 | 0.0367 |
* The bold row denotes the saved checkpoint.