---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8692806
- loss:MSELoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: This is an opportunity.
sentences:
- 今では30秒ほどの短くて繰り返されるテレビCMがものを言います
- これは機会なのです これだけでなく ここにあるアイデアを繋げ
- エイズと鳥インフルエンザについての もっともな心配事については 本日後ほど お話をいただきましょう ”ブリリアント”な ブリリアント博士から 私はもうひとつの流行病について
話したいと思います 心血管疾患、糖尿病、高血圧です これらの疾患は 全て完ぺきに防ぐことができます 少なくとも95%の人々に関しては ただ食生活と生活習慣を
改善するだけでいいのです
- source_sentence: I add new images, because I learn more about it every time I give
it.
sentences:
- 公立学校の生徒は 86%が黒人です
- いつも新たに学んだことがあり 新しい内容を付け加えています
- なので たとえ状況が楽観的とは いえない状況であっても 地域のリーダー達と 国際社会の指導者は 状況をよくするための 選択をすることができます
- source_sentence: From over 12,000 civilians deliberately killed in civil wars in
1997 and 1998, a decade later, this figure stands at 4,000.
sentences:
- 見てください これは選挙結果ではありませんよ これは各州の肥満の人の割合を 色で示しています 1985年に始まり、'86年、’87年 CDCのホームページから引用しました
'88年、'89年、'90年、'91年 ここから新しい色が出てきます '92年、'93年、'94年、'95年、'96年 97年、'98年、'99年、2000年そして2001年
事態はさらに悪化します なんだか私たちは 退化してるみたいですね (笑) 私たちには何が できるのでしょうか？ 私たちが発見した心臓病や癌を治す力 を秘めている食事というのは
- 1万2千人もの民間人が 内戦によって殺害されていたのは 1997年と1998年ですが 10年後 その数は４千に減少しました
- それには３つの理由があると思います まずは 経済を発展させるため 技術職に数学は必要です そして 毎日の生活です 今の世界で役に立つには 数年前よりもずっと
定量的でなくてはいけません 住宅ローンを計算したり 政府の統計に懐疑的になったりするからです ３つめは私が論理的な心の訓練と呼んでいるもので 論理的な考え方をすることです
- source_sentence: And so this played out for us in a couple different ways.
sentences:
- 見つけ出そうとしているのは何なのか ということですが
- 中心部のゴーストタウン化
- ２つの違う方法で取り組みました
- source_sentence: We'll apply that throughout our global supply chain regardless
of ownership or control.
sentences:
- 前にも述べた自動車ですが ハイブリッドカーを買うか 電車を使ってください
- 浸透していません
- 私たちはこれを グローバル・サプライチェーンにおいて 所有権や支配権に関係なく適用します。
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- negative_mse
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: en ja
type: en-ja
metrics:
- type: negative_mse
value: -0.17230987548828125
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: en ja
type: en-ja
metrics:
- type: src2trg_accuracy
value: 0.688293760353396
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.6616510215350635
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.6749723909442298
name: Mean Accuracy
---
# 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) on the en-ja dataset. 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)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- en-ja
### 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': 128, '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 = [
"We'll apply that throughout our global supply chain regardless of ownership or control.",
'私たちはこれを グローバル・サプライチェーンにおいて 所有権や支配権に関係なく適用します。',
'浸透していません',
]
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]
```
## Evaluation
### Metrics
#### Knowledge Distillation
* Dataset: `en-ja`
* Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:------------|
| **negative_mse** | **-0.1723** |
#### Translation
* Dataset: `en-ja`
* Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:----------|
| src2trg_accuracy | 0.6883 |
| trg2src_accuracy | 0.6617 |
| **mean_accuracy** | **0.675** |
## Training Details
### Training Dataset
#### en-ja
* Dataset: en-ja
* Size: 8,692,806 training samples
* Columns: english, non_english, and label
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | - min: 8 tokens
- mean: 72.37 tokens
- max: 128 tokens
| - min: 4 tokens
- mean: 37.76 tokens
- max: 128 tokens
| |
* Samples:
| english | non_english | label |
|:-------------------------------------------------------------------------------------------------------|:-----------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------|
| Are the basics of driving a car learning how to service it, or design it for that matter? | 車の運転の基礎は 点検の仕方？それともデザインの仕方？ | [0.04097634181380272, 0.015725482255220413, 0.04093917831778526, 0.005089071579277515, -0.008469141088426113, ...] |
| Are the basics of writing learning how to sharpen a quill? | 執筆の基礎は羽ペンの削り方？ | [-0.0036177693400532007, 0.010684962384402752, -0.014135013334453106, -0.05535861477255821, -0.08116177469491959, ...] |
| I don't think so. | 違うと思います | [-0.07840073108673096, -0.0229326281696558, -0.012929541990160942, -0.007635382004082203, -0.04817994683980942, ...] |
* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Dataset
#### en-ja
* Dataset: en-ja
* Size: 7,244 evaluation samples
* Columns: english, non_english, and label
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | - min: 5 tokens
