---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1021596
- loss:MultipleNegativesRankingLoss
base_model: codersan/FaLabse
widget:
- source_sentence: 'بیشتر زنان دلیل این کار را درک نمی‌کنند '
sentences:
- Most women can't understand why this happens.
- feeling with confusion and annoyance that what he could decide easily and clearly
by himself, he could not discuss before Princess Tverskaya, who to him stood for
the incarnation of that brute force which would inevitably control him in the
life he led in the eyes of the world, and hinder him from giving way to his feeling
of love and forgiveness.
- 'MR TALLBOYS: Happy days, happy days!'
- source_sentence: به ادارات دولتی و اداره پست و سپس نزد استاندار رفت.
sentences:
- It strengthens the disease
- to government offices, to the post office, and to the Governor's.
- but she was utterly beside herself, and moved hanging on her husband's arm as
though in a dream.
- source_sentence: در همین آن صدائی به گوشش رسید که بدون شک صدای بسته شدن پنجره خانه
خانم سمپریل بود!
sentences:
- 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.
- That was over the line.
- No one would be better able than she to shape the virtuous man who would restore
the prestige of the family
- source_sentence: معنی آن مهر این است که 3 خدا، امروز به دست من انجام شد.
sentences:
- 'It signifies God: done this day by my hand.'
- They all embraced one another
- that's the mark of a Dark wizard.
- source_sentence: اگر این کار مداومت می‌یافت، سنگر قادر به مقاومت نمی‌بود.
sentences:
- If this were continued, the barricade was no longer tenable.
- They rolled down on the ground.
- Well, for this moment she had a protector.
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_Mizan3")
# Run inference
sentences = [
'اگر این کار مداومت می\u200cیافت، سنگر قادر به مقاومت نمی\u200cبود.',
'If this were continued, the barricade was no longer tenable.',
'Well, for this moment she had a protector.',
]
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: 18.63 tokens
- max: 81 tokens
| - min: 3 tokens
- mean: 16.37 tokens
- max: 85 tokens
|
* Samples:
| anchor | positive |
|:--------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------|
| دختران برای اطاعت امر پدر از جا برخاستند. | They arose to obey. |
| همه چیز را بم وقع خواهی دانست. | You'll know it all in time |
| او هر لحظه گرفتار یک وضع است، زارزار گریه می‌کند. می‌گوید به ما توهین کرده‌اند، حیثیتمان را لکه‌دار نمودند. | She is in hysterics up there, and moans and says that we have been 'shamed and disgraced. |
* 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`: 16
