---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: FacebookAI/roberta-large
widget:
- source_sentence: A man is jumping unto his filthy bed.
sentences:
- A young male is looking at a newspaper while 2 females walks past him.
- The bed is dirty.
- The man is on the moon.
- source_sentence: A carefully balanced male stands on one foot near a clean ocean
beach area.
sentences:
- A man is ouside near the beach.
- Three policemen patrol the streets on bikes
- A man is sitting on his couch.
- source_sentence: The man is wearing a blue shirt.
sentences:
- Near the trashcan the man stood and smoked
- A man in a blue shirt leans on a wall beside a road with a blue van and red car
with water in the background.
- A man in a black shirt is playing a guitar.
- source_sentence: The girls are outdoors.
sentences:
- Two girls riding on an amusement part ride.
- a guy laughs while doing laundry
- Three girls are standing together in a room, one is listening, one is writing
on a wall and the third is talking to them.
- source_sentence: A construction worker peeking out of a manhole while his coworker
sits on the sidewalk smiling.
sentences:
- A worker is looking out of a manhole.
- A man is giving a presentation.
- The workers are both inside the manhole.
datasets:
- sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on FacebookAI/roberta-large
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.8231572403502458
name: Pearson Cosine
- type: spearman_cosine
value: 0.8229407700505489
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7945069591117604
name: Pearson Cosine
- type: spearman_cosine
value: 0.7977186384958888
name: Spearman Cosine
---
# SentenceTransformer based on FacebookAI/roberta-large
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 1024-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:** [FacebookAI/roberta-large](https://huggingface.co/FacebookAI/roberta-large)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/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': 512, 'do_lower_case': False, 'architecture': 'RobertaModel'})
(1): Pooling({'word_embedding_dimension': 1024, '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 = [
'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
'A worker is looking out of a manhole.',
'The workers are both inside the manhole.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.6658, 0.1268],
# [0.6658, 1.0000, 0.2392],
# [0.1268, 0.2392, 1.0000]])
```
## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-dev` and `sts-test`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-dev | sts-test |
|:--------------------|:-----------|:-----------|
| pearson_cosine | 0.8232 | 0.7945 |
| **spearman_cosine** | **0.8229** | **0.7977** |
## Training Details
### Training Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 557,850 training samples
* Columns: anchor, positive, and negative
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 7 tokens
- mean: 10.38 tokens
- max: 45 tokens
| - min: 6 tokens
- mean: 12.8 tokens
- max: 39 tokens
| - min: 6 tokens
- mean: 13.4 tokens
- max: 50 tokens
|
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | A person is at a diner, ordering an omelette. |
| Children smiling and waving at camera | There are children present | The kids are frowning |
| A boy is jumping on skateboard in the middle of a red bridge. | The boy does a skateboarding trick. | The boy skates down the sidewalk. |
* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: anchor, positive, and negative
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 6 tokens