- mean: 77.14 tokens
- max: 128 tokens
| - min: 4 tokens
- mean: 42.63 tokens
- max: 128 tokens
| |
* Samples:
| english | non_english | label |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|
| Thank you so much, Chris. | どうもありがとう クリス このステージに立てる機会を | [0.02692059800028801, 0.05314800143241882, 0.14048902690410614, -0.10380179435014725, -0.04118778929114342, ...] |
| And it's truly a great honor to have the opportunity to come to this stage twice; I'm extremely grateful. | 2度もいただけるというのは実に光栄なことで とてもうれしく思っています | [0.024387270212173462, 0.09500126540660858, 0.12180333584547043, -0.07149269431829453, -0.018444567918777466, ...] |
| I have been blown away by this conference, and I want to thank all of you for the many nice comments about what I had to say the other night. | このカンファレンスには圧倒されっぱなしです 皆さんから― 前回の講演に対していただいた温かいコメントにお礼を申し上げたい | [0.015005433931946754, 0.014678305946290493, 0.13112004101276398, 0.03133269399404526, 0.06942533701658249, ...] |
* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `gradient_accumulation_steps`: 4
- `learning_rate`: 0.0003
- `num_train_epochs`: 10
- `warmup_ratio`: 0.15
- `bf16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0003
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.15
- `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`: True
- `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
- `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`: 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`: proportional
Click to expand
| Epoch | Step | Training Loss | en-ja loss | en-ja_negative_mse | en-ja_mean_accuracy |
|:------:|:-----:|:-------------:|:----------:|:------------------:|:-------------------:|
| 0.0118 | 100 | 0.0049 | - | - | - |
| 0.0236 | 200 | 0.004 | - | - | - |
| 0.0353 | 300 | 0.0038 | - | - | - |
| 0.0471 | 400 | 0.0037 | - | - | - |
| 0.0589 | 500 | 0.0037 | - | - | - |
| 0.0707 | 600 | 0.0037 | - | - | - |
| 0.0825 | 700 | 0.0036 | - | - | - |
| 0.0942 | 800 | 0.0036 | - | - | - |
| 0.1060 | 900 | 0.0035 | - | - | - |
| 0.1178 | 1000 | 0.0035 | 0.0034 | -0.34472314 | 0.0121 |
| 0.1296 | 1100 | 0.0035 | - | - | - |
| 0.1414 | 1200 | 0.0034 | - | - | - |
| 0.1531 | 1300 | 0.0034 | - | - | - |
| 0.1649 | 1400 | 0.0034 | - | - | - |
| 0.1767 | 1500 | 0.0033 | - | - | - |
| 0.1885 | 1600 | 0.0033 | - | - | - |
| 0.2003 | 1700 | 0.0032 | - | - | - |
| 0.2120 | 1800 | 0.0032 | - | - | - |
| 0.2238 | 1900 | 0.0032 | - | - | - |
| 0.2356 | 2000 | 0.0031 | 0.0031 | -0.3021978 | 0.0279 |
| 0.2474 | 2100 | 0.0031 | - | - | - |
| 0.2592 | 2200 | 0.0031 | - | - | - |
| 0.2709 | 2300 | 0.0031 | - | - | - |
| 0.2827 | 2400 | 0.003 | - | - | - |
| 0.2945 | 2500 | 0.003 | - | - | - |
| 0.3063 | 2600 | 0.003 | - | - | - |
| 0.3180 | 2700 | 0.0029 | - | - | - |
| 0.3298 | 2800 | 0.0029 | - | - | - |
| 0.3416 | 2900 | 0.0029 | - | - | - |
| 0.3534 | 3000 | 0.0028 | 0.0027 | -0.27366027 | 0.0888 |
| 0.3652 | 3100 | 0.0028 | - | - | - |
| 0.3769 | 3200 | 0.0028 | - | - | - |
| 0.3887 | 3300 | 0.0027 | - | - | - |
| 0.4005 | 3400 | 0.0027 | - | - | - |
| 0.4123 | 3500 | 0.0027 | - | - | - |
| 0.4241 | 3600 | 0.0026 | - | - | - |
| 0.4358 | 3700 | 0.0026 | - | - | - |
| 0.4476 | 3800 | 0.0026 | - | - | - |
| 0.4594 | 3900 | 0.0025 | - | - | - |
| 0.4712 | 4000 | 0.0025 | 0.0024 | -0.25388333 | 0.2314 |
| 0.4830 | 4100 | 0.0025 | - | - | - |
| 0.4947 | 4200 | 0.0024 | - | - | - |
| 0.5065 | 4300 | 0.0024 | - | - | - |
| 0.5183 | 4400 | 0.0024 | - | - | - |
| 0.5301 | 4500 | 0.0023 | - | - | - |
| 0.5419 | 4600 | 0.0023 | - | - | - |
| 0.5536 | 4700 | 0.0023 | - | - | - |
| 0.5654 | 4800 | 0.0023 | - | - | - |
| 0.5772 | 4900 | 0.0022 | - | - | - |
| 0.5890 | 5000 | 0.0022 | 0.0021 | -0.23925437 | 0.3782 |
| 0.6008 | 5100 | 0.0022 | - | - | - |
| 0.6125 | 5200 | 0.0022 | - | - | - |
| 0.6243 | 5300 | 0.0021 | - | - | - |
| 0.6361 | 5400 | 0.0021 | - | - | - |
| 0.6479 | 5500 | 0.0021 | - | - | - |
| 0.6597 | 5600 | 0.0021 | - | - | - |
| 0.6714 | 5700 | 0.0021 | - | - | - |
| 0.6832 | 5800 | 0.002 | - | - | - |
| 0.6950 | 5900 | 0.002 | - | - | - |
| 0.7068 | 6000 | 0.002 | 0.0020 | -0.22806446 | 0.4660 |
| 0.7186 | 6100 | 0.002 | - | - | - |
| 0.7303 | 6200 | 0.002 | - | - | - |
| 0.7421 | 6300 | 0.002 | - | - | - |
| 0.7539 | 6400 | 0.0019 | - | - | - |
| 0.7657 | 6500 | 0.0019 | - | - | - |
| 0.7775 | 6600 | 0.0019 | - | - | - |
| 0.7892 | 6700 | 0.0019 | - | - | - |
| 0.8010 | 6800 | 0.0019 | - | - | - |