- `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_Mizan3
- `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`: 16
- `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_Mizan3
- `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 | - |
| 0.0016 | 100 | 0.067 |
| 0.0031 | 200 | 0.0623 |
| 0.0047 | 300 | 0.0887 |
| 0.0063 | 400 | 0.0615 |
| 0.0078 | 500 | 0.0647 |
| 0.0094 | 600 | 0.0599 |
| 0.0110 | 700 | 0.0504 |
| **0.0125** | **800** | **0.0548** |
| 0.0141 | 900 | 0.0491 |
| 0.0157 | 1000 | 0.0418 |
| 0.0172 | 1100 | 0.0484 |
| 0.0188 | 1200 | 0.0499 |
| 0.0204 | 1300 | 0.0604 |
| 0.0219 | 1400 | 0.0492 |
| 0.0235 | 1500 | 0.0619 |
| 0.0251 | 1600 | 0.0505 |
| 0.0266 | 1700 | 0.0552 |
| 0.0282 | 1800 | 0.0384 |
| 0.0298 | 1900 | 0.0456 |
| 0.0313 | 2000 | 0.0459 |
| 0.0329 | 2100 | 0.0385 |
| 0.0345 | 2200 | 0.0493 |
| 0.0360 | 2300 | 0.0483 |
| 0.0376 | 2400 | 0.0398 |
| 0.0392 | 2500 | 0.0392 |
| 0.0407 | 2600 | 0.0652 |
| 0.0423 | 2700 | 0.0323 |
| 0.0439 | 2800 | 0.0521 |
| 0.0454 | 2900 | 0.0424 |
| 0.0470 | 3000 | 0.0334 |
| 0.0486 | 3100 | 0.0457 |
| 0.0501 | 3200 | 0.0349 |
| 0.0517 | 3300 | 0.032 |
| 0.0532 | 3400 | 0.0334 |
| 0.0548 | 3500 | 0.0442 |
| 0.0564 | 3600 | 0.0414 |
| 0.0579 | 3700 | 0.0555 |
| 0.0595 | 3800 | 0.0378 |
| 0.0611 | 3900 | 0.0583 |
| 0.0626 | 4000 | 0.0452 |
| 0.0642 | 4100 | 0.0406 |
| 0.0658 | 4200 | 0.0461 |
| 0.0673 | 4300 | 0.0433 |
| 0.0689 | 4400 | 0.0371 |
| 0.0705 | 4500 | 0.0392 |
| 0.0720 | 4600 | 0.0434 |
| 0.0736 | 4700 | 0.046 |
| 0.0752 | 4800 | 0.0477 |
| 0.0767 | 4900 | 0.06 |
| 0.0783 | 5000 | 0.0415 |
| 0.0799 | 5100 | 0.0434 |
| 0.0814 | 5200 | 0.0495 |
| 0.0830 | 5300 | 0.043 |
| 0.0846 | 5400 | 0.0515 |
| 0.0861 | 5500 | 0.0457 |
| 0.0877 | 5600 | 0.0444 |
| 0.0893 | 5700 | 0.0454 |
| 0.0908 | 5800 | 0.0575 |
| 0.0924 | 5900 | 0.0394 |
| 0.0940 | 6000 | 0.0554 |
| 0.0955 | 6100 | 0.0362 |
| 0.0971 | 6200 | 0.0381 |
| 0.0987 | 6300 | 0.0446 |
| 0.1002 | 6400 | 0.0426 |
| 0.1018 | 6500 | 0.0606 |
| 0.1034 | 6600 | 0.052 |
| 0.1049 | 6700 | 0.0414 |
| 0.1065 | 6800 | 0.059 |
| 0.1081 | 6900 | 0.0485 |
| 0.1096 | 7000 | 0.0488 |
| 0.1112 | 7100 | 0.0312 |
| 0.1128 | 7200 | 0.0662 |
| 0.1143 | 7300 | 0.0495 |
| 0.1159 | 7400 | 0.0529 |
| 0.1175 | 7500 | 0.0598 |
| 0.1190 | 7600 | 0.0465 |
| 0.1206 | 7700 | 0.0577 |
| 0.1222 | 7800 | 0.0571 |
| 0.1237 | 7900 | 0.055 |
| 0.1253 | 8000 | 0.0679 |
| 0.1269 | 8100 | 0.0525 |
| 0.1284 | 8200 | 0.0427 |
| 0.1300 | 8300 | 0.0534 |
| 0.1316 | 8400 | 0.0593 |
| 0.1331 | 8500 | 0.0483 |
| 0.1347 | 8600 | 0.0437 |
