---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1000000
- loss:MSELoss
base_model: distilbert/distilbert-base-multilingual-cased
widget:
- source_sentence: "Agreement on Pledge and Right of Disposal of Cash Account based\
\ on agreement template dated 4 June 2015 1. This agreement (the â\x80\x9CPledged\
\ Cash Account Agreementâ\x80\x9D or â\x80\x9CAgreementâ\x80\x9D) has been entered\
\ into on this. [insert the day] day of [month] [year] by and between."
sentences:
- Perjanjian Gadai dan Hak Pelepasan Rekening Kas berdasarkan template perjanjian
tanggal 4 Juni 2015 1. Perjanjian ini (¢Â€ÂœPerjanjian Gadai Rekening Kasâ€Â
atau ¢ €ÂœPerjanjian†) telah ditandatangani mengenai hal ini.
[masukkan hari] hari [bulan] [tahun] oleh dan di antara.
- verizon.net yahoo
- Teori sosial-kognitif adalah perspektif teoretis di mana belajar dengan mengamati
orang lain adalah fokus studi. Teori sosial-kognitif didasarkan pada beberapa
asumsi dasar. Salah satunya adalah bahwa orang dapat belajar dengan mengamati
orang lain. Pembelajar dapat memperoleh perilaku dan pengetahuan baru hanya dengan
mengamati model. Teori sosial-kognitif berfokus pada pembelajaran yang terjadi
dalam konteks sosial. Dalam konteks sosial ini, peserta didik mengamati perilaku
dan keterampilan baru dari seorang model. Mereka mungkin menginternalisasi dan
mendemonstrasikan keterampilan tersebut atau terkadang tidak.
- source_sentence: a poem of six six-line stanzas and a three-line envoy, originally
without rhyme, in which each stanza repeats the end words of the lines of the
first stanza, but in different order, the envoy using the six words again, three
in the middle of the lines and three at the end. 1 Also called sextain.
sentences:
- puisi enam bait enam baris dan utusan tiga baris, awalnya tanpa sajak, di mana
setiap bait mengulangi kata-kata akhir dari baris bait pertama, tetapi dalam urutan
yang berbeda, utusan menggunakan enam kata lagi, tiga di tengah baris dan tiga
di akhir. 1 Disebut juga sextain.
- Lipase lambung adalah enzim pencernaan lain yang dibuat oleh sel utama. Ini membantu
memecah lemak rantai pendek dan menengah. Amilase juga ditemukan dalam cairan
lambung, tetapi tidak dibuat oleh lambung. Enzim ini berasal dari air liur dan
berjalan bersama dengan bolus ke dalam lambung.
- Masuk dengan ID Apple Anda dan atur iCloud Drive. Masukkan ID Apple dan kata sandi
Anda. Untuk melewati langkah ini, atau jika Anda tidak memiliki ID Apple, ketuk
Tidak memiliki ID Apple atau lupa. Jika Anda menggunakan lebih dari satu ID, ketuk
Gunakan ID Apple yang berbeda untuk iCloud dan iTunes.masuk dengan ID Apple Anda
dan atur iCloud Drive. Masukkan ID Apple dan kata sandi Anda. Untuk melewati langkah
ini, atau jika Anda tidak memiliki ID Apple, ketuk Tidak memiliki ID Apple atau
lupa. Jika Anda menggunakan lebih dari satu ID, ketuk Gunakan ID Apple yang berbeda
untuk iCloud dan iTunes.
- source_sentence: "Ways to Calculate GSAâ\x80\x99s. Industrial Funding Fee (IFF)\
\ Divide the original unit price by one (1) minus the IFF rate (e.g., 1 â\x80\x93\
\ 0.0075 = 0.9925) to establish the contract price. For example, if the original\
\ price of $198.50 is divided by .9925 , the selling price will be $200."
sentences:
- Kompetisi Pelatih Pertunjukan Dr. Oz. Dr. Oz sedang mencari pelatih untuk bergabung
dalam acara ini untuk membantu menginspirasi jutaan orang lain di rumah agar bugar
dan sehat! LIHAT SELENGKAPNYA
- perbedaan antara tulang rusuk sejati, tulang rusuk palsu, dan tulang rusuk melayang
- Cara Menghitung GSA¢Âââ. Biaya Pendanaan Industri (IFF) Bagi harga satuan asli
dengan satu (1) dikurangi tarif IFF (misalnya, 1 ¢Â€Â“ 0,0075 = 0,9925)
untuk menetapkan harga kontrak. Misalnya, jika harga asli $198,50 dibagi ,9925
, harga jualnya adalah $200.
- source_sentence: how does one get a scrapped vehicle registered again
sentences:
- bagaimana cara mendapatkan kendaraan bekas yang didaftarkan kembali?
- definisi bisnis yang bertanggung jawab
- apakah cwu mendapatkan perguruan tinggi?
- source_sentence: "1 Concrete is typically measured by cubic yards (3â\x80\x99x3â\x80\
\x99x3â\x80\x99). 2 An average cost for a cubic yard of concrete is $75 to $125,\
\ depending on how much is needed and local prices. 3 Labor costs to pour and\
\ form concrete run somewhere around $3.50 to $7.00 per square foot. An average\
\ cost for a cubic yard of concrete is $75 to $125, depending on how much is needed\
\ and local prices. 2 Labor costs to pour and form concrete run somewhere around\
\ $3.50 to $7.00 per square foot."
sentences:
- 1 Beton biasanya diukur dengan meter kubik (3âÂâââx3Ãâ¢Ã‚ââx3âÂââ„¢).
2 Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung
pada berapa banyak yang dibutuhkan dan harga setempat. 3 Biaya tenaga kerja untuk
menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi.
Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung
pada berapa banyak yang dibutuhkan dan harga lokal. 2 Biaya tenaga kerja untuk
menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi.
- seberapa terlambat alat utara terbuka?
- Parrot Tattoos - Polly ingin cracker.. Ungkapan ini identik dengan 'parrot', terutama
yang duduk di bahu bajak laut, seperti yang dibuat terkenal dalam cerita klasik
Robert Louis Stevenson, Treasure Island (1883).lint', yang mungkin tidak adalah
burung beo pertama yang diasosiasikan dengan nama 'Polly', tapi dia pasti mempopulerkannya.
Dan Polly tentu memastikan burung beo itu akan menjadi simbol ikonik dari tradisi
bajak laut. Sebagai pendamping legendaris bagi manusia, burung beo menyarankan
semacam wali.