| 0.8128 | 6900 | 0.0019 | - | - | - |
| 0.8246 | 7000 | 0.0018 | 0.0018 | -0.2197086 | 0.5271 |
| 0.8364 | 7100 | 0.0018 | - | - | - |
| 0.8481 | 7200 | 0.0018 | - | - | - |
| 0.8599 | 7300 | 0.0018 | - | - | - |
| 0.8717 | 7400 | 0.0018 | - | - | - |
| 0.8835 | 7500 | 0.0018 | - | - | - |
| 0.8952 | 7600 | 0.0018 | - | - | - |
| 0.9070 | 7700 | 0.0018 | - | - | - |
| 0.9188 | 7800 | 0.0018 | - | - | - |
| 0.9306 | 7900 | 0.0018 | - | - | - |
| 0.9424 | 8000 | 0.0017 | 0.0017 | -0.21387897 | 0.5574 |
| 0.9541 | 8100 | 0.0017 | - | - | - |
| 0.9659 | 8200 | 0.0017 | - | - | - |
| 0.9777 | 8300 | 0.0017 | - | - | - |
| 0.9895 | 8400 | 0.0017 | - | - | - |
| 1.0012 | 8500 | 0.0017 | - | - | - |
| 1.0130 | 8600 | 0.0017 | - | - | - |
| 1.0247 | 8700 | 0.0017 | - | - | - |
| 1.0365 | 8800 | 0.0017 | - | - | - |
| 1.0483 | 8900 | 0.0017 | - | - | - |
| 1.0601 | 9000 | 0.0017 | 0.0016 | -0.2085446 | 0.5752 |
| 1.0719 | 9100 | 0.0016 | - | - | - |
| 1.0836 | 9200 | 0.0016 | - | - | - |
| 1.0954 | 9300 | 0.0016 | - | - | - |
| 1.1072 | 9400 | 0.0016 | - | - | - |
| 1.1190 | 9500 | 0.0016 | - | - | - |
| 1.1308 | 9600 | 0.0016 | - | - | - |
| 1.1425 | 9700 | 0.0016 | - | - | - |
| 1.1543 | 9800 | 0.0016 | - | - | - |
| 1.1661 | 9900 | 0.0016 | - | - | - |
| 1.1779 | 10000 | 0.0016 | 0.0016 | -0.20458691 | 0.5946 |
| 1.1897 | 10100 | 0.0016 | - | - | - |
| 1.2014 | 10200 | 0.0016 | - | - | - |
| 1.2132 | 10300 | 0.0016 | - | - | - |
| 1.2250 | 10400 | 0.0016 | - | - | - |
| 1.2368 | 10500 | 0.0016 | - | - | - |
| 1.2485 | 10600 | 0.0016 | - | - | - |
| 1.2603 | 10700 | 0.0015 | - | - | - |
| 1.2721 | 10800 | 0.0015 | - | - | - |
| 1.2839 | 10900 | 0.0015 | - | - | - |
| 1.2957 | 11000 | 0.0015 | 0.0015 | -0.20193738 | 0.6010 |
| 1.3074 | 11100 | 0.0015 | - | - | - |
| 1.3192 | 11200 | 0.0015 | - | - | - |
| 1.3310 | 11300 | 0.0015 | - | - | - |
| 1.3428 | 11400 | 0.0015 | - | - | - |
| 1.3546 | 11500 | 0.0015 | - | - | - |
| 1.3663 | 11600 | 0.0015 | - | - | - |
| 1.3781 | 11700 | 0.0015 | - | - | - |
| 1.3899 | 11800 | 0.0015 | - | - | - |
| 1.4017 | 11900 | 0.0015 | - | - | - |
| 1.4135 | 12000 | 0.0015 | 0.0015 | -0.19939835 | 0.6132 |
| 1.4252 | 12100 | 0.0015 | - | - | - |
| 1.4370 | 12200 | 0.0015 | - | - | - |
| 1.4488 | 12300 | 0.0015 | - | - | - |
| 1.4606 | 12400 | 0.0015 | - | - | - |
| 1.4724 | 12500 | 0.0015 | - | - | - |
| 1.4841 | 12600 | 0.0015 | - | - | - |
| 1.4959 | 12700 | 0.0015 | - | - | - |
| 1.5077 | 12800 | 0.0015 | - | - | - |
| 1.5195 | 12900 | 0.0015 | - | - | - |
| 1.5313 | 13000 | 0.0015 | 0.0014 | -0.19621891 | 0.6252 |
| 1.5430 | 13100 | 0.0014 | - | - | - |
| 1.5548 | 13200 | 0.0014 | - | - | - |
| 1.5666 | 13300 | 0.0014 | - | - | - |
| 1.5784 | 13400 | 0.0014 | - | - | - |
| 1.5902 | 13500 | 0.0014 | - | - | - |
| 1.6019 | 13600 | 0.0014 | - | - | - |
| 1.6137 | 13700 | 0.0014 | - | - | - |
| 1.6255 | 13800 | 0.0014 | - | - | - |
| 1.6373 | 13900 | 0.0014 | - | - | - |
| 1.6491 | 14000 | 0.0014 | 0.0014 | -0.19430138 | 0.6285 |
| 1.6608 | 14100 | 0.0014 | - | - | - |
| 1.6726 | 14200 | 0.0014 | - | - | - |
| 1.6844 | 14300 | 0.0014 | - | - | - |
| 1.6962 | 14400 | 0.0014 | - | - | - |
| 1.7080 | 14500 | 0.0014 | - | - | - |
| 1.7197 | 14600 | 0.0014 | - | - | - |
| 1.7315 | 14700 | 0.0014 | - | - | - |
| 1.7433 | 14800 | 0.0014 | - | - | - |
| 1.7551 | 14900 | 0.0014 | - | - | - |
| 1.7669 | 15000 | 0.0014 | 0.0014 | -0.19249634 | 0.6341 |
| 1.7786 | 15100 | 0.0014 | - | - | - |
| 1.7904 | 15200 | 0.0014 | - | - | - |
| 1.8022 | 15300 | 0.0014 | - | - | - |
| 1.8140 | 15400 | 0.0014 | - | - | - |
| 1.8258 | 15500 | 0.0014 | - | - | - |
| 1.8375 | 15600 | 0.0014 | - | - | - |
| 1.8493 | 15700 | 0.0014 | - | - | - |
| 1.8611 | 15800 | 0.0014 | - | - | - |
| 1.8729 | 15900 | 0.0014 | - | - | - |
| 1.8846 | 16000 | 0.0014 | 0.0014 | -0.19071046 | 0.6386 |
| 1.8964 | 16100 | 0.0014 | - | - | - |
| 1.9082 | 16200 | 0.0014 | - | - | - |
| 1.9200 | 16300 | 0.0014 | - | - | - |
| 1.9318 | 16400 | 0.0014 | - | - | - |
| 1.9435 | 16500 | 0.0014 | - | - | - |
| 1.9553 | 16600 | 0.0014 | - | - | - |
| 1.9671 | 16700 | 0.0014 | - | - | - |
| 1.9789 | 16800 | 0.0014 | - | - | - |
| 1.9907 | 16900 | 0.0014 | - | - | - |
| 2.0024 | 17000 | 0.0013 | 0.0014 | -0.1893078 | 0.6416 |
| 2.0141 | 17100 | 0.0013 | - | - | - |
| 2.0259 | 17200 | 0.0013 | - | - | - |
| 2.0377 | 17300 | 0.0013 | - | - | - |
| 2.0495 | 17400 | 0.0013 | - | - | - |