| 0.1363 | 8700 | 0.0505 |
| 0.1378 | 8800 | 0.0462 |
| 0.1394 | 8900 | 0.0411 |
| 0.1410 | 9000 | 0.0665 |
| 0.1425 | 9100 | 0.0338 |
| 0.1441 | 9200 | 0.0512 |
| 0.1457 | 9300 | 0.0683 |
| 0.1472 | 9400 | 0.077 |
| 0.1488 | 9500 | 0.0469 |
| 0.1504 | 9600 | 0.0408 |
| 0.1519 | 9700 | 0.0374 |
| 0.1535 | 9800 | 0.0422 |
| 0.1551 | 9900 | 0.0429 |
| 0.1566 | 10000 | 0.0466 |
| 0.1582 | 10100 | 0.0504 |
| 0.1597 | 10200 | 0.0401 |
| 0.1613 | 10300 | 0.0384 |
| 0.1629 | 10400 | 0.0554 |
| 0.1644 | 10500 | 0.0515 |
| 0.1660 | 10600 | 0.0529 |
| 0.1676 | 10700 | 0.0413 |
| 0.1691 | 10800 | 0.0386 |
| 0.1707 | 10900 | 0.046 |
| 0.1723 | 11000 | 0.059 |
| 0.1738 | 11100 | 0.0716 |
| 0.1754 | 11200 | 0.0496 |
| 0.1770 | 11300 | 0.0541 |
| 0.1785 | 11400 | 0.0499 |
| 0.1801 | 11500 | 0.0486 |
| 0.1817 | 11600 | 0.0446 |
| 0.1832 | 11700 | 0.0508 |
| 0.1848 | 11800 | 0.0451 |
| 0.1864 | 11900 | 0.0379 |
| 0.1879 | 12000 | 0.0515 |
| 0.1895 | 12100 | 0.0422 |
| 0.1911 | 12200 | 0.0335 |
| 0.1926 | 12300 | 0.0408 |
| 0.1942 | 12400 | 0.0584 |
| 0.1958 | 12500 | 0.0524 |
| 0.1973 | 12600 | 0.0562 |
| 0.1989 | 12700 | 0.0399 |
| 0.2005 | 12800 | 0.0465 |
| 0.2020 | 12900 | 0.0334 |
| 0.2036 | 13000 | 0.0471 |
| 0.2052 | 13100 | 0.0462 |
| 0.2067 | 13200 | 0.0419 |
| 0.2083 | 13300 | 0.0398 |
| 0.2099 | 13400 | 0.0581 |
| 0.2114 | 13500 | 0.037 |
| 0.2130 | 13600 | 0.0328 |
| 0.2146 | 13700 | 0.0466 |
| 0.2161 | 13800 | 0.0343 |
| 0.2177 | 13900 | 0.0491 |
| 0.2193 | 14000 | 0.0485 |
| 0.2208 | 14100 | 0.0417 |
| 0.2224 | 14200 | 0.0603 |
| 0.2240 | 14300 | 0.0392 |
| 0.2255 | 14400 | 0.0605 |
| 0.2271 | 14500 | 0.0369 |
| 0.2287 | 14600 | 0.0368 |
| 0.2302 | 14700 | 0.0418 |
| 0.2318 | 14800 | 0.0471 |
| 0.2334 | 14900 | 0.0521 |
| 0.2349 | 15000 | 0.0455 |
| 0.2365 | 15100 | 0.0371 |
| 0.2381 | 15200 | 0.044 |
| 0.2396 | 15300 | 0.0388 |
| 0.2412 | 15400 | 0.0445 |
| 0.2428 | 15500 | 0.0608 |
| 0.2443 | 15600 | 0.0552 |
| 0.2459 | 15700 | 0.0416 |
| 0.2475 | 15800 | 0.0395 |
| 0.2490 | 15900 | 0.0285 |
| 0.2506 | 16000 | 0.0513 |
| 0.2522 | 16100 | 0.0524 |
| 0.2537 | 16200 | 0.0379 |
| 0.2553 | 16300 | 0.0321 |
| 0.2569 | 16400 | 0.0405 |
| 0.2584 | 16500 | 0.0448 |
| 0.2600 | 16600 | 0.0362 |
| 0.2616 | 16700 | 0.0516 |
| 0.2631 | 16800 | 0.068 |
| 0.2647 | 16900 | 0.057 |
| 0.2662 | 17000 | 0.046 |
| 0.2678 | 17100 | 0.0349 |
| 0.2694 | 17200 | 0.0502 |
| 0.2709 | 17300 | 0.0508 |
| 0.2725 | 17400 | 0.0575 |
| 0.2741 | 17500 | 0.0369 |
| 0.2756 | 17600 | 0.0385 |
| 0.2772 | 17700 | 0.0441 |
| 0.2788 | 17800 | 0.0455 |