datasets:
- carles-undergrad-thesis/en-id-parallel-sentences
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- negative_mse
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
model-index:
- name: SentenceTransformer based on distilbert/distilbert-base-multilingual-cased
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: default
type: default
metrics:
- type: negative_mse
value: -3.55539433658123
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: default
type: default
metrics:
- type: src2trg_accuracy
value: 0.9894
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.9861
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.9877499999999999
name: Mean Accuracy
---
# SentenceTransformer based on distilbert/distilbert-base-multilingual-cased
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilbert-base-multilingual-cased](https://huggingface.co./distilbert/distilbert-base-multilingual-cased) on the [default](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences) dataset. 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:** [distilbert/distilbert-base-multilingual-cased](https://huggingface.co./distilbert/distilbert-base-multilingual-cased)
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [default](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences)
### 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: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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 = [
'1 Concrete is typically measured by cubic yards (3â\x80\x99x3â\x80\x99x3â\x80\x99). 2 An average cost for a cubic yard of concrete is $75 to $125, depending on how much is needed and local prices. 3 Labor costs to pour and form concrete run somewhere around $3.50 to $7.00 per square foot. An average cost for a cubic yard of concrete is $75 to $125, depending on how much is needed and local prices. 2 Labor costs to pour and form concrete run somewhere around $3.50 to $7.00 per square foot.',
'1 Beton biasanya diukur dengan meter kubik (3âÂâââx3Ãâ¢Ã‚ââx3âÂââ„¢). 2 Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung pada berapa banyak yang dibutuhkan dan harga setempat. 3 Biaya tenaga kerja untuk menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi. Biaya rata-rata untuk satu yard kubik beton adalah $75 sampai $125, tergantung pada berapa banyak yang dibutuhkan dan harga lokal. 2 Biaya tenaga kerja untuk menuangkan dan membentuk beton berkisar antara $3,50 hingga $7,00 per kaki persegi.',
"Parrot Tattoos - Polly ingin cracker.. Ungkapan ini identik dengan 'parrot', terutama yang duduk di bahu bajak laut, seperti yang dibuat terkenal dalam cerita klasik Robert Louis Stevenson, Treasure Island (1883).lint', yang mungkin tidak adalah burung beo pertama yang diasosiasikan dengan nama 'Polly', tapi dia pasti mempopulerkannya. Dan Polly tentu memastikan burung beo itu akan menjadi simbol ikonik dari tradisi bajak laut. Sebagai pendamping legendaris bagi manusia, burung beo menyarankan semacam wali.",
]
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]
```
## Evaluation
### Metrics
#### Knowledge Distillation
* Dataset: `default`
* Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | Value |
|:-----------------|:------------|
| **negative_mse** | **-3.5554** |
#### Translation
* Dataset: `default`
* Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | Value |
|:------------------|:-----------|
| src2trg_accuracy | 0.9894 |
| trg2src_accuracy | 0.9861 |
| **mean_accuracy** | **0.9877** |
## Training Details
### Training Dataset
#### default
* Dataset: [default](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences) at [c8bc0cb](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences/tree/c8bc0cbe69684da379af3d36424041162d63e702)
* Size: 1,000,000 training samples
* Columns: english, indonesian, and label
* Approximate statistics based on the first 1000 samples:
| | english | indonesian | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | - min: 4 tokens
- mean: 44.27 tokens
- max: 128 tokens
| - min: 5 tokens
- mean: 48.93 tokens
- max: 128 tokens
| |
* Samples:
| english | indonesian | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------|
| This sample job description shares how one smaller sized, growing, multi-site nonprofit organization configured the role of executive director.The executive director is responsible for general management as well as designing a national expansion plan. There also is a heavy emphasis on program evaluation.Feel free to use this sample job description in creating one for your organization.osition. Reporting to the Board of Directors, the Executive Director (ED) will have overall strategic and operational responsibility for XYZ Nonprofit's staff, programs, expansion, and execution of its mission. S/he will initially develop deep knowledge of field, core programs, operations, and business plans. | Uraian tugas contoh ini membagikan bagaimana satu organisasi nirlaba multi-situs berukuran lebih kecil, berkembang, mengonfigurasi peran direktur eksekutif. Direktur eksekutif bertanggung jawab atas manajemen umum serta merancang rencana ekspansi nasional. Ada juga penekanan berat pada evaluasi program. Jangan ragu untuk menggunakan contoh deskripsi pekerjaan ini dalam membuat satu untuk posisi organisasi Anda. Melaporkan kepada Dewan Direksi, Direktur Eksekutif (ED) akan memiliki tanggung jawab strategis dan operasional secara keseluruhan untuk staf, program, ekspansi, dan pelaksanaan misi XYZ Nirlaba. Dia awalnya akan mengembangkan pengetahuan yang mendalam tentang lapangan, program inti, operasi, dan rencana bisnis. | [-0.4337165653705597, -0.0650932714343071, -0.04308838024735451, -0.1756953001022339, 0.32854965329170227, ...] |