| 2.0613 | 17500 | 0.0013 | - | - | - |
| 2.0730 | 17600 | 0.0013 | - | - | - |
| 2.0848 | 17700 | 0.0013 | - | - | - |
| 2.0966 | 17800 | 0.0013 | - | - | - |
| 2.1084 | 17900 | 0.0013 | - | - | - |
| 2.1202 | 18000 | 0.0013 | 0.0013 | -0.188142 | 0.6453 |
| 2.1319 | 18100 | 0.0013 | - | - | - |
| 2.1437 | 18200 | 0.0013 | - | - | - |
| 2.1555 | 18300 | 0.0013 | - | - | - |
| 2.1673 | 18400 | 0.0013 | - | - | - |
| 2.1790 | 18500 | 0.0013 | - | - | - |
| 2.1908 | 18600 | 0.0013 | - | - | - |
| 2.2026 | 18700 | 0.0013 | - | - | - |
| 2.2144 | 18800 | 0.0013 | - | - | - |
| 2.2262 | 18900 | 0.0013 | - | - | - |
| 2.2379 | 19000 | 0.0013 | 0.0013 | -0.18757328 | 0.6469 |
| 2.2497 | 19100 | 0.0013 | - | - | - |
| 2.2615 | 19200 | 0.0013 | - | - | - |
| 2.2733 | 19300 | 0.0013 | - | - | - |
| 2.2851 | 19400 | 0.0013 | - | - | - |
| 2.2968 | 19500 | 0.0013 | - | - | - |
| 2.3086 | 19600 | 0.0013 | - | - | - |
| 2.3204 | 19700 | 0.0013 | - | - | - |
| 2.3322 | 19800 | 0.0013 | - | - | - |
| 2.3440 | 19900 | 0.0013 | - | - | - |
| 2.3557 | 20000 | 0.0013 | 0.0013 | -0.18630134 | 0.6434 |
| 2.3675 | 20100 | 0.0013 | - | - | - |
| 2.3793 | 20200 | 0.0013 | - | - | - |
| 2.3911 | 20300 | 0.0013 | - | - | - |
| 2.4029 | 20400 | 0.0013 | - | - | - |
| 2.4146 | 20500 | 0.0013 | - | - | - |
| 2.4264 | 20600 | 0.0013 | - | - | - |
| 2.4382 | 20700 | 0.0013 | - | - | - |
| 2.4500 | 20800 | 0.0013 | - | - | - |
| 2.4618 | 20900 | 0.0013 | - | - | - |
| 2.4735 | 21000 | 0.0013 | 0.0013 | -0.18556643 | 0.6488 |
| 2.4853 | 21100 | 0.0013 | - | - | - |
| 2.4971 | 21200 | 0.0013 | - | - | - |
| 2.5089 | 21300 | 0.0013 | - | - | - |
| 2.5207 | 21400 | 0.0013 | - | - | - |
| 2.5324 | 21500 | 0.0013 | - | - | - |
| 2.5442 | 21600 | 0.0013 | - | - | - |
| 2.5560 | 21700 | 0.0013 | - | - | - |
| 2.5678 | 21800 | 0.0013 | - | - | - |
| 2.5796 | 21900 | 0.0013 | - | - | - |
| 2.5913 | 22000 | 0.0013 | 0.0013 | -0.18505765 | 0.6485 |
| 2.6031 | 22100 | 0.0013 | - | - | - |
| 2.6149 | 22200 | 0.0013 | - | - | - |
| 2.6267 | 22300 | 0.0013 | - | - | - |
| 2.6385 | 22400 | 0.0013 | - | - | - |
| 2.6502 | 22500 | 0.0013 | - | - | - |
| 2.6620 | 22600 | 0.0013 | - | - | - |
| 2.6738 | 22700 | 0.0013 | - | - | - |
| 2.6856 | 22800 | 0.0013 | - | - | - |
| 2.6974 | 22900 | 0.0013 | - | - | - |
| 2.7091 | 23000 | 0.0013 | 0.0013 | -0.18397436 | 0.6518 |
| 2.7209 | 23100 | 0.0013 | - | - | - |
| 2.7327 | 23200 | 0.0013 | - | - | - |
| 2.7445 | 23300 | 0.0013 | - | - | - |
| 2.7563 | 23400 | 0.0013 | - | - | - |
| 2.7680 | 23500 | 0.0013 | - | - | - |
| 2.7798 | 23600 | 0.0013 | - | - | - |
| 2.7916 | 23700 | 0.0013 | - | - | - |
| 2.8034 | 23800 | 0.0013 | - | - | - |
| 2.8151 | 23900 | 0.0013 | - | - | - |
| 2.8269 | 24000 | 0.0013 | 0.0013 | -0.18347077 | 0.6547 |
| 2.8387 | 24100 | 0.0013 | - | - | - |
| 2.8505 | 24200 | 0.0013 | - | - | - |
| 2.8623 | 24300 | 0.0013 | - | - | - |
| 2.8740 | 24400 | 0.0013 | - | - | - |
| 2.8858 | 24500 | 0.0013 | - | - | - |
| 2.8976 | 24600 | 0.0013 | - | - | - |
| 2.9094 | 24700 | 0.0013 | - | - | - |
| 2.9212 | 24800 | 0.0013 | - | - | - |
| 2.9329 | 24900 | 0.0013 | - | - | - |
| 2.9447 | 25000 | 0.0013 | 0.0013 | -0.18285994 | 0.6518 |
| 2.9565 | 25100 | 0.0013 | - | - | - |
| 2.9683 | 25200 | 0.0013 | - | - | - |
| 2.9801 | 25300 | 0.0013 | - | - | - |
| 2.9918 | 25400 | 0.0013 | - | - | - |
| 3.0035 | 25500 | 0.0012 | - | - | - |
| 3.0153 | 25600 | 0.0013 | - | - | - |
| 3.0271 | 25700 | 0.0012 | - | - | - |
| 3.0389 | 25800 | 0.0013 | - | - | - |
| 3.0507 | 25900 | 0.0012 | - | - | - |
| 3.0624 | 26000 | 0.0012 | 0.0013 | -0.18248549 | 0.6536 |
| 3.0742 | 26100 | 0.0012 | - | - | - |
| 3.0860 | 26200 | 0.0012 | - | - | - |
| 3.0978 | 26300 | 0.0012 | - | - | - |
| 3.1096 | 26400 | 0.0012 | - | - | - |
| 3.1213 | 26500 | 0.0012 | - | - | - |
| 3.1331 | 26600 | 0.0012 | - | - | - |
| 3.1449 | 26700 | 0.0012 | - | - | - |
| 3.1567 | 26800 | 0.0012 | - | - | - |
| 3.1684 | 26900 | 0.0012 | - | - | - |
| 3.1802 | 27000 | 0.0012 | 0.0013 | -0.1820667 | 0.6569 |
| 3.1920 | 27100 | 0.0012 | - | - | - |
| 3.2038 | 27200 | 0.0012 | - | - | - |
| 3.2156 | 27300 | 0.0012 | - | - | - |
| 3.2273 | 27400 | 0.0012 | - | - | - |
| 3.2391 | 27500 | 0.0012 | - | - | - |
| 3.2509 | 27600 | 0.0012 | - | - | - |
| 3.2627 | 27700 | 0.0012 | - | - | - |
| 3.2745 | 27800 | 0.0012 | - | - | - |
| 3.2862 | 27900 | 0.0012 | - | - | - |