| 0.2803 | 17900 | 0.0404 |
| 0.2819 | 18000 | 0.057 |
| 0.2835 | 18100 | 0.0328 |
| 0.2850 | 18200 | 0.0577 |
| 0.2866 | 18300 | 0.0355 |
| 0.2882 | 18400 | 0.0629 |
| 0.2897 | 18500 | 0.0703 |
| 0.2913 | 18600 | 0.0464 |
| 0.2929 | 18700 | 0.0624 |
| 0.2944 | 18800 | 0.027 |
| 0.2960 | 18900 | 0.0541 |
| 0.2976 | 19000 | 0.0361 |
| 0.2991 | 19100 | 0.0494 |
| 0.3007 | 19200 | 0.0652 |
| 0.3023 | 19300 | 0.0574 |
| 0.3038 | 19400 | 0.0451 |
| 0.3054 | 19500 | 0.0443 |
| 0.3070 | 19600 | 0.0571 |
| 0.3085 | 19700 | 0.0553 |
| 0.3101 | 19800 | 0.042 |
| 0.3117 | 19900 | 0.0583 |
| 0.3132 | 20000 | 0.0394 |
| 0.3148 | 20100 | 0.0378 |
| 0.3164 | 20200 | 0.042 |
| 0.3179 | 20300 | 0.0391 |
| 0.3195 | 20400 | 0.0452 |
| 0.3211 | 20500 | 0.0421 |
| 0.3226 | 20600 | 0.0348 |
| 0.3242 | 20700 | 0.0332 |
| 0.3258 | 20800 | 0.0612 |
| 0.3273 | 20900 | 0.0492 |
| 0.3289 | 21000 | 0.0319 |
| 0.3305 | 21100 | 0.0432 |
| 0.3320 | 21200 | 0.0339 |
| 0.3336 | 21300 | 0.0441 |
| 0.3352 | 21400 | 0.0652 |
| 0.3367 | 21500 | 0.044 |
| 0.3383 | 21600 | 0.0459 |
| 0.3399 | 21700 | 0.0571 |
| 0.3414 | 21800 | 0.0357 |
| 0.3430 | 21900 | 0.0378 |
| 0.3446 | 22000 | 0.0364 |
| 0.3461 | 22100 | 0.0381 |
| 0.3477 | 22200 | 0.0214 |
| 0.3493 | 22300 | 0.0424 |
| 0.3508 | 22400 | 0.054 |
| 0.3524 | 22500 | 0.0272 |
| 0.3540 | 22600 | 0.0478 |
| 0.3555 | 22700 | 0.0438 |
| 0.3571 | 22800 | 0.0519 |
| 0.3587 | 22900 | 0.0666 |
| 0.3602 | 23000 | 0.06 |
| 0.3618 | 23100 | 0.0462 |
| 0.3634 | 23200 | 0.0447 |
| 0.3649 | 23300 | 0.0426 |
| 0.3665 | 23400 | 0.0518 |
| 0.3681 | 23500 | 0.0443 |
| 0.3696 | 23600 | 0.0585 |
| 0.3712 | 23700 | 0.0356 |
| 0.3727 | 23800 | 0.0511 |
| 0.3743 | 23900 | 0.044 |
| 0.3759 | 24000 | 0.0342 |
| 0.3774 | 24100 | 0.0272 |
| 0.3790 | 24200 | 0.0602 |
| 0.3806 | 24300 | 0.0438 |
| 0.3821 | 24400 | 0.027 |
| 0.3837 | 24500 | 0.0485 |
| 0.3853 | 24600 | 0.0404 |
| 0.3868 | 24700 | 0.0562 |
| 0.3884 | 24800 | 0.0334 |
| 0.3900 | 24900 | 0.0354 |
| 0.3915 | 25000 | 0.0558 |
| 0.3931 | 25100 | 0.0366 |
| 0.3947 | 25200 | 0.0387 |
| 0.3962 | 25300 | 0.0302 |
| 0.3978 | 25400 | 0.0554 |
| 0.3994 | 25500 | 0.0317 |
| 0.4009 | 25600 | 0.0418 |
| 0.4025 | 25700 | 0.0534 |
| 0.4041 | 25800 | 0.0331 |
| 0.4056 | 25900 | 0.0489 |
| 0.4072 | 26000 | 0.0428 |
| 0.4088 | 26100 | 0.0273 |
| 0.4103 | 26200 | 0.0346 |
| 0.4119 | 26300 | 0.041 |
| 0.4135 | 26400 | 0.027 |
| 0.4150 | 26500 | 0.025 |
| 0.4166 | 26600 | 0.0522 |
| 0.4182 | 26700 | 0.0319 |
| 0.4197 | 26800 | 0.0461 |
| 0.4213 | 26900 | 0.031 |
| 0.4229 | 27000 | 0.0318 |
| 0.4244 | 27100 | 0.0412 |