| Industrial revolution occured last in Russia. In Germany, France and United States industrial revolution occured in early-to-mid 1800's. While in Russia creation of railroads, and foundation of factories happened by govermental initiatives towards the end of XIX century.n Germany, France and United States industrial revolution occured in early-to-mid 1800's. | Revolusi industri terakhir terjadi di Rusia. Di Jerman, Perancis dan Amerika Serikat terjadi revolusi industri pada awal hingga pertengahan 1800-an. Sedangkan di Rusia pembuatan rel kereta api, dan pendirian pabrik terjadi atas inisiatif pemerintah menjelang akhir abad XIX. Revolusi industri Jerman, Prancis dan Amerika Serikat terjadi pada awal hingga pertengahan 1800-an. | [-0.22887374460697174, -0.17583712935447693, 0.08270637691020966, -0.15496928989887238, -0.18010610342025757, ...] |
| what causes hordeolum internum left lower eyelid | apa penyebab hordeolum internum kelopak mata kiri bawah | [-0.19872592389583588, 0.4119395911693573, 0.3756648004055023, -0.4884617030620575, 0.15375499427318573, ...] |
* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Dataset
#### default
* Dataset: [default](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences) at [c8bc0cb](https://huggingface.co./datasets/carles-undergrad-thesis/en-id-parallel-sentences/tree/c8bc0cbe69684da379af3d36424041162d63e702)
* Size: 1,000,000 evaluation samples
* Columns: english, indonesian, and label
* Approximate statistics based on the first 1000 samples:
| | english | indonesian | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | - min: 5 tokens
- mean: 46.58 tokens
- max: 128 tokens
| - min: 5 tokens
- mean: 51.0 tokens
- max: 128 tokens
| |
* Samples:
| english | indonesian | label |
|:---------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------|
| do appraisers give adjustments for lot size | apakah penilai memberikan penyesuaian untuk ukuran lot? | [0.12256570905447006, 0.011573846451938152, -0.19426874816417694, -0.17596185207366943, 0.35024771094322205, ...] |
| hotels in binghamton ny | hotel di binghamton ny | [0.14259624481201172, -0.048470016568899155, 0.1078888401389122, 0.06728225946426392, 0.6096671223640442, ...] |
| guitarist kenny greenberg | gitaris kenny greenberg | [-0.6973275542259216, 0.27737292647361755, -0.09295299649238586, 0.24035970866680145, 0.154855415225029, ...] |
* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `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`: True
- `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`: False
- `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 | default loss | default_negative_mse | default_mean_accuracy |
|:------:|:-----:|:-------------:|:------------:|:--------------------:|:---------------------:|
| 0.0065 | 100 | 0.1968 | - | - | - |
| 0.0129 | 200 | 0.1797 | - | - | - |
| 0.0194 | 300 | 0.1596 | - | - | - |
| 0.0259 | 400 | 0.1367 | - | - | - |
| 0.0323 | 500 | 0.1167 | - | - | - |
| 0.0388 | 600 | 0.103 | - | - | - |
| 0.0453 | 700 | 0.0954 | - | - | - |
| 0.0517 | 800 | 0.0909 | - | - | - |
| 0.0582 | 900 | 0.088 | - | - | - |
| 0.0646 | 1000 | 0.0861 | - | - | - |
| 0.0711 | 1100 | 0.0847 | - | - | - |
| 0.0776 | 1200 | 0.082 | - | - | - |
| 0.0840 | 1300 | 0.0818 | - | - | - |
| 0.0905 | 1400 | 0.0813 | - | - | - |
| 0.0970 | 1500 | 0.0804 | - | - | - |
| 0.1034 | 1600 | 0.0817 | - | - | - |
| 0.1099 | 1700 | 0.0799 | - | - | - |
| 0.1164 | 1800 | 0.0804 | - | - | - |
| 0.1228 | 1900 | 0.0802 | - | - | - |
| 0.1293 | 2000 | 0.0791 | - | - | - |
| 0.1358 | 2100 | 0.0789 | - | - | - |
| 0.1422 | 2200 | 0.0783 | - | - | - |
| 0.1487 | 2300 | 0.0783 | - | - | - |
| 0.1551 | 2400 | 0.077 | - | - | - |
| 0.1616 | 2500 | 0.0762 | - | - | - |
| 0.1681 | 2600 | 0.0762 | - | - | - |
| 0.1745 | 2700 | 0.0754 | - | - | - |
| 0.1810 | 2800 | 0.075 | - | - | - |
| 0.1875 | 2900 | 0.0735 | - | - | - |
| 0.1939 | 3000 | 0.0745 | - | - | - |
| 0.2004 | 3100 | 0.0739 | - | - | - |
| 0.2069 | 3200 | 0.0732 | - | - | - |
| 0.2133 | 3300 | 0.0724 | - | - | - |
| 0.2198 | 3400 | 0.0727 | - | - | - |
| 0.2263 | 3500 | 0.0726 | - | - | - |
| 0.2327 | 3600 | 0.071 | - | - | - |
| 0.2392 | 3700 | 0.0713 | - | - | - |
| 0.2457 | 3800 | 0.0708 | - | - | - |
| 0.2521 | 3900 | 0.0704 | - | - | - |
| 0.2586 | 4000 | 0.0703 | - | - | - |
| 0.2650 | 4100 | 0.0704 | - | - | - |
| 0.2715 | 4200 | 0.0695 | - | - | - |
| 0.2780 | 4300 | 0.068 | - | - | - |
| 0.2844 | 4400 | 0.0681 | - | - | - |
| 0.2909 | 4500 | 0.0683 | - | - | - |
| 0.2974 | 4600 | 0.0674 | - | - | - |
| 0.3038 | 4700 | 0.0683 | - | - | - |
| 0.3103 | 4800 | 0.0674 | - | - | - |
| 0.3168 | 4900 | 0.0674 | - | - | - |
| 0.3232 | 5000 | 0.0666 | - | - | - |
| 0.3297 | 5100 | 0.0677 | - | - | - |
| 0.3362 | 5200 | 0.066 | - | - | - |
| 0.3426 | 5300 | 0.0655 | - | - | - |
| 0.3491 | 5400 | 0.0658 | - | - | - |
| 0.3555 | 5500 | 0.0658 | - | - | - |
| 0.3620 | 5600 | 0.0646 | - | - | - |
| 0.3685 | 5700 | 0.0638 | - | - | - |
| 0.3749 | 5800 | 0.065 | - | - | - |
| 0.3814 | 5900 | 0.0648 | - | - | - |
| 0.3879 | 6000 | 0.0636 | - | - | - |
| 0.3943 | 6100 | 0.0637 | - | - | - |
| 0.4008 | 6200 | 0.0636 | - | - | - |
| 0.4073 | 6300 | 0.0633 | - | - | - |
| 0.4137 | 6400 | 0.0629 | - | - | - |
| 0.4202 | 6500 | 0.0638 | - | - | - |
| 0.4267 | 6600 | 0.0625 | - | - | - |
| 0.4331 | 6700 | 0.0615 | - | - | - |
| 0.4396 | 6800 | 0.062 | - | - | - |
| 0.4461 | 6900 | 0.062 | - | - | - |
| 0.4525 | 7000 | 0.0614 | - | - | - |
| 0.4590 | 7100 | 0.0622 | - | - | - |