| 3.2980 | 28000 | 0.0012 | 0.0013 | -0.18136427 | 0.6596 |
| 3.3098 | 28100 | 0.0012 | - | - | - |
| 3.3216 | 28200 | 0.0012 | - | - | - |
| 3.3334 | 28300 | 0.0012 | - | - | - |
| 3.3451 | 28400 | 0.0012 | - | - | - |
| 3.3569 | 28500 | 0.0012 | - | - | - |
| 3.3687 | 28600 | 0.0012 | - | - | - |
| 3.3805 | 28700 | 0.0012 | - | - | - |
| 3.3923 | 28800 | 0.0012 | - | - | - |
| 3.4040 | 28900 | 0.0012 | - | - | - |
| 3.4158 | 29000 | 0.0012 | 0.0012 | -0.18090644 | 0.6586 |
| 3.4276 | 29100 | 0.0012 | - | - | - |
| 3.4394 | 29200 | 0.0012 | - | - | - |
| 3.4512 | 29300 | 0.0012 | - | - | - |
| 3.4629 | 29400 | 0.0012 | - | - | - |
| 3.4747 | 29500 | 0.0012 | - | - | - |
| 3.4865 | 29600 | 0.0012 | - | - | - |
| 3.4983 | 29700 | 0.0012 | - | - | - |
| 3.5101 | 29800 | 0.0012 | - | - | - |
| 3.5218 | 29900 | 0.0012 | - | - | - |
| 3.5336 | 30000 | 0.0012 | 0.0012 | -0.18087307 | 0.6602 |
| 3.5454 | 30100 | 0.0012 | - | - | - |
| 3.5572 | 30200 | 0.0012 | - | - | - |
| 3.5690 | 30300 | 0.0012 | - | - | - |
| 3.5807 | 30400 | 0.0012 | - | - | - |
| 3.5925 | 30500 | 0.0012 | - | - | - |
| 3.6043 | 30600 | 0.0012 | - | - | - |
| 3.6161 | 30700 | 0.0012 | - | - | - |
| 3.6279 | 30800 | 0.0012 | - | - | - |
| 3.6396 | 30900 | 0.0012 | - | - | - |
| 3.6514 | 31000 | 0.0012 | 0.0012 | -0.17998679 | 0.6612 |
| 3.6632 | 31100 | 0.0012 | - | - | - |
| 3.6750 | 31200 | 0.0012 | - | - | - |
| 3.6868 | 31300 | 0.0012 | - | - | - |
| 3.6985 | 31400 | 0.0012 | - | - | - |
| 3.7103 | 31500 | 0.0012 | - | - | - |
| 3.7221 | 31600 | 0.0012 | - | - | - |
| 3.7339 | 31700 | 0.0012 | - | - | - |
| 3.7456 | 31800 | 0.0012 | - | - | - |
| 3.7574 | 31900 | 0.0012 | - | - | - |
| 3.7692 | 32000 | 0.0012 | 0.0012 | -0.17991492 | 0.6630 |
| 3.7810 | 32100 | 0.0012 | - | - | - |
| 3.7928 | 32200 | 0.0012 | - | - | - |
| 3.8045 | 32300 | 0.0012 | - | - | - |
| 3.8163 | 32400 | 0.0012 | - | - | - |
| 3.8281 | 32500 | 0.0012 | - | - | - |
| 3.8399 | 32600 | 0.0012 | - | - | - |
| 3.8517 | 32700 | 0.0012 | - | - | - |
| 3.8634 | 32800 | 0.0012 | - | - | - |
| 3.8752 | 32900 | 0.0012 | - | - | - |
| 3.8870 | 33000 | 0.0012 | 0.0012 | -0.1794728 | 0.6632 |
| 3.8988 | 33100 | 0.0012 | - | - | - |
| 3.9106 | 33200 | 0.0012 | - | - | - |
| 3.9223 | 33300 | 0.0012 | - | - | - |
| 3.9341 | 33400 | 0.0012 | - | - | - |
| 3.9459 | 33500 | 0.0012 | - | - | - |
| 3.9577 | 33600 | 0.0012 | - | - | - |
| 3.9695 | 33700 | 0.0012 | - | - | - |
| 3.9812 | 33800 | 0.0012 | - | - | - |
| 3.9930 | 33900 | 0.0012 | - | - | - |
| 4.0047 | 34000 | 0.0012 | 0.0012 | -0.17887509 | 0.6643 |
| 4.0165 | 34100 | 0.0012 | - | - | - |
| 4.0283 | 34200 | 0.0012 | - | - | - |
| 4.0401 | 34300 | 0.0012 | - | - | - |
| 4.0518 | 34400 | 0.0012 | - | - | - |
| 4.0636 | 34500 | 0.0012 | - | - | - |
| 4.0754 | 34600 | 0.0012 | - | - | - |
| 4.0872 | 34700 | 0.0012 | - | - | - |
| 4.0989 | 34800 | 0.0012 | - | - | - |
| 4.1107 | 34900 | 0.0012 | - | - | - |
| 4.1225 | 35000 | 0.0012 | 0.0012 | -0.17876983 | 0.6648 |
| 4.1343 | 35100 | 0.0012 | - | - | - |
| 4.1461 | 35200 | 0.0012 | - | - | - |
| 4.1578 | 35300 | 0.0012 | - | - | - |
| 4.1696 | 35400 | 0.0012 | - | - | - |
| 4.1814 | 35500 | 0.0012 | - | - | - |
| 4.1932 | 35600 | 0.0012 | - | - | - |
| 4.2050 | 35700 | 0.0012 | - | - | - |
| 4.2167 | 35800 | 0.0012 | - | - | - |
| 4.2285 | 35900 | 0.0012 | - | - | - |
| 4.2403 | 36000 | 0.0012 | 0.0012 | -0.17866188 | 0.6629 |
| 4.2521 | 36100 | 0.0012 | - | - | - |
| 4.2639 | 36200 | 0.0012 | - | - | - |
| 4.2756 | 36300 | 0.0012 | - | - | - |
| 4.2874 | 36400 | 0.0012 | - | - | - |
| 4.2992 | 36500 | 0.0012 | - | - | - |
| 4.3110 | 36600 | 0.0012 | - | - | - |
| 4.3228 | 36700 | 0.0012 | - | - | - |
| 4.3345 | 36800 | 0.0012 | - | - | - |
| 4.3463 | 36900 | 0.0012 | - | - | - |
| 4.3581 | 37000 | 0.0012 | 0.0012 | -0.17826374 | 0.6642 |
| 4.3699 | 37100 | 0.0012 | - | - | - |
| 4.3817 | 37200 | 0.0012 | - | - | - |
| 4.3934 | 37300 | 0.0012 | - | - | - |
| 4.4052 | 37400 | 0.0012 | - | - | - |
| 4.4170 | 37500 | 0.0012 | - | - | - |
| 4.4288 | 37600 | 0.0012 | - | - | - |
| 4.4406 | 37700 | 0.0012 | - | - | - |
| 4.4523 | 37800 | 0.0012 | - | - | - |
| 4.4641 | 37900 | 0.0012 | - | - | - |
| 4.4759 | 38000 | 0.0012 | 0.0012 | -0.17813009 | 0.6654 |
| 4.4877 | 38100 | 0.0012 | - | - | - |
| 4.4995 | 38200 | 0.0012 | - | - | - |
| 4.5112 | 38300 | 0.0012 | - | - | - |
| 4.5230 | 38400 | 0.0012 | - | - | - |