| 0.4260 | 27200 | 0.0388 |
| 0.4276 | 27300 | 0.0336 |
| 0.4291 | 27400 | 0.0345 |
| 0.4307 | 27500 | 0.0403 |
| 0.4323 | 27600 | 0.0567 |
| 0.4338 | 27700 | 0.0378 |
| 0.4354 | 27800 | 0.0438 |
| 0.4370 | 27900 | 0.0419 |
| 0.4385 | 28000 | 0.0533 |
| 0.4401 | 28100 | 0.0569 |
| 0.4417 | 28200 | 0.0458 |
| 0.4432 | 28300 | 0.0293 |
| 0.4448 | 28400 | 0.0501 |
| 0.4464 | 28500 | 0.0616 |
| 0.4479 | 28600 | 0.0526 |
| 0.4495 | 28700 | 0.0332 |
| 0.4511 | 28800 | 0.0367 |
| 0.4526 | 28900 | 0.0334 |
| 0.4542 | 29000 | 0.0332 |
| 0.4558 | 29100 | 0.0296 |
| 0.4573 | 29200 | 0.0504 |
| 0.4589 | 29300 | 0.0444 |
| 0.4605 | 29400 | 0.0404 |
| 0.4620 | 29500 | 0.0396 |
| 0.4636 | 29600 | 0.041 |
| 0.4652 | 29700 | 0.0289 |
| 0.4667 | 29800 | 0.0354 |
| 0.4683 | 29900 | 0.0377 |
| 0.4699 | 30000 | 0.0381 |
| 0.4714 | 30100 | 0.0456 |
| 0.4730 | 30200 | 0.0474 |
| 0.4745 | 30300 | 0.0299 |
| 0.4761 | 30400 | 0.0433 |
| 0.4777 | 30500 | 0.0509 |
| 0.4792 | 30600 | 0.0397 |
| 0.4808 | 30700 | 0.0449 |
| 0.4824 | 30800 | 0.0338 |
| 0.4839 | 30900 | 0.0597 |
| 0.4855 | 31000 | 0.0369 |
| 0.4871 | 31100 | 0.0404 |
| 0.4886 | 31200 | 0.0483 |
| 0.4902 | 31300 | 0.0501 |
| 0.4918 | 31400 | 0.0502 |
| 0.4933 | 31500 | 0.046 |
| 0.4949 | 31600 | 0.0461 |
| 0.4965 | 31700 | 0.0365 |
| 0.4980 | 31800 | 0.0394 |
| 0.4996 | 31900 | 0.0348 |
| 0.5012 | 32000 | 0.0309 |
| 0.5027 | 32100 | 0.0312 |
| 0.5043 | 32200 | 0.0318 |
| 0.5059 | 32300 | 0.0349 |
| 0.5074 | 32400 | 0.0351 |
| 0.5090 | 32500 | 0.0392 |
| 0.5106 | 32600 | 0.0478 |
| 0.5121 | 32700 | 0.0374 |
| 0.5137 | 32800 | 0.0396 |
| 0.5153 | 32900 | 0.0346 |
| 0.5168 | 33000 | 0.0357 |
| 0.5184 | 33100 | 0.0428 |
| 0.5200 | 33200 | 0.0348 |
| 0.5215 | 33300 | 0.048 |
| 0.5231 | 33400 | 0.0491 |
| 0.5247 | 33500 | 0.0681 |
| 0.5262 | 33600 | 0.0348 |
| 0.5278 | 33700 | 0.034 |
| 0.5294 | 33800 | 0.0388 |
| 0.5309 | 33900 | 0.0426 |
| 0.5325 | 34000 | 0.0355 |
| 0.5341 | 34100 | 0.0322 |
| 0.5356 | 34200 | 0.047 |
| 0.5372 | 34300 | 0.0416 |
| 0.5388 | 34400 | 0.0267 |
| 0.5403 | 34500 | 0.0415 |
| 0.5419 | 34600 | 0.0377 |
| 0.5435 | 34700 | 0.0426 |
| 0.5450 | 34800 | 0.0399 |
| 0.5466 | 34900 | 0.0456 |
| 0.5482 | 35000 | 0.0458 |
| 0.5497 | 35100 | 0.0337 |
| 0.5513 | 35200 | 0.0448 |
| 0.5529 | 35300 | 0.0437 |
| 0.5544 | 35400 | 0.0468 |
| 0.5560 | 35500 | 0.0347 |
| 0.5576 | 35600 | 0.0406 |
| 0.5591 | 35700 | 0.037 |
| 0.5607 | 35800 | 0.0304 |
| 0.5623 | 35900 | 0.0318 |
| 0.5638 | 36000 | 0.0405 |
| 0.5654 | 36100 | 0.0426 |
| 0.5670 | 36200 | 0.0338 |
| 0.5685 | 36300 | 0.0254 |