| 0.4654 | 7200 | 0.061 | - | - | - |
| 0.4719 | 7300 | 0.06 | - | - | - |
| 0.4784 | 7400 | 0.0606 | - | - | - |
| 0.4848 | 7500 | 0.0606 | - | - | - |
| 0.4913 | 7600 | 0.0597 | - | - | - |
| 0.4978 | 7700 | 0.0598 | - | - | - |
| 0.5042 | 7800 | 0.0594 | - | - | - |
| 0.5107 | 7900 | 0.0596 | - | - | - |
| 0.5172 | 8000 | 0.0584 | - | - | - |
| 0.5236 | 8100 | 0.0589 | - | - | - |
| 0.5301 | 8200 | 0.0587 | - | - | - |
| 0.5366 | 8300 | 0.059 | - | - | - |
| 0.5430 | 8400 | 0.0592 | - | - | - |
| 0.5495 | 8500 | 0.058 | - | - | - |
| 0.5560 | 8600 | 0.0576 | - | - | - |
| 0.5624 | 8700 | 0.0577 | - | - | - |
| 0.5689 | 8800 | 0.0575 | - | - | - |
| 0.5753 | 8900 | 0.0576 | - | - | - |
| 0.5818 | 9000 | 0.0575 | - | - | - |
| 0.5883 | 9100 | 0.0567 | - | - | - |
| 0.5947 | 9200 | 0.0568 | - | - | - |
| 0.6012 | 9300 | 0.0558 | - | - | - |
| 0.6077 | 9400 | 0.0558 | - | - | - |
| 0.6141 | 9500 | 0.0563 | - | - | - |
| 0.6206 | 9600 | 0.0565 | - | - | - |
| 0.6271 | 9700 | 0.0547 | - | - | - |
| 0.6335 | 9800 | 0.0555 | - | - | - |
| 0.6400 | 9900 | 0.0551 | - | - | - |
| 0.6465 | 10000 | 0.055 | - | - | - |
| 0.6529 | 10100 | 0.0553 | - | - | - |
| 0.6594 | 10200 | 0.0548 | - | - | - |
| 0.6658 | 10300 | 0.0542 | - | - | - |
| 0.6723 | 10400 | 0.0551 | - | - | - |
| 0.6788 | 10500 | 0.0545 | - | - | - |
| 0.6852 | 10600 | 0.0545 | - | - | - |
| 0.6917 | 10700 | 0.0542 | - | - | - |
| 0.6982 | 10800 | 0.0538 | - | - | - |
| 0.7046 | 10900 | 0.0532 | - | - | - |
| 0.7111 | 11000 | 0.0534 | - | - | - |
| 0.7176 | 11100 | 0.053 | - | - | - |
| 0.7240 | 11200 | 0.0534 | - | - | - |
| 0.7305 | 11300 | 0.0532 | - | - | - |
| 0.7370 | 11400 | 0.0535 | - | - | - |
| 0.7434 | 11500 | 0.0533 | - | - | - |
| 0.7499 | 11600 | 0.0532 | - | - | - |
| 0.7564 | 11700 | 0.053 | - | - | - |
| 0.7628 | 11800 | 0.0526 | - | - | - |
| 0.7693 | 11900 | 0.0527 | - | - | - |
| 0.7757 | 12000 | 0.053 | - | - | - |
| 0.7822 | 12100 | 0.0522 | - | - | - |
| 0.7887 | 12200 | 0.0521 | - | - | - |
| 0.7951 | 12300 | 0.0524 | - | - | - |
| 0.8016 | 12400 | 0.0518 | - | - | - |
| 0.8081 | 12500 | 0.0521 | - | - | - |
| 0.8145 | 12600 | 0.0516 | - | - | - |
| 0.8210 | 12700 | 0.0517 | - | - | - |
| 0.8275 | 12800 | 0.0511 | - | - | - |
| 0.8339 | 12900 | 0.0517 | - | - | - |
| 0.8404 | 13000 | 0.0516 | - | - | - |
| 0.8469 | 13100 | 0.0516 | - | - | - |
| 0.8533 | 13200 | 0.0509 | - | - | - |
| 0.8598 | 13300 | 0.0508 | - | - | - |
| 0.8662 | 13400 | 0.0506 | - | - | - |
| 0.8727 | 13500 | 0.0507 | - | - | - |
| 0.8792 | 13600 | 0.0507 | - | - | - |
| 0.8856 | 13700 | 0.0503 | - | - | - |
| 0.8921 | 13800 | 0.0504 | - | - | - |
| 0.8986 | 13900 | 0.0506 | - | - | - |
| 0.9050 | 14000 | 0.0507 | - | - | - |
| 0.9115 | 14100 | 0.0503 | - | - | - |
| 0.9180 | 14200 | 0.0496 | - | - | - |
| 0.9244 | 14300 | 0.0498 | - | - | - |
| 0.9309 | 14400 | 0.0499 | - | - | - |
| 0.9374 | 14500 | 0.0504 | - | - | - |
| 0.9438 | 14600 | 0.0493 | - | - | - |
| 0.9503 | 14700 | 0.0495 | - | - | - |
| 0.9568 | 14800 | 0.0493 | - | - | - |
| 0.9632 | 14900 | 0.0494 | - | - | - |
| 0.9697 | 15000 | 0.0495 | - | - | - |
| 0.9761 | 15100 | 0.0496 | - | - | - |
| 0.9826 | 15200 | 0.0486 | - | - | - |
| 0.9891 | 15300 | 0.0491 | - | - | - |
| 0.9955 | 15400 | 0.0485 | - | - | - |
| 1.0 | 15469 | - | 0.0479 | -4.9594 | 0.9693 |
| 1.0020 | 15500 | 0.0487 | - | - | - |
| 1.0085 | 15600 | 0.0488 | - | - | - |
| 1.0149 | 15700 | 0.0482 | - | - | - |
| 1.0214 | 15800 | 0.0486 | - | - | - |
| 1.0279 | 15900 | 0.0487 | - | - | - |
| 1.0343 | 16000 | 0.0487 | - | - | - |
| 1.0408 | 16100 | 0.0484 | - | - | - |
| 1.0473 | 16200 | 0.0478 | - | - | - |
| 1.0537 | 16300 | 0.0478 | - | - | - |
| 1.0602 | 16400 | 0.048 | - | - | - |
| 1.0666 | 16500 | 0.048 | - | - | - |
| 1.0731 | 16600 | 0.048 | - | - | - |
| 1.0796 | 16700 | 0.0478 | - | - | - |
| 1.0860 | 16800 | 0.0478 | - | - | - |
| 1.0925 | 16900 | 0.0478 | - | - | - |
| 1.0990 | 17000 | 0.0472 | - | - | - |
| 1.1054 | 17100 | 0.048 | - | - | - |
| 1.1119 | 17200 | 0.047 | - | - | - |
| 1.1184 | 17300 | 0.0477 | - | - | - |
| 1.1248 | 17400 | 0.0476 | - | - | - |
| 1.1313 | 17500 | 0.0473 | - | - | - |
| 1.1378 | 17600 | 0.0474 | - | - | - |
| 1.1442 | 17700 | 0.0472 | - | - | - |
| 1.1507 | 17800 | 0.0473 | - | - | - |
| 1.1572 | 17900 | 0.0468 | - | - | - |
| 1.1636 | 18000 | 0.047 | - | - | - |
| 1.1701 | 18100 | 0.0471 | - | - | - |
| 1.1765 | 18200 | 0.0467 | - | - | - |
| 1.1830 | 18300 | 0.0464 | - | - | - |
| 1.1895 | 18400 | 0.0463 | - | - | - |
| 1.1959 | 18500 | 0.047 | - | - | - |
| 1.2024 | 18600 | 0.0463 | - | - | - |
| 1.2089 | 18700 | 0.0466 | - | - | - |
| 1.2153 | 18800 | 0.0458 | - | - | - |
| 1.2218 | 18900 | 0.0465 | - | - | - |
| 1.2283 | 19000 | 0.0466 | - | - | - |
| 1.2347 | 19100 | 0.0459 | - | - | - |
| 1.2412 | 19200 | 0.0464 | - | - | - |
| 1.2477 | 19300 | 0.0457 | - | - | - |
| 1.2541 | 19400 | 0.0459 | - | - | - |
| 1.2606 | 19500 | 0.0463 | - | - | - |
| 1.2671 | 19600 | 0.0458 | - | - | - |
| 1.2735 | 19700 | 0.0463 | - | - | - |
| 1.2800 | 19800 | 0.0449 | - | - | - |
| 1.2864 | 19900 | 0.0455 | - | - | - |
| 1.2929 | 20000 | 0.0457 | - | - | - |
| 1.2994 | 20100 | 0.0455 | - | - | - |
| 1.3058 | 20200 | 0.0456 | - | - | - |
| 1.3123 | 20300 | 0.0453 | - | - | - |
| 1.3188 | 20400 | 0.0453 | - | - | - |
| 1.3252 | 20500 | 0.0454 | - | - | - |
| 1.3317 | 20600 | 0.0458 | - | - | - |
| 1.3382 | 20700 | 0.0449 | - | - | - |
| 1.3446 | 20800 | 0.0449 | - | - | - |
| 1.3511 | 20900 | 0.0454 | - | - | - |
| 1.3576 | 21000 | 0.0448 | - | - | - |
| 1.3640 | 21100 | 0.0445 | - | - | - |
| 1.3705 | 21200 | 0.0445 | - | - | - |