| 4.5348 | 38500 | 0.0012 | - | - | - |
| 4.5466 | 38600 | 0.0012 | - | - | - |
| 4.5584 | 38700 | 0.0012 | - | - | - |
| 4.5701 | 38800 | 0.0012 | - | - | - |
| 4.5819 | 38900 | 0.0012 | - | - | - |
| 4.5937 | 39000 | 0.0012 | 0.0012 | -0.17787422 | 0.6677 |
| 4.6055 | 39100 | 0.0012 | - | - | - |
| 4.6173 | 39200 | 0.0012 | - | - | - |
| 4.6290 | 39300 | 0.0012 | - | - | - |
| 4.6408 | 39400 | 0.0012 | - | - | - |
| 4.6526 | 39500 | 0.0012 | - | - | - |
| 4.6644 | 39600 | 0.0012 | - | - | - |
| 4.6761 | 39700 | 0.0012 | - | - | - |
| 4.6879 | 39800 | 0.0012 | - | - | - |
| 4.6997 | 39900 | 0.0012 | - | - | - |
| 4.7115 | 40000 | 0.0012 | 0.0012 | -0.17763866 | 0.6631 |
| 4.7233 | 40100 | 0.0012 | - | - | - |
| 4.7350 | 40200 | 0.0012 | - | - | - |
| 4.7468 | 40300 | 0.0012 | - | - | - |
| 4.7586 | 40400 | 0.0012 | - | - | - |
| 4.7704 | 40500 | 0.0012 | - | - | - |
| 4.7822 | 40600 | 0.0012 | - | - | - |
| 4.7939 | 40700 | 0.0012 | - | - | - |
| 4.8057 | 40800 | 0.0012 | - | - | - |
| 4.8175 | 40900 | 0.0012 | - | - | - |
| 4.8293 | 41000 | 0.0012 | 0.0012 | -0.17747028 | 0.6683 |
| 4.8411 | 41100 | 0.0012 | - | - | - |
| 4.8528 | 41200 | 0.0012 | - | - | - |
| 4.8646 | 41300 | 0.0012 | - | - | - |
| 4.8764 | 41400 | 0.0012 | - | - | - |
| 4.8882 | 41500 | 0.0012 | - | - | - |
| 4.9000 | 41600 | 0.0012 | - | - | - |
| 4.9117 | 41700 | 0.0012 | - | - | - |
| 4.9235 | 41800 | 0.0012 | - | - | - |
| 4.9353 | 41900 | 0.0012 | - | - | - |
| 4.9471 | 42000 | 0.0012 | 0.0012 | -0.1769735 | 0.6677 |
| 4.9589 | 42100 | 0.0012 | - | - | - |
| 4.9706 | 42200 | 0.0012 | - | - | - |
| 4.9824 | 42300 | 0.0012 | - | - | - |
| 4.9942 | 42400 | 0.0012 | - | - | - |
| 5.0059 | 42500 | 0.0012 | - | - | - |
| 5.0177 | 42600 | 0.0012 | - | - | - |
| 5.0294 | 42700 | 0.0012 | - | - | - |
| 5.0412 | 42800 | 0.0012 | - | - | - |
| 5.0530 | 42900 | 0.0012 | - | - | - |
| 5.0648 | 43000 | 0.0012 | 0.0012 | -0.17719762 | 0.6653 |
| 5.0766 | 43100 | 0.0012 | - | - | - |
| 5.0883 | 43200 | 0.0012 | - | - | - |
| 5.1001 | 43300 | 0.0012 | - | - | - |
| 5.1119 | 43400 | 0.0012 | - | - | - |
| 5.1237 | 43500 | 0.0012 | - | - | - |
| 5.1355 | 43600 | 0.0012 | - | - | - |
| 5.1472 | 43700 | 0.0012 | - | - | - |
| 5.1590 | 43800 | 0.0012 | - | - | - |
| 5.1708 | 43900 | 0.0012 | - | - | - |
| 5.1826 | 44000 | 0.0012 | 0.0012 | -0.17653656 | 0.6693 |
| 5.1944 | 44100 | 0.0012 | - | - | - |
| 5.2061 | 44200 | 0.0012 | - | - | - |
| 5.2179 | 44300 | 0.0012 | - | - | - |
| 5.2297 | 44400 | 0.0012 | - | - | - |
| 5.2415 | 44500 | 0.0012 | - | - | - |
| 5.2533 | 44600 | 0.0012 | - | - | - |
| 5.2650 | 44700 | 0.0012 | - | - | - |
| 5.2768 | 44800 | 0.0012 | - | - | - |
| 5.2886 | 44900 | 0.0012 | - | - | - |
| 5.3004 | 45000 | 0.0012 | 0.0012 | -0.17657608 | 0.6683 |
| 5.3122 | 45100 | 0.0012 | - | - | - |
| 5.3239 | 45200 | 0.0012 | - | - | - |
| 5.3357 | 45300 | 0.0012 | - | - | - |
| 5.3475 | 45400 | 0.0012 | - | - | - |
| 5.3593 | 45500 | 0.0012 | - | - | - |
| 5.3711 | 45600 | 0.0012 | - | - | - |
| 5.3828 | 45700 | 0.0012 | - | - | - |
| 5.3946 | 45800 | 0.0012 | - | - | - |
| 5.4064 | 45900 | 0.0012 | - | - | - |
| 5.4182 | 46000 | 0.0012 | 0.0012 | -0.17643414 | 0.6694 |
| 5.4300 | 46100 | 0.0012 | - | - | - |
| 5.4417 | 46200 | 0.0012 | - | - | - |
| 5.4535 | 46300 | 0.0012 | - | - | - |
| 5.4653 | 46400 | 0.0012 | - | - | - |
| 5.4771 | 46500 | 0.0012 | - | - | - |
| 5.4889 | 46600 | 0.0012 | - | - | - |
| 5.5006 | 46700 | 0.0012 | - | - | - |
| 5.5124 | 46800 | 0.0012 | - | - | - |
| 5.5242 | 46900 | 0.0012 | - | - | - |
| 5.5360 | 47000 | 0.0012 | 0.0012 | -0.17619923 | 0.6679 |
| 5.5478 | 47100 | 0.0012 | - | - | - |
| 5.5595 | 47200 | 0.0012 | - | - | - |
| 5.5713 | 47300 | 0.0012 | - | - | - |
| 5.5831 | 47400 | 0.0012 | - | - | - |
| 5.5949 | 47500 | 0.0012 | - | - | - |
| 5.6066 | 47600 | 0.0012 | - | - | - |
| 5.6184 | 47700 | 0.0012 | - | - | - |
| 5.6302 | 47800 | 0.0012 | - | - | - |
| 5.6420 | 47900 | 0.0012 | - | - | - |
| 5.6538 | 48000 | 0.0012 | 0.0012 | -0.17586452 | 0.6694 |
| 5.6655 | 48100 | 0.0012 | - | - | - |
| 5.6773 | 48200 | 0.0012 | - | - | - |
| 5.6891 | 48300 | 0.0012 | - | - | - |
| 5.7009 | 48400 | 0.0012 | - | - | - |
| 5.7127 | 48500 | 0.0012 | - | - | - |
| 5.7244 | 48600 | 0.0012 | - | - | - |
| 5.7362 | 48700 | 0.0012 | - | - | - |
| 5.7480 | 48800 | 0.0012 | - | - | - |
| 5.7598 | 48900 | 0.0012 | - | - | - |
| 5.7716 | 49000 | 0.0012 | 0.0012 | -0.1760546 | 0.6687 |