| 0.5701 | 36400 | 0.0399 |
| 0.5717 | 36500 | 0.0493 |
| 0.5732 | 36600 | 0.0435 |
| 0.5748 | 36700 | 0.0357 |
| 0.5764 | 36800 | 0.042 |
| 0.5779 | 36900 | 0.0336 |
| 0.5795 | 37000 | 0.0492 |
| 0.5810 | 37100 | 0.0263 |
| 0.5826 | 37200 | 0.0277 |
| 0.5842 | 37300 | 0.0414 |
| 0.5857 | 37400 | 0.0418 |
| 0.5873 | 37500 | 0.0494 |
| 0.5889 | 37600 | 0.0355 |
| 0.5904 | 37700 | 0.0306 |
| 0.5920 | 37800 | 0.045 |
| 0.5936 | 37900 | 0.0304 |
| 0.5951 | 38000 | 0.0305 |
| 0.5967 | 38100 | 0.0485 |
| 0.5983 | 38200 | 0.0513 |
| 0.5998 | 38300 | 0.0362 |
| 0.6014 | 38400 | 0.0255 |
| 0.6030 | 38500 | 0.038 |
| 0.6045 | 38600 | 0.0586 |
| 0.6061 | 38700 | 0.0221 |
| 0.6077 | 38800 | 0.0199 |
| 0.6092 | 38900 | 0.0466 |
| 0.6108 | 39000 | 0.0251 |
| 0.6124 | 39100 | 0.0288 |
| 0.6139 | 39200 | 0.0341 |
| 0.6155 | 39300 | 0.0335 |
| 0.6171 | 39400 | 0.037 |
| 0.6186 | 39500 | 0.0295 |
| 0.6202 | 39600 | 0.0334 |
| 0.6218 | 39700 | 0.0518 |
| 0.6233 | 39800 | 0.0367 |
| 0.6249 | 39900 | 0.044 |
| 0.6265 | 40000 | 0.0431 |
| 0.6280 | 40100 | 0.0496 |
| 0.6296 | 40200 | 0.0425 |
| 0.6312 | 40300 | 0.0449 |
| 0.6327 | 40400 | 0.0337 |
| 0.6343 | 40500 | 0.0273 |
| 0.6359 | 40600 | 0.0359 |
| 0.6374 | 40700 | 0.0283 |
| 0.6390 | 40800 | 0.0317 |
| 0.6406 | 40900 | 0.0297 |
| 0.6421 | 41000 | 0.0302 |
| 0.6437 | 41100 | 0.0309 |
| 0.6453 | 41200 | 0.0327 |
| 0.6468 | 41300 | 0.013 |
| 0.6484 | 41400 | 0.041 |
| 0.6500 | 41500 | 0.0254 |
| 0.6515 | 41600 | 0.0208 |
| 0.6531 | 41700 | 0.0338 |
| 0.6547 | 41800 | 0.0371 |
| 0.6562 | 41900 | 0.0253 |
| 0.6578 | 42000 | 0.0314 |
| 0.6594 | 42100 | 0.0452 |
| 0.6609 | 42200 | 0.0371 |
| 0.6625 | 42300 | 0.0367 |
| 0.6641 | 42400 | 0.052 |
| 0.6656 | 42500 | 0.0267 |
| 0.6672 | 42600 | 0.0353 |
| 0.6688 | 42700 | 0.0385 |
| 0.6703 | 42800 | 0.0361 |
| 0.6719 | 42900 | 0.0342 |
| 0.6735 | 43000 | 0.0311 |
| 0.6750 | 43100 | 0.0587 |
| 0.6766 | 43200 | 0.0393 |
| 0.6782 | 43300 | 0.0325 |
| 0.6797 | 43400 | 0.0356 |
| 0.6813 | 43500 | 0.0428 |
| 0.6829 | 43600 | 0.026 |
| 0.6844 | 43700 | 0.0292 |
| 0.6860 | 43800 | 0.0499 |
| 0.6875 | 43900 | 0.0192 |
| 0.6891 | 44000 | 0.0378 |
| 0.6907 | 44100 | 0.0323 |
| 0.6922 | 44200 | 0.0303 |
| 0.6938 | 44300 | 0.0153 |
| 0.6954 | 44400 | 0.023 |
| 0.6969 | 44500 | 0.0261 |
| 0.6985 | 44600 | 0.0311 |
| 0.7001 | 44700 | 0.029 |
| 0.7016 | 44800 | 0.0247 |
| 0.7032 | 44900 | 0.0353 |
| 0.7048 | 45000 | 0.041 |
| 0.7063 | 45100 | 0.0238 |
| 0.7079 | 45200 | 0.0415 |
| 0.7095 | 45300 | 0.0394 |
| 0.7110 | 45400 | 0.0342 |
| 0.7126 | 45500 | 0.0468 |