| 1.3769 | 21300 | 0.045 | - | - | - |
| 1.3834 | 21400 | 0.0448 | - | - | - |
| 1.3899 | 21500 | 0.0444 | - | - | - |
| 1.3963 | 21600 | 0.0446 | - | - | - |
| 1.4028 | 21700 | 0.0446 | - | - | - |
| 1.4093 | 21800 | 0.0444 | - | - | - |
| 1.4157 | 21900 | 0.0449 | - | - | - |
| 1.4222 | 22000 | 0.0447 | - | - | - |
| 1.4287 | 22100 | 0.044 | - | - | - |
| 1.4351 | 22200 | 0.0444 | - | - | - |
| 1.4416 | 22300 | 0.044 | - | - | - |
| 1.4481 | 22400 | 0.0443 | - | - | - |
| 1.4545 | 22500 | 0.0443 | - | - | - |
| 1.4610 | 22600 | 0.0445 | - | - | - |
| 1.4675 | 22700 | 0.0436 | - | - | - |
| 1.4739 | 22800 | 0.0438 | - | - | - |
| 1.4804 | 22900 | 0.0441 | - | - | - |
| 1.4868 | 23000 | 0.0437 | - | - | - |
| 1.4933 | 23100 | 0.0434 | - | - | - |
| 1.4998 | 23200 | 0.0437 | - | - | - |
| 1.5062 | 23300 | 0.0435 | - | - | - |
| 1.5127 | 23400 | 0.0437 | - | - | - |
| 1.5192 | 23500 | 0.043 | - | - | - |
| 1.5256 | 23600 | 0.0434 | - | - | - |
| 1.5321 | 23700 | 0.0436 | - | - | - |
| 1.5386 | 23800 | 0.0439 | - | - | - |
| 1.5450 | 23900 | 0.0438 | - | - | - |
| 1.5515 | 24000 | 0.0433 | - | - | - |
| 1.5580 | 24100 | 0.0429 | - | - | - |
| 1.5644 | 24200 | 0.0433 | - | - | - |
| 1.5709 | 24300 | 0.0428 | - | - | - |
| 1.5773 | 24400 | 0.0434 | - | - | - |
| 1.5838 | 24500 | 0.0432 | - | - | - |
| 1.5903 | 24600 | 0.0433 | - | - | - |
| 1.5967 | 24700 | 0.0426 | - | - | - |
| 1.6032 | 24800 | 0.0426 | - | - | - |
| 1.6097 | 24900 | 0.0425 | - | - | - |
| 1.6161 | 25000 | 0.0432 | - | - | - |
| 1.6226 | 25100 | 0.043 | - | - | - |
| 1.6291 | 25200 | 0.042 | - | - | - |
| 1.6355 | 25300 | 0.0427 | - | - | - |
| 1.6420 | 25400 | 0.0425 | - | - | - |
| 1.6485 | 25500 | 0.0422 | - | - | - |
| 1.6549 | 25600 | 0.0428 | - | - | - |
| 1.6614 | 25700 | 0.0423 | - | - | - |
| 1.6679 | 25800 | 0.0422 | - | - | - |
| 1.6743 | 25900 | 0.0425 | - | - | - |
| 1.6808 | 26000 | 0.0424 | - | - | - |
| 1.6872 | 26100 | 0.0426 | - | - | - |
| 1.6937 | 26200 | 0.0422 | - | - | - |
| 1.7002 | 26300 | 0.0419 | - | - | - |
| 1.7066 | 26400 | 0.0416 | - | - | - |
| 1.7131 | 26500 | 0.0421 | - | - | - |
| 1.7196 | 26600 | 0.0416 | - | - | - |
| 1.7260 | 26700 | 0.0422 | - | - | - |
| 1.7325 | 26800 | 0.0418 | - | - | - |
| 1.7390 | 26900 | 0.0425 | - | - | - |
| 1.7454 | 27000 | 0.0421 | - | - | - |
| 1.7519 | 27100 | 0.0421 | - | - | - |
| 1.7584 | 27200 | 0.0418 | - | - | - |
| 1.7648 | 27300 | 0.042 | - | - | - |
| 1.7713 | 27400 | 0.0419 | - | - | - |
| 1.7777 | 27500 | 0.0423 | - | - | - |
| 1.7842 | 27600 | 0.0415 | - | - | - |
| 1.7907 | 27700 | 0.0413 | - | - | - |
| 1.7971 | 27800 | 0.0423 | - | - | - |
| 1.8036 | 27900 | 0.0413 | - | - | - |
| 1.8101 | 28000 | 0.0414 | - | - | - |
| 1.8165 | 28100 | 0.0418 | - | - | - |
| 1.8230 | 28200 | 0.0414 | - | - | - |
| 1.8295 | 28300 | 0.0411 | - | - | - |
| 1.8359 | 28400 | 0.0418 | - | - | - |
| 1.8424 | 28500 | 0.0416 | - | - | - |
| 1.8489 | 28600 | 0.0417 | - | - | - |
| 1.8553 | 28700 | 0.041 | - | - | - |
| 1.8618 | 28800 | 0.0413 | - | - | - |
| 1.8683 | 28900 | 0.0409 | - | - | - |
| 1.8747 | 29000 | 0.0413 | - | - | - |
| 1.8812 | 29100 | 0.0413 | - | - | - |
| 1.8876 | 29200 | 0.0411 | - | - | - |
| 1.8941 | 29300 | 0.0408 | - | - | - |
| 1.9006 | 29400 | 0.0415 | - | - | - |
| 1.9070 | 29500 | 0.0415 | - | - | - |
| 1.9135 | 29600 | 0.0408 | - | - | - |
| 1.9200 | 29700 | 0.0407 | - | - | - |
| 1.9264 | 29800 | 0.0409 | - | - | - |
| 1.9329 | 29900 | 0.0414 | - | - | - |
| 1.9394 | 30000 | 0.0409 | - | - | - |
| 1.9458 | 30100 | 0.0407 | - | - | - |
| 1.9523 | 30200 | 0.0404 | - | - | - |
| 1.9588 | 30300 | 0.0408 | - | - | - |
| 1.9652 | 30400 | 0.0409 | - | - | - |
| 1.9717 | 30500 | 0.0409 | - | - | - |
| 1.9781 | 30600 | 0.0408 | - | - | - |
| 1.9846 | 30700 | 0.0403 | - | - | - |
| 1.9911 | 30800 | 0.0403 | - | - | - |
| 1.9975 | 30900 | 0.0405 | - | - | - |
| 2.0 | 30938 | - | 0.0394 | -4.1528 | 0.9835 |
| 2.0040 | 31000 | 0.0407 | - | - | - |
| 2.0105 | 31100 | 0.0403 | - | - | - |
| 2.0169 | 31200 | 0.0401 | - | - | - |
| 2.0234 | 31300 | 0.0404 | - | - | - |
| 2.0299 | 31400 | 0.0406 | - | - | - |
| 2.0363 | 31500 | 0.0408 | - | - | - |
| 2.0428 | 31600 | 0.0402 | - | - | - |
| 2.0493 | 31700 | 0.0402 | - | - | - |
| 2.0557 | 31800 | 0.0398 | - | - | - |
| 2.0622 | 31900 | 0.0403 | - | - | - |
| 2.0687 | 32000 | 0.0401 | - | - | - |
| 2.0751 | 32100 | 0.0405 | - | - | - |
| 2.0816 | 32200 | 0.0401 | - | - | - |
| 2.0880 | 32300 | 0.04 | - | - | - |
| 2.0945 | 32400 | 0.0399 | - | - | - |
| 2.1010 | 32500 | 0.0398 | - | - | - |
| 2.1074 | 32600 | 0.0406 | - | - | - |
| 2.1139 | 32700 | 0.0397 | - | - | - |
| 2.1204 | 32800 | 0.0403 | - | - | - |
| 2.1268 | 32900 | 0.0399 | - | - | - |
| 2.1333 | 33000 | 0.0401 | - | - | - |
| 2.1398 | 33100 | 0.0401 | - | - | - |
| 2.1462 | 33200 | 0.0403 | - | - | - |
| 2.1527 | 33300 | 0.0399 | - | - | - |
| 2.1592 | 33400 | 0.0398 | - | - | - |
| 2.1656 | 33500 | 0.0399 | - | - | - |
| 2.1721 | 33600 | 0.0398 | - | - | - |
| 2.1786 | 33700 | 0.0395 | - | - | - |
| 2.1850 | 33800 | 0.0395 | - | - | - |
| 2.1915 | 33900 | 0.0396 | - | - | - |
| 2.1979 | 34000 | 0.0399 | - | - | - |
| 2.2044 | 34100 | 0.0398 | - | - | - |
| 2.2109 | 34200 | 0.0393 | - | - | - |
| 2.2173 | 34300 | 0.0393 | - | - | - |
| 2.2238 | 34400 | 0.0399 | - | - | - |
| 2.2303 | 34500 | 0.0393 | - | - | - |
| 2.2367 | 34600 | 0.0398 | - | - | - |
| 2.2432 | 34700 | 0.0394 | - | - | - |
| 2.2497 | 34800 | 0.0392 | - | - | - |
| 2.2561 | 34900 | 0.0397 | - | - | - |
| 2.2626 | 35000 | 0.0399 | - | - | - |
| 2.2691 | 35100 | 0.0393 | - | - | - |
| 2.2755 | 35200 | 0.0394 | - | - | - |