| 5.7833 | 49100 | 0.0012 | - | - | - |
| 5.7951 | 49200 | 0.0012 | - | - | - |
| 5.8069 | 49300 | 0.0012 | - | - | - |
| 5.8187 | 49400 | 0.0012 | - | - | - |
| 5.8305 | 49500 | 0.0012 | - | - | - |
| 5.8422 | 49600 | 0.0012 | - | - | - |
| 5.8540 | 49700 | 0.0012 | - | - | - |
| 5.8658 | 49800 | 0.0012 | - | - | - |
| 5.8776 | 49900 | 0.0012 | - | - | - |
| 5.8894 | 50000 | 0.0012 | 0.0012 | -0.17587146 | 0.6677 |
| 5.9011 | 50100 | 0.0012 | - | - | - |
| 5.9129 | 50200 | 0.0012 | - | - | - |
| 5.9247 | 50300 | 0.0012 | - | - | - |
| 5.9365 | 50400 | 0.0012 | - | - | - |
| 5.9483 | 50500 | 0.0012 | - | - | - |
| 5.9600 | 50600 | 0.0012 | - | - | - |
| 5.9718 | 50700 | 0.0012 | - | - | - |
| 5.9836 | 50800 | 0.0012 | - | - | - |
| 5.9954 | 50900 | 0.0012 | - | - | - |
| 6.0071 | 51000 | 0.0011 | 0.0012 | -0.17524663 | 0.6717 |
| 6.0188 | 51100 | 0.0012 | - | - | - |
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| 6.0660 | 51500 | 0.0012 | - | - | - |
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| 6.1013 | 51800 | 0.0012 | - | - | - |
| 6.1131 | 51900 | 0.0012 | - | - | - |
| 6.1249 | 52000 | 0.0012 | 0.0012 | -0.17546739 | 0.6696 |
| 6.1366 | 52100 | 0.0012 | - | - | - |
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| 6.2191 | 52800 | 0.0012 | - | - | - |
| 6.2309 | 52900 | 0.0012 | - | - | - |
| 6.2427 | 53000 | 0.0012 | 0.0012 | -0.17500012 | 0.6687 |
| 6.2544 | 53100 | 0.0012 | - | - | - |
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| 6.3487 | 53900 | 0.0012 | - | - | - |
| 6.3605 | 54000 | 0.0012 | 0.0012 | -0.1750529 | 0.6728 |
| 6.3722 | 54100 | 0.0012 | - | - | - |
| 6.3840 | 54200 | 0.0012 | - | - | - |
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| 6.4665 | 54900 | 0.0011 | - | - | - |
| 6.4783 | 55000 | 0.0011 | 0.0012 | -0.17491975 | 0.6687 |
| 6.4900 | 55100 | 0.0012 | - | - | - |
| 6.5018 | 55200 | 0.0012 | - | - | - |
| 6.5136 | 55300 | 0.0011 | - | - | - |
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| 6.5843 | 55900 | 0.0012 | - | - | - |
| 6.5960 | 56000 | 0.0012 | 0.0012 | -0.17456226 | 0.6726 |
| 6.6078 | 56100 | 0.0012 | - | - | - |
| 6.6196 | 56200 | 0.0011 | - | - | - |
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| 6.7138 | 57000 | 0.0011 | 0.0012 | -0.17479357 | 0.6721 |
| 6.7256 | 57100 | 0.0011 | - | - | - |
| 6.7374 | 57200 | 0.0012 | - | - | - |
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| 6.8316 | 58000 | 0.0011 | 0.0012 | -0.17439803 | 0.6710 |
| 6.8434 | 58100 | 0.0011 | - | - | - |
| 6.8552 | 58200 | 0.0011 | - | - | - |
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| 6.9377 | 58900 | 0.0011 | - | - | - |
| 6.9494 | 59000 | 0.0011 | 0.0012 | -0.17475043 | 0.6718 |
| 6.9612 | 59100 | 0.0011 | - | - | - |
| 6.9730 | 59200 | 0.0011 | - | - | - |
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| 7.0554 | 59900 | 0.0011 | - | - | - |
| 7.0671 | 60000 | 0.0011 | 0.0012 | -0.17430946 | 0.6722 |
| 7.0789 | 60100 | 0.0011 | - | - | - |
| 7.0907 | 60200 | 0.0011 | - | - | - |
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| 7.1849 | 61000 | 0.0011 | 0.0012 | -0.17427135 | 0.6732 |
| 7.1967 | 61100 | 0.0011 | - | - | - |
| 7.2085 | 61200 | 0.0011 | - | - | - |
| 7.2203 | 61300 | 0.0011 | - | - | - |
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| 7.2792 | 61800 | 0.0011 | - | - | - |
| 7.2910 | 61900 | 0.0011 | - | - | - |
| 7.3027 | 62000 | 0.0011 | 0.0012 | -0.17400834 | 0.6719 |
| 7.3145 | 62100 | 0.0011 | - | - | - |
| 7.3263 | 62200 | 0.0011 | - | - | - |
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| 7.3970 | 62800 | 0.0011 | - | - | - |
| 7.4088 | 62900 | 0.0011 | - | - | - |
| 7.4205 | 63000 | 0.0011 | 0.0012 | -0.1740251 | 0.6726 |
| 7.4323 | 63100 | 0.0011 | - | - | - |
| 7.4441 | 63200 | 0.0011 | - | - | - |
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| 7.5383 | 64000 | 0.0011 | 0.0012 | -0.17380536 | 0.6712 |
| 7.5501 | 64100 | 0.0011 | - | - | - |
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| 7.6561 | 65000 | 0.0011 | 0.0012 | -0.17366579 | 0.6723 |
| 7.6679 | 65100 | 0.0011 | - | - | - |
| 7.6797 | 65200 | 0.0011 | - | - | - |
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| 7.7739 | 66000 | 0.0011 | 0.0012 | -0.17369126 | 0.6746 |
| 7.7857 | 66100 | 0.0011 | - | - | - |
| 7.7975 | 66200 | 0.0011 | - | - | - |
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| 7.8917 | 67000 | 0.0011 | 0.0012 | -0.17330332 | 0.6740 |