| 0.7142 | 45600 | 0.0409 |
| 0.7157 | 45700 | 0.0345 |
| 0.7173 | 45800 | 0.0405 |
| 0.7189 | 45900 | 0.0454 |
| 0.7204 | 46000 | 0.0277 |
| 0.7220 | 46100 | 0.05 |
| 0.7236 | 46200 | 0.0329 |
| 0.7251 | 46300 | 0.0281 |
| 0.7267 | 46400 | 0.0232 |
| 0.7283 | 46500 | 0.0325 |
| 0.7298 | 46600 | 0.0332 |
| 0.7314 | 46700 | 0.0504 |
| 0.7330 | 46800 | 0.0338 |
| 0.7345 | 46900 | 0.0355 |
| 0.7361 | 47000 | 0.0441 |
| 0.7377 | 47100 | 0.0386 |
| 0.7392 | 47200 | 0.0467 |
| 0.7408 | 47300 | 0.0371 |
| 0.7424 | 47400 | 0.0407 |
| 0.7439 | 47500 | 0.0313 |
| 0.7455 | 47600 | 0.0397 |
| 0.7471 | 47700 | 0.0279 |
| 0.7486 | 47800 | 0.04 |
| 0.7502 | 47900 | 0.0318 |
| 0.7518 | 48000 | 0.0337 |
| 0.7533 | 48100 | 0.0483 |
| 0.7549 | 48200 | 0.0356 |
| 0.7565 | 48300 | 0.0368 |
| 0.7580 | 48400 | 0.0322 |
| 0.7596 | 48500 | 0.0228 |
| 0.7612 | 48600 | 0.0275 |
| 0.7627 | 48700 | 0.0361 |
| 0.7643 | 48800 | 0.0257 |
| 0.7659 | 48900 | 0.0277 |
| 0.7674 | 49000 | 0.0448 |
| 0.7690 | 49100 | 0.0369 |
| 0.7706 | 49200 | 0.0224 |
| 0.7721 | 49300 | 0.049 |
| 0.7737 | 49400 | 0.036 |
| 0.7753 | 49500 | 0.0373 |
| 0.7768 | 49600 | 0.0225 |
| 0.7784 | 49700 | 0.0355 |
| 0.7800 | 49800 | 0.0296 |
| 0.7815 | 49900 | 0.0228 |
| 0.7831 | 50000 | 0.0506 |
| 0.7847 | 50100 | 0.0411 |
| 0.7862 | 50200 | 0.0289 |
| 0.7878 | 50300 | 0.0331 |
| 0.7894 | 50400 | 0.0278 |
| 0.7909 | 50500 | 0.0246 |
| 0.7925 | 50600 | 0.0329 |
| 0.7940 | 50700 | 0.0295 |
| 0.7956 | 50800 | 0.0551 |
| 0.7972 | 50900 | 0.0291 |
| 0.7987 | 51000 | 0.0358 |
| 0.8003 | 51100 | 0.0387 |
| 0.8019 | 51200 | 0.0303 |
| 0.8034 | 51300 | 0.0319 |
| 0.8050 | 51400 | 0.0282 |
| 0.8066 | 51500 | 0.0247 |
| 0.8081 | 51600 | 0.0298 |
| 0.8097 | 51700 | 0.0336 |
| 0.8113 | 51800 | 0.0408 |
| 0.8128 | 51900 | 0.04 |
| 0.8144 | 52000 | 0.0425 |
| 0.8160 | 52100 | 0.0326 |
| 0.8175 | 52200 | 0.0308 |
| 0.8191 | 52300 | 0.0307 |
| 0.8207 | 52400 | 0.0226 |
| 0.8222 | 52500 | 0.0325 |
| 0.8238 | 52600 | 0.0449 |
| 0.8254 | 52700 | 0.0437 |
| 0.8269 | 52800 | 0.031 |
| 0.8285 | 52900 | 0.0228 |
| 0.8301 | 53000 | 0.0411 |
| 0.8316 | 53100 | 0.0226 |
| 0.8332 | 53200 | 0.029 |
| 0.8348 | 53300 | 0.033 |
| 0.8363 | 53400 | 0.0325 |
| 0.8379 | 53500 | 0.0382 |
| 0.8395 | 53600 | 0.035 |
| 0.8410 | 53700 | 0.027 |
| 0.8426 | 53800 | 0.0221 |
| 0.8442 | 53900 | 0.0394 |
| 0.8457 | 54000 | 0.03 |
| 0.8473 | 54100 | 0.0406 |
| 0.8489 | 54200 | 0.0257 |
| 0.8504 | 54300 | 0.0322 |
| 0.8520 | 54400 | 0.0312 |
| 0.8536 | 54500 | 0.0338 |
| 0.8551 | 54600 | 0.0522 |
| 0.8567 | 54700 | 0.023 |