| 2.2820 | 35300 | 0.0389 | - | - | - |
| 2.2884 | 35400 | 0.0392 | - | - | - |
| 2.2949 | 35500 | 0.0393 | - | - | - |
| 2.3014 | 35600 | 0.0393 | - | - | - |
| 2.3078 | 35700 | 0.0393 | - | - | - |
| 2.3143 | 35800 | 0.0391 | - | - | - |
| 2.3208 | 35900 | 0.0389 | - | - | - |
| 2.3272 | 36000 | 0.0398 | - | - | - |
| 2.3337 | 36100 | 0.0394 | - | - | - |
| 2.3402 | 36200 | 0.0389 | - | - | - |
| 2.3466 | 36300 | 0.0388 | - | - | - |
| 2.3531 | 36400 | 0.0392 | - | - | - |
| 2.3596 | 36500 | 0.0386 | - | - | - |
| 2.3660 | 36600 | 0.039 | - | - | - |
| 2.3725 | 36700 | 0.0387 | - | - | - |
| 2.3790 | 36800 | 0.0391 | - | - | - |
| 2.3854 | 36900 | 0.0389 | - | - | - |
| 2.3919 | 37000 | 0.0389 | - | - | - |
| 2.3983 | 37100 | 0.0387 | - | - | - |
| 2.4048 | 37200 | 0.0388 | - | - | - |
| 2.4113 | 37300 | 0.0387 | - | - | - |
| 2.4177 | 37400 | 0.0391 | - | - | - |
| 2.4242 | 37500 | 0.039 | - | - | - |
| 2.4307 | 37600 | 0.0384 | - | - | - |
| 2.4371 | 37700 | 0.0388 | - | - | - |
| 2.4436 | 37800 | 0.0385 | - | - | - |
| 2.4501 | 37900 | 0.0388 | - | - | - |
| 2.4565 | 38000 | 0.039 | - | - | - |
| 2.4630 | 38100 | 0.0387 | - | - | - |
| 2.4695 | 38200 | 0.0382 | - | - | - |
| 2.4759 | 38300 | 0.0384 | - | - | - |
| 2.4824 | 38400 | 0.0388 | - | - | - |
| 2.4888 | 38500 | 0.0381 | - | - | - |
| 2.4953 | 38600 | 0.0384 | - | - | - |
| 2.5018 | 38700 | 0.0384 | - | - | - |
| 2.5082 | 38800 | 0.0383 | - | - | - |
| 2.5147 | 38900 | 0.0382 | - | - | - |
| 2.5212 | 39000 | 0.0381 | - | - | - |
| 2.5276 | 39100 | 0.0382 | - | - | - |
| 2.5341 | 39200 | 0.0384 | - | - | - |
| 2.5406 | 39300 | 0.0387 | - | - | - |
| 2.5470 | 39400 | 0.0384 | - | - | - |
| 2.5535 | 39500 | 0.0381 | - | - | - |
| 2.5600 | 39600 | 0.038 | - | - | - |
| 2.5664 | 39700 | 0.0384 | - | - | - |
| 2.5729 | 39800 | 0.0379 | - | - | - |
| 2.5794 | 39900 | 0.0385 | - | - | - |
| 2.5858 | 40000 | 0.0381 | - | - | - |
| 2.5923 | 40100 | 0.0382 | - | - | - |
| 2.5987 | 40200 | 0.0377 | - | - | - |
| 2.6052 | 40300 | 0.0375 | - | - | - |
| 2.6117 | 40400 | 0.038 | - | - | - |
| 2.6181 | 40500 | 0.0384 | - | - | - |
| 2.6246 | 40600 | 0.0378 | - | - | - |
| 2.6311 | 40700 | 0.0379 | - | - | - |
| 2.6375 | 40800 | 0.0376 | - | - | - |
| 2.6440 | 40900 | 0.0378 | - | - | - |
| 2.6505 | 41000 | 0.0376 | - | - | - |
| 2.6569 | 41100 | 0.0381 | - | - | - |
| 2.6634 | 41200 | 0.0374 | - | - | - |
| 2.6699 | 41300 | 0.0377 | - | - | - |
| 2.6763 | 41400 | 0.038 | - | - | - |
| 2.6828 | 41500 | 0.0377 | - | - | - |
| 2.6892 | 41600 | 0.0379 | - | - | - |
| 2.6957 | 41700 | 0.0377 | - | - | - |
| 2.7022 | 41800 | 0.0373 | - | - | - |
| 2.7086 | 41900 | 0.0374 | - | - | - |
| 2.7151 | 42000 | 0.0373 | - | - | - |
| 2.7216 | 42100 | 0.0374 | - | - | - |
| 2.7280 | 42200 | 0.0375 | - | - | - |
| 2.7345 | 42300 | 0.0375 | - | - | - |
| 2.7410 | 42400 | 0.0379 | - | - | - |
| 2.7474 | 42500 | 0.0379 | - | - | - |
| 2.7539 | 42600 | 0.0378 | - | - | - |
| 2.7604 | 42700 | 0.0375 | - | - | - |
| 2.7668 | 42800 | 0.0375 | - | - | - |
| 2.7733 | 42900 | 0.0377 | - | - | - |
| 2.7798 | 43000 | 0.0378 | - | - | - |
| 2.7862 | 43100 | 0.0372 | - | - | - |
| 2.7927 | 43200 | 0.0374 | - | - | - |
| 2.7991 | 43300 | 0.0376 | - | - | - |
| 2.8056 | 43400 | 0.0374 | - | - | - |
| 2.8121 | 43500 | 0.0371 | - | - | - |
| 2.8185 | 43600 | 0.0377 | - | - | - |
| 2.8250 | 43700 | 0.0368 | - | - | - |
| 2.8315 | 43800 | 0.0376 | - | - | - |
| 2.8379 | 43900 | 0.0374 | - | - | - |
| 2.8444 | 44000 | 0.0378 | - | - | - |
| 2.8509 | 44100 | 0.0375 | - | - | - |
| 2.8573 | 44200 | 0.0371 | - | - | - |
| 2.8638 | 44300 | 0.037 | - | - | - |
| 2.8703 | 44400 | 0.0371 | - | - | - |
| 2.8767 | 44500 | 0.0374 | - | - | - |
| 2.8832 | 44600 | 0.037 | - | - | - |
| 2.8897 | 44700 | 0.0374 | - | - | - |
| 2.8961 | 44800 | 0.0368 | - | - | - |
| 2.9026 | 44900 | 0.0377 | - | - | - |
| 2.9090 | 45000 | 0.0375 | - | - | - |
| 2.9155 | 45100 | 0.0367 | - | - | - |
| 2.9220 | 45200 | 0.0368 | - | - | - |
| 2.9284 | 45300 | 0.0372 | - | - | - |
| 2.9349 | 45400 | 0.0374 | - | - | - |
| 2.9414 | 45500 | 0.0367 | - | - | - |
| 2.9478 | 45600 | 0.037 | - | - | - |
| 2.9543 | 45700 | 0.0368 | - | - | - |
| 2.9608 | 45800 | 0.0367 | - | - | - |
| 2.9672 | 45900 | 0.0372 | - | - | - |
| 2.9737 | 46000 | 0.0375 | - | - | - |
| 2.9802 | 46100 | 0.0368 | - | - | - |
| 2.9866 | 46200 | 0.0368 | - | - | - |
| 2.9931 | 46300 | 0.0367 | - | - | - |
| 2.9995 | 46400 | 0.0366 | - | - | - |
| 3.0 | 46407 | - | 0.0357 | -3.7998 | 0.9869 |
| 3.0060 | 46500 | 0.0372 | - | - | - |
| 3.0125 | 46600 | 0.0365 | - | - | - |
| 3.0189 | 46700 | 0.0369 | - | - | - |
| 3.0254 | 46800 | 0.0368 | - | - | - |
| 3.0319 | 46900 | 0.037 | - | - | - |
| 3.0383 | 47000 | 0.037 | - | - | - |
| 3.0448 | 47100 | 0.0367 | - | - | - |
| 3.0513 | 47200 | 0.0364 | - | - | - |
| 3.0577 | 47300 | 0.0366 | - | - | - |
| 3.0642 | 47400 | 0.0366 | - | - | - |
| 3.0707 | 47500 | 0.0371 | - | - | - |
| 3.0771 | 47600 | 0.0367 | - | - | - |
| 3.0836 | 47700 | 0.0368 | - | - | - |
| 3.0901 | 47800 | 0.0366 | - | - | - |
| 3.0965 | 47900 | 0.0362 | - | - | - |
| 3.1030 | 48000 | 0.0368 | - | - | - |
| 3.1094 | 48100 | 0.0366 | - | - | - |
| 3.1159 | 48200 | 0.0367 | - | - | - |
| 3.1224 | 48300 | 0.0369 | - | - | - |
| 3.1288 | 48400 | 0.0366 | - | - | - |
| 3.1353 | 48500 | 0.0366 | - | - | - |
| 3.1418 | 48600 | 0.0367 | - | - | - |
| 3.1482 | 48700 | 0.037 | - | - | - |
| 3.1547 | 48800 | 0.0367 | - | - | - |
| 3.1612 | 48900 | 0.0362 | - | - | - |
| 3.1676 | 49000 | 0.0367 | - | - | - |
| 3.1741 | 49100 | 0.0365 | - | - | - |
| 3.1806 | 49200 | 0.0363 | - | - | - |