| 7.9035 | 67100 | 0.0011 | - | - | - |
| 7.9153 | 67200 | 0.0011 | - | - | - |
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| 8.0094 | 68000 | 0.0011 | 0.0012 | -0.17326406 | 0.6732 |
| 8.0212 | 68100 | 0.0011 | - | - | - |
| 8.0330 | 68200 | 0.0011 | - | - | - |
| 8.0448 | 68300 | 0.0011 | - | - | - |
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| 8.1272 | 69000 | 0.0011 | 0.0012 | -0.17333928 | 0.6730 |
| 8.1390 | 69100 | 0.0011 | - | - | - |
| 8.1508 | 69200 | 0.0011 | - | - | - |
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| 8.2450 | 70000 | 0.0011 | 0.0012 | -0.17320158 | 0.6726 |
| 8.2568 | 70100 | 0.0011 | - | - | - |
| 8.2686 | 70200 | 0.0011 | - | - | - |
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| 8.3393 | 70800 | 0.0011 | - | - | - |
| 8.3510 | 70900 | 0.0011 | - | - | - |
| 8.3628 | 71000 | 0.0011 | 0.0012 | -0.17346236 | 0.6726 |
| 8.3746 | 71100 | 0.0011 | - | - | - |
| 8.3864 | 71200 | 0.0011 | - | - | - |
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| 8.4570 | 71800 | 0.0011 | - | - | - |
| 8.4688 | 71900 | 0.0011 | - | - | - |
| 8.4806 | 72000 | 0.0011 | 0.0012 | -0.17322065 | 0.6749 |
| 8.4924 | 72100 | 0.0011 | - | - | - |
| 8.5042 | 72200 | 0.0011 | - | - | - |
| 8.5159 | 72300 | 0.0011 | - | - | - |
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| 8.5748 | 72800 | 0.0011 | - | - | - |
| 8.5866 | 72900 | 0.0011 | - | - | - |
| 8.5984 | 73000 | 0.0011 | 0.0011 | -0.1729941 | 0.6725 |
| 8.6102 | 73100 | 0.0011 | - | - | - |
| 8.6220 | 73200 | 0.0011 | - | - | - |
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| 8.7044 | 73900 | 0.0011 | - | - | - |
| 8.7162 | 74000 | 0.0011 | 0.0011 | -0.17297848 | 0.6719 |
| 8.7280 | 74100 | 0.0011 | - | - | - |
| 8.7398 | 74200 | 0.0011 | - | - | - |
| 8.7515 | 74300 | 0.0011 | - | - | - |
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| 8.8340 | 75000 | 0.0011 | 0.0011 | -0.17291391 | 0.6728 |
| 8.8458 | 75100 | 0.0011 | - | - | - |
| 8.8576 | 75200 | 0.0011 | - | - | - |
| 8.8693 | 75300 | 0.0011 | - | - | - |
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| 8.9400 | 75900 | 0.0011 | - | - | - |
| 8.9518 | 76000 | 0.0011 | 0.0011 | -0.1728144 | 0.6741 |
| 8.9636 | 76100 | 0.0011 | - | - | - |
| 8.9754 | 76200 | 0.0011 | - | - | - |
| 8.9871 | 76300 | 0.0011 | - | - | - |
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| 9.0459 | 76800 | 0.0011 | - | - | - |
| 9.0577 | 76900 | 0.0011 | - | - | - |
| 9.0695 | 77000 | 0.0011 | 0.0011 | -0.1725926 | 0.6728 |
| 9.0813 | 77100 | 0.0011 | - | - | - |
| 9.0931 | 77200 | 0.0011 | - | - | - |
| 9.1048 | 77300 | 0.0011 | - | - | - |
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| 9.1873 | 78000 | 0.0011 | 0.0011 | -0.17272119 | 0.6735 |
| 9.1991 | 78100 | 0.0011 | - | - | - |
| 9.2109 | 78200 | 0.0011 | - | - | - |
| 9.2226 | 78300 | 0.0011 | - | - | - |
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| 9.3051 | 79000 | 0.0011 | 0.0011 | -0.1726715 | 0.6730 |
| 9.3169 | 79100 | 0.0011 | - | - | - |
| 9.3287 | 79200 | 0.0011 | - | - | - |
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| 9.4229 | 80000 | 0.0011 | 0.0011 | -0.172526 | 0.6737 |
| 9.4347 | 80100 | 0.0011 | - | - | - |
| 9.4464 | 80200 | 0.0011 | - | - | - |
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| 9.5407 | 81000 | 0.0011 | 0.0011 | -0.1724438 | 0.6749 |
| 9.5525 | 81100 | 0.0011 | - | - | - |
| 9.5642 | 81200 | 0.0011 | - | - | - |
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| 9.5996 | 81500 | 0.0011 | - | - | - |
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| 9.6349 | 81800 | 0.0011 | - | - | - |
| 9.6467 | 81900 | 0.0011 | - | - | - |
| 9.6585 | 82000 | 0.0011 | 0.0011 | -0.17250738 | 0.6729 |
| 9.6703 | 82100 | 0.0011 | - | - | - |
| 9.6820 | 82200 | 0.0011 | - | - | - |
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| 9.7409 | 82700 | 0.0011 | - | - | - |
| 9.7527 | 82800 | 0.0011 | - | - | - |
| 9.7645 | 82900 | 0.0011 | - | - | - |
| 9.7763 | 83000 | 0.0011 | 0.0011 | -0.1723447 | 0.6728 |
| 9.7881 | 83100 | 0.0011 | - | - | - |
| 9.7998 | 83200 | 0.0011 | - | - | - |
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| 9.8941 | 84000 | 0.0011 | 0.0011 | -0.17230988 | 0.6750 |
| 9.9059 | 84100 | 0.0011 | - | - | - |
| 9.9176 | 84200 | 0.0011 | - | - | - |
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