| 0.8583 | 54800 | 0.0353 |
| 0.8598 | 54900 | 0.0381 |
| 0.8614 | 55000 | 0.0318 |
| 0.8630 | 55100 | 0.0402 |
| 0.8645 | 55200 | 0.0314 |
| 0.8661 | 55300 | 0.0358 |
| 0.8677 | 55400 | 0.0201 |
| 0.8692 | 55500 | 0.0348 |
| 0.8708 | 55600 | 0.0256 |
| 0.8724 | 55700 | 0.0289 |
| 0.8739 | 55800 | 0.0541 |
| 0.8755 | 55900 | 0.0337 |
| 0.8771 | 56000 | 0.0239 |
| 0.8786 | 56100 | 0.0255 |
| 0.8802 | 56200 | 0.0204 |
| 0.8818 | 56300 | 0.0321 |
| 0.8833 | 56400 | 0.0241 |
| 0.8849 | 56500 | 0.022 |
| 0.8865 | 56600 | 0.0325 |
| 0.8880 | 56700 | 0.03 |
| 0.8896 | 56800 | 0.0365 |
| 0.8912 | 56900 | 0.0341 |
| 0.8927 | 57000 | 0.028 |
| 0.8943 | 57100 | 0.0271 |
| 0.8958 | 57200 | 0.0256 |
| 0.8974 | 57300 | 0.0305 |
| 0.8990 | 57400 | 0.0292 |
| 0.9005 | 57500 | 0.0496 |
| 0.9021 | 57600 | 0.0236 |
| 0.9037 | 57700 | 0.0245 |
| 0.9052 | 57800 | 0.0388 |
| 0.9068 | 57900 | 0.0296 |
| 0.9084 | 58000 | 0.046 |
| 0.9099 | 58100 | 0.0301 |
| 0.9115 | 58200 | 0.0441 |
| 0.9131 | 58300 | 0.0244 |
| 0.9146 | 58400 | 0.0355 |
| 0.9162 | 58500 | 0.0343 |
| 0.9178 | 58600 | 0.0377 |
| 0.9193 | 58700 | 0.0256 |
| 0.9209 | 58800 | 0.0378 |
| 0.9225 | 58900 | 0.03 |
| 0.9240 | 59000 | 0.0296 |
| 0.9256 | 59100 | 0.0214 |
| 0.9272 | 59200 | 0.0257 |
| 0.9287 | 59300 | 0.0366 |
| 0.9303 | 59400 | 0.0435 |
| 0.9319 | 59500 | 0.0399 |
| 0.9334 | 59600 | 0.0302 |
| 0.9350 | 59700 | 0.033 |
| 0.9366 | 59800 | 0.0391 |
| 0.9381 | 59900 | 0.0228 |
| 0.9397 | 60000 | 0.0354 |
| 0.9413 | 60100 | 0.0276 |
| 0.9428 | 60200 | 0.0332 |
| 0.9444 | 60300 | 0.0219 |
| 0.9460 | 60400 | 0.0249 |
| 0.9475 | 60500 | 0.0327 |
| 0.9491 | 60600 | 0.0296 |
| 0.9507 | 60700 | 0.0357 |
| 0.9522 | 60800 | 0.0268 |
| 0.9538 | 60900 | 0.0371 |
| 0.9554 | 61000 | 0.0376 |
| 0.9569 | 61100 | 0.0308 |
| 0.9585 | 61200 | 0.0342 |
| 0.9601 | 61300 | 0.0222 |
| 0.9616 | 61400 | 0.0279 |
| 0.9632 | 61500 | 0.0351 |
| 0.9648 | 61600 | 0.0405 |
| 0.9663 | 61700 | 0.0315 |
| 0.9679 | 61800 | 0.0354 |
| 0.9695 | 61900 | 0.0203 |
| 0.9710 | 62000 | 0.0295 |
| 0.9726 | 62100 | 0.0338 |
| 0.9742 | 62200 | 0.0341 |
| 0.9757 | 62300 | 0.0315 |
| 0.9773 | 62400 | 0.0221 |
| 0.9789 | 62500 | 0.0288 |
| 0.9804 | 62600 | 0.0249 |
| 0.9820 | 62700 | 0.0332 |
| 0.9836 | 62800 | 0.0289 |
| 0.9851 | 62900 | 0.0416 |
| 0.9867 | 63000 | 0.0363 |
| 0.9883 | 63100 | 0.0297 |
| 0.9898 | 63200 | 0.0253 |
| 0.9914 | 63300 | 0.0268 |
| 0.9930 | 63400 | 0.0318 |
| 0.9945 | 63500 | 0.0409 |
| 0.9961 | 63600 | 0.0248 |
| 0.9977 | 63700 | 0.0262 |
| 0.9992 | 63800 | 0.0294 |
* The bold row denotes the saved checkpoint.