| 3.1870 | 49300 | 0.036 | - | - | - |
| 3.1935 | 49400 | 0.0366 | - | - | - |
| 3.1999 | 49500 | 0.0366 | - | - | - |
| 3.2064 | 49600 | 0.0366 | - | - | - |
| 3.2129 | 49700 | 0.0361 | - | - | - |
| 3.2193 | 49800 | 0.0365 | - | - | - |
| 3.2258 | 49900 | 0.0365 | - | - | - |
| 3.2323 | 50000 | 0.0361 | - | - | - |
| 3.2387 | 50100 | 0.0365 | - | - | - |
| 3.2452 | 50200 | 0.0363 | - | - | - |
| 3.2517 | 50300 | 0.0362 | - | - | - |
| 3.2581 | 50400 | 0.0366 | - | - | - |
| 3.2646 | 50500 | 0.0366 | - | - | - |
| 3.2711 | 50600 | 0.0367 | - | - | - |
| 3.2775 | 50700 | 0.0361 | - | - | - |
| 3.2840 | 50800 | 0.0359 | - | - | - |
| 3.2905 | 50900 | 0.0363 | - | - | - |
| 3.2969 | 51000 | 0.0361 | - | - | - |
| 3.3034 | 51100 | 0.0364 | - | - | - |
| 3.3098 | 51200 | 0.0363 | - | - | - |
| 3.3163 | 51300 | 0.0362 | - | - | - |
| 3.3228 | 51400 | 0.0359 | - | - | - |
| 3.3292 | 51500 | 0.0368 | - | - | - |
| 3.3357 | 51600 | 0.0361 | - | - | - |
| 3.3422 | 51700 | 0.0359 | - | - | - |
| 3.3486 | 51800 | 0.0362 | - | - | - |
| 3.3551 | 51900 | 0.0363 | - | - | - |
| 3.3616 | 52000 | 0.0357 | - | - | - |
| 3.3680 | 52100 | 0.0358 | - | - | - |
| 3.3745 | 52200 | 0.036 | - | - | - |
| 3.3810 | 52300 | 0.0365 | - | - | - |
| 3.3874 | 52400 | 0.0359 | - | - | - |
| 3.3939 | 52500 | 0.0359 | - | - | - |
| 3.4003 | 52600 | 0.0362 | - | - | - |
| 3.4068 | 52700 | 0.0358 | - | - | - |
| 3.4133 | 52800 | 0.036 | - | - | - |
| 3.4197 | 52900 | 0.0366 | - | - | - |
| 3.4262 | 53000 | 0.036 | - | - | - |
| 3.4327 | 53100 | 0.0357 | - | - | - |
| 3.4391 | 53200 | 0.036 | - | - | - |
| 3.4456 | 53300 | 0.036 | - | - | - |
| 3.4521 | 53400 | 0.036 | - | - | - |
| 3.4585 | 53500 | 0.0364 | - | - | - |
| 3.4650 | 53600 | 0.0359 | - | - | - |
| 3.4715 | 53700 | 0.0354 | - | - | - |
| 3.4779 | 53800 | 0.0359 | - | - | - |
| 3.4844 | 53900 | 0.036 | - | - | - |
| 3.4909 | 54000 | 0.0355 | - | - | - |
| 3.4973 | 54100 | 0.0358 | - | - | - |
| 3.5038 | 54200 | 0.0355 | - | - | - |
| 3.5102 | 54300 | 0.036 | - | - | - |
| 3.5167 | 54400 | 0.0354 | - | - | - |
| 3.5232 | 54500 | 0.0357 | - | - | - |
| 3.5296 | 54600 | 0.0356 | - | - | - |
| 3.5361 | 54700 | 0.036 | - | - | - |
| 3.5426 | 54800 | 0.036 | - | - | - |
| 3.5490 | 54900 | 0.0358 | - | - | - |
| 3.5555 | 55000 | 0.0356 | - | - | - |
| 3.5620 | 55100 | 0.0357 | - | - | - |
| 3.5684 | 55200 | 0.0356 | - | - | - |
| 3.5749 | 55300 | 0.0358 | - | - | - |
| 3.5814 | 55400 | 0.036 | - | - | - |
| 3.5878 | 55500 | 0.0356 | - | - | - |
| 3.5943 | 55600 | 0.0358 | - | - | - |
| 3.6007 | 55700 | 0.0351 | - | - | - |
| 3.6072 | 55800 | 0.0352 | - | - | - |
| 3.6137 | 55900 | 0.0357 | - | - | - |
| 3.6201 | 56000 | 0.0359 | - | - | - |
| 3.6266 | 56100 | 0.035 | - | - | - |
| 3.6331 | 56200 | 0.0357 | - | - | - |
| 3.6395 | 56300 | 0.0354 | - | - | - |
| 3.6460 | 56400 | 0.0352 | - | - | - |
| 3.6525 | 56500 | 0.0356 | - | - | - |
| 3.6589 | 56600 | 0.0356 | - | - | - |
| 3.6654 | 56700 | 0.0349 | - | - | - |
| 3.6719 | 56800 | 0.0358 | - | - | - |
| 3.6783 | 56900 | 0.0355 | - | - | - |
| 3.6848 | 57000 | 0.0353 | - | - | - |
| 3.6913 | 57100 | 0.0355 | - | - | - |
| 3.6977 | 57200 | 0.0353 | - | - | - |
| 3.7042 | 57300 | 0.035 | - | - | - |
| 3.7106 | 57400 | 0.0351 | - | - | - |
| 3.7171 | 57500 | 0.035 | - | - | - |
| 3.7236 | 57600 | 0.0353 | - | - | - |
| 3.7300 | 57700 | 0.0353 | - | - | - |
| 3.7365 | 57800 | 0.0356 | - | - | - |
| 3.7430 | 57900 | 0.0356 | - | - | - |
| 3.7494 | 58000 | 0.0355 | - | - | - |
| 3.7559 | 58100 | 0.0355 | - | - | - |
| 3.7624 | 58200 | 0.0354 | - | - | - |
| 3.7688 | 58300 | 0.0353 | - | - | - |
| 3.7753 | 58400 | 0.0357 | - | - | - |
| 3.7818 | 58500 | 0.0353 | - | - | - |
| 3.7882 | 58600 | 0.035 | - | - | - |
| 3.7947 | 58700 | 0.0355 | - | - | - |
| 3.8012 | 58800 | 0.035 | - | - | - |
| 3.8076 | 58900 | 0.0355 | - | - | - |
| 3.8141 | 59000 | 0.0351 | - | - | - |
| 3.8205 | 59100 | 0.0353 | - | - | - |
| 3.8270 | 59200 | 0.0349 | - | - | - |
| 3.8335 | 59300 | 0.0355 | - | - | - |
| 3.8399 | 59400 | 0.0353 | - | - | - |
| 3.8464 | 59500 | 0.0357 | - | - | - |
| 3.8529 | 59600 | 0.0351 | - | - | - |
| 3.8593 | 59700 | 0.0351 | - | - | - |
| 3.8658 | 59800 | 0.0352 | - | - | - |
| 3.8723 | 59900 | 0.035 | - | - | - |
| 3.8787 | 60000 | 0.0353 | - | - | - |
| 3.8852 | 60100 | 0.0351 | - | - | - |
| 3.8917 | 60200 | 0.0352 | - | - | - |
| 3.8981 | 60300 | 0.0351 | - | - | - |
| 3.9046 | 60400 | 0.0356 | - | - | - |
| 3.9110 | 60500 | 0.0352 | - | - | - |
| 3.9175 | 60600 | 0.0347 | - | - | - |
| 3.9240 | 60700 | 0.035 | - | - | - |
| 3.9304 | 60800 | 0.0352 | - | - | - |
| 3.9369 | 60900 | 0.0356 | - | - | - |
| 3.9434 | 61000 | 0.0346 | - | - | - |
| 3.9498 | 61100 | 0.0352 | - | - | - |
| 3.9563 | 61200 | 0.0349 | - | - | - |
| 3.9628 | 61300 | 0.0349 | - | - | - |
| 3.9692 | 61400 | 0.0354 | - | - | - |
| 3.9757 | 61500 | 0.0354 | - | - | - |
| 3.9822 | 61600 | 0.0348 | - | - | - |
| 3.9886 | 61700 | 0.0349 | - | - | - |
| 3.9951 | 61800 | 0.0347 | - | - | - |
| 4.0 | 61876 | - | 0.0339 | -3.6284 | 0.9876 |
| 4.0016 | 61900 | 0.0351 | - | - | - |
| 4.0080 | 62000 | 0.035 | - | - | - |
| 4.0145 | 62100 | 0.0348 | - | - | - |
| 4.0209 | 62200 | 0.0349 | - | - | - |
| 4.0274 | 62300 | 0.0352 | - | - | - |
| 4.0339 | 62400 | 0.0351 | - | - | - |
| 4.0403 | 62500 | 0.0352 | - | - | - |
| 4.0468 | 62600 | 0.0347 | - | - | - |
| 4.0533 | 62700 | 0.0347 | - | - | - |
| 4.0597 | 62800 | 0.0348 | - | - | - |
| 4.0662 | 62900 | 0.035 | - | - | - |
| 4.0727 | 63000 | 0.035 | - | - | - |
| 4.0791 | 63100 | 0.0349 | - | - | - |
| 4.0856 | 63200 | 0.035 | - | - | - |
| 4.0921 | 63300 | 0.0349 | - | - | - |
| 4.0985 | 63400 | 0.0346 | - | - | - |
| 4.1050 | 63500 | 0.035 | - | - | - |
| 4.1114 | 63600 | 0.0347 | - | - | - |
| 4.1179 | 63700 | 0.0351 | - | - | - |
| 4.1244 | 63800 | 0.0351 | - | - | - |
| 4.1308 | 63900 | 0.035 | - | - | - |
| 4.1373 | 64000 | 0.0349 | - | - | - |
| 4.1438 | 64100 | 0.0352 | - | - | - |
| 4.1502 | 64200 | 0.0351 | - | - | - |
| 4.1567 | 64300 | 0.0348 | - | - | - |
| 4.1632 | 64400 | 0.0347 | - | - | - |
| 4.1696 | 64500 | 0.0352 | - | - | - |
| 4.1761 | 64600 | 0.0346 | - | - | - |
| 4.1826 | 64700 | 0.0345 | - | - | - |
| 4.1890 | 64800 | 0.0346 | - | - | - |
| 4.1955 | 64900 | 0.0351 | - | - | - |
| 4.2020 | 65000 | 0.0348 | - | - | - |
| 4.2084 | 65100 | 0.035 | - | - | - |
| 4.2149 | 65200 | 0.0345 | - | - | - |
| 4.2213 | 65300 | 0.0349 | - | - | - |
| 4.2278 | 65400 | 0.0351 | - | - | - |
| 4.2343 | 65500 | 0.0345 | - | - | - |
| 4.2407 | 65600 | 0.035 | - | - | - |
| 4.2472 | 65700 | 0.0346 | - | - | - |
| 4.2537 | 65800 | 0.0347 | - | - | - |
| 4.2601 | 65900 | 0.0351 | - | - | - |
| 4.2666 | 66000 | 0.0347 | - | - | - |
| 4.2731 | 66100 | 0.0354 | - | - | - |
| 4.2795 | 66200 | 0.0342 | - | - | - |
| 4.2860 | 66300 | 0.0345 | - | - | - |
| 4.2925 | 66400 | 0.0349 | - | - | - |
| 4.2989 | 66500 | 0.0347 | - | - | - |
| 4.3054 | 66600 | 0.0347 | - | - | - |
| 4.3118 | 66700 | 0.0348 | - | - | - |
| 4.3183 | 66800 | 0.0347 | - | - | - |
| 4.3248 | 66900 | 0.0346 | - | - | - |
| 4.3312 | 67000 | 0.0353 | - | - | - |
| 4.3377 | 67100 | 0.0345 | - | - | - |
| 4.3442 | 67200 | 0.0343 | - | - | - |
| 4.3506 | 67300 | 0.035 | - | - | - |
| 4.3571 | 67400 | 0.0346 | - | - | - |
| 4.3636 | 67500 | 0.0343 | - | - | - |
| 4.3700 | 67600 | 0.0344 | - | - | - |
| 4.3765 | 67700 | 0.0348 | - | - | - |
| 4.3830 | 67800 | 0.0348 | - | - | - |
| 4.3894 | 67900 | 0.0345 | - | - | - |
| 4.3959 | 68000 | 0.0347 | - | - | - |
| 4.4024 | 68100 | 0.0345 | - | - | - |
| 4.4088 | 68200 | 0.0346 | - | - | - |
| 4.4153 | 68300 | 0.0349 | - | - | - |
| 4.4217 | 68400 | 0.0349 | - | - | - |
| 4.4282 | 68500 | 0.0345 | - | - | - |
| 4.4347 | 68600 | 0.0346 | - | - | - |
| 4.4411 | 68700 | 0.0345 | - | - | - |
| 4.4476 | 68800 | 0.0347 | - | - | - |
| 4.4541 | 68900 | 0.0346 | - | - | - |
| 4.4605 | 69000 | 0.035 | - | - | - |
| 4.4670 | 69100 | 0.0343 | - | - | - |
| 4.4735 | 69200 | 0.0346 | - | - | - |
| 4.4799 | 69300 | 0.0346 | - | - | - |
| 4.4864 | 69400 | 0.0346 | - | - | - |
| 4.4929 | 69500 | 0.0342 | - | - | - |
| 4.4993 | 69600 | 0.0346 | - | - | - |
| 4.5058 | 69700 | 0.0342 | - | - | - |
| 4.5123 | 69800 | 0.0348 | - | - | - |
| 4.5187 | 69900 | 0.0341 | - | - | - |
| 4.5252 | 70000 | 0.0344 | - | - | - |
| 4.5316 | 70100 | 0.0345 | - | - | - |
| 4.5381 | 70200 | 0.0348 | - | - | - |
| 4.5446 | 70300 | 0.0349 | - | - | - |
| 4.5510 | 70400 | 0.0344 | - | - | - |
| 4.5575 | 70500 | 0.0342 | - | - | - |
| 4.5640 | 70600 | 0.0346 | - | - | - |
| 4.5704 | 70700 | 0.0342 | - | - | - |
| 4.5769 | 70800 | 0.0345 | - | - | - |
| 4.5834 | 70900 | 0.0347 | - | - | - |
| 4.5898 | 71000 | 0.0345 | - | - | - |
| 4.5963 | 71100 | 0.0343 | - | - | - |
| 4.6028 | 71200 | 0.0341 | - | - | - |
| 4.6092 | 71300 | 0.0341 | - | - | - |
| 4.6157 | 71400 | 0.0347 | - | - | - |
| 4.6221 | 71500 | 0.0347 | - | - | - |
| 4.6286 | 71600 | 0.0339 | - | - | - |
| 4.6351 | 71700 | 0.0344 | - | - | - |
| 4.6415 | 71800 | 0.0342 | - | - | - |
| 4.6480 | 71900 | 0.0342 | - | - | - |
| 4.6545 | 72000 | 0.0346 | - | - | - |
| 4.6609 | 72100 | 0.0342 | - | - | - |
| 4.6674 | 72200 | 0.0341 | - | - | - |
| 4.6739 | 72300 | 0.0344 | - | - | - |
| 4.6803 | 72400 | 0.0345 | - | - | - |
| 4.6868 | 72500 | 0.0345 | - | - | - |
| 4.6933 | 72600 | 0.0342 | - | - | - |
| 4.6997 | 72700 | 0.0341 | - | - | - |
| 4.7062 | 72800 | 0.034 | - | - | - |
| 4.7127 | 72900 | 0.0343 | - | - | - |
| 4.7191 | 73000 | 0.0337 | - | - | - |
| 4.7256 | 73100 | 0.0343 | - | - | - |
| 4.7320 | 73200 | 0.0343 | - | - | - |
| 4.7385 | 73300 | 0.0346 | - | - | - |
| 4.7450 | 73400 | 0.0346 | - | - | - |
| 4.7514 | 73500 | 0.0345 | - | - | - |
| 4.7579 | 73600 | 0.0343 | - | - | - |
| 4.7644 | 73700 | 0.0344 | - | - | - |
| 4.7708 | 73800 | 0.0345 | - | - | - |
| 4.7773 | 73900 | 0.0347 | - | - | - |
| 4.7838 | 74000 | 0.034 | - | - | - |
| 4.7902 | 74100 | 0.034 | - | - | - |
| 4.7967 | 74200 | 0.0348 | - | - | - |
| 4.8032 | 74300 | 0.0338 | - | - | - |
| 4.8096 | 74400 | 0.0346 | - | - | - |
| 4.8161 | 74500 | 0.0344 | - | - | - |
| 4.8225 | 74600 | 0.0342 | - | - | - |
| 4.8290 | 74700 | 0.034 | - | - | - |
| 4.8355 | 74800 | 0.0346 | - | - | - |
| 4.8419 | 74900 | 0.0346 | - | - | - |
| 4.8484 | 75000 | 0.0346 | - | - | - |
| 4.8549 | 75100 | 0.034 | - | - | - |
| 4.8613 | 75200 | 0.0343 | - | - | - |
| 4.8678 | 75300 | 0.034 | - | - | - |
| 4.8743 | 75400 | 0.0344 | - | - | - |
| 4.8807 | 75500 | 0.0344 | - | - | - |
| 4.8872 | 75600 | 0.0342 | - | - | - |
| 4.8937 | 75700 | 0.0341 | - | - | - |
| 4.9001 | 75800 | 0.0345 | - | - | - |
| 4.9066 | 75900 | 0.0347 | - | - | - |
| 4.9131 | 76000 | 0.0341 | - | - | - |
| 4.9195 | 76100 | 0.0339 | - | - | - |
| 4.9260 | 76200 | 0.0343 | - | - | - |
| 4.9324 | 76300 | 0.0346 | - | - | - |
| 4.9389 | 76400 | 0.0344 | - | - | - |
| 4.9454 | 76500 | 0.0341 | - | - | - |
| 4.9518 | 76600 | 0.034 | - | - | - |
| 4.9583 | 76700 | 0.0342 | - | - | - |
| 4.9648 | 76800 | 0.0344 | - | - | - |
| 4.9712 | 76900 | 0.0344 | - | - | - |
| 4.9777 | 77000 | 0.0343 | - | - | - |
| 4.9842 | 77100 | 0.0341 | - | - | - |
| 4.9906 | 77200 | 0.0341 | - | - | - |
| 4.9971 | 77300 | 0.0342 | - | - | - |
| 5.0 | 77345 | - | 0.0331 | -3.5554 | 0.9877 |