- mean: 18.02 tokens
- max: 66 tokens
| - min: 5 tokens
- mean: 9.81 tokens
- max: 29 tokens
| - min: 5 tokens
- mean: 10.37 tokens
- max: 29 tokens
|
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| Two women are embracing while holding to go packages. | Two woman are holding packages. | The men are fighting outside a deli. |
| Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink. | Two kids in numbered jerseys wash their hands. | Two kids in jackets walk to school. |
| A man selling donuts to a customer during a world exhibition event held in the city of Angeles | A man selling donuts to a customer. | A woman drinks her coffee in a small cafe. |
* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
1024,
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 15
- `warmup_ratio`: 0.1
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-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`: 15
- `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
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `project`: huggingface
- `trackio_space_id`: trackio
- `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
- `hub_revision`: None
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: no
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
Click to expand
| Epoch | Step | Training Loss | Validation Loss | sts-dev_spearman_cosine | sts-test_spearman_cosine |
|:-------:|:------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|
| -1 | -1 | - | - | 0.5730 | - |
| 0.0287 | 500 | 13.1307 | 3.8711 | 0.8322 | - |
| 0.0574 | 1000 | 4.9118 | 2.2331 | 0.8644 | - |
| 0.0860 | 1500 | 3.9032 | 1.8578 | 0.8656 | - |
| 0.1147 | 2000 | 3.4689 | 1.6312 | 0.8714 | - |
| 0.1434 | 2500 | 3.1647 | 1.5546 | 0.8716 | - |
| 0.1721 | 3000 | 2.9685 | 1.4834 | 0.8801 | - |
| 0.2008 | 3500 | 2.8231 | 1.3738 | 0.8712 | - |
| 0.2294 | 4000 | 2.6908 | 1.3634 | 0.8718 | - |
| 0.2581 | 4500 | 2.6483 | 1.3831 | 0.8789 | - |
| 0.2868 | 5000 | 2.5545 | 1.3328 | 0.8756 | - |
| 0.3155 | 5500 | 2.428 | 1.2995 | 0.8737 | - |
| 0.3442 | 6000 | 2.4545 | 1.2703 | 0.8752 | - |
| 0.3729 | 6500 | 2.4895 | 1.2613 | 0.8704 | - |
| 0.4015 | 7000 | 2.406 | 1.2644 | 0.8632 | - |
| 0.4302 | 7500 | 2.2968 | 1.3179 | 0.8635 | - |
| 0.4589 | 8000 | 2.2072 | 1.3236 | 0.8724 | - |
| 0.4876 | 8500 | 2.3579 | 1.3357 | 0.8599 | - |
| 0.5163 | 9000 | 2.3808 | 1.2971 | 0.8592 | - |
| 0.5449 | 9500 | 2.2616 | 1.3413 | 0.8692 | - |
| 0.5736 | 10000 | 2.199 | 1.3170 | 0.8601 | - |
| 0.6023 | 10500 | 2.2254 | 1.3450 | 0.8590 | - |
| 0.6310 | 11000 | 2.16 | 1.3072 | 0.8606 | - |
| 0.6597 | 11500 | 2.1753 | 1.3070 | 0.8662 | - |
| 0.6883 | 12000 | 2.0891 | 1.2685 | 0.8687 | - |
| 0.7170 | 12500 | 2.1434 | 1.3496 | 0.8605 | - |
| 0.7457 | 13000 | 2.133 | 1.2944 | 0.8533 | - |
| 0.7744 | 13500 | 2.0775 | 1.3831 | 0.8528 | - |
| 0.8031 | 14000 | 2.0856 | 1.3325 | 0.8558 | - |
| 0.8318 | 14500 | 2.0905 | 1.3525 | 0.8713 | - |
| 0.8604 | 15000 | 2.0856 | 1.3079 | 0.8513 | - |
| 0.8891 | 15500 | 2.1206 | 1.3687 | 0.8509 | - |
| 0.9178 | 16000 | 2.0854 | 1.3752 | 0.8429 | - |
| 0.9465 | 16500 | 2.0765 | 1.4162 | 0.8423 | - |
| 0.9752 | 17000 | 2.0011 | 1.3374 | 0.8487 | - |
| 1.0038 | 17500 | 2.1728 | 1.4115 | 0.8504 | - |
| 1.0325 | 18000 | 1.8636 | 1.6978 | 0.8451 | - |
| 1.0612 | 18500 | 2.2661 | 1.3775 | 0.8501 | - |
| 1.0899 | 19000 | 1.9163 | 1.3913 | 0.8407 | - |
| 1.1186 | 19500 | 1.8524 | 1.3511 | 0.8495 | - |
| 1.1472 | 20000 | 1.9746 | 1.4419 | 0.8480 | - |
| 1.1759 | 20500 | 1.9949 | 1.4820 | 0.8406 | - |
| 1.2046 | 21000 | 2.0087 | 1.4877 | 0.8444 | - |
| 1.2333 | 21500 | 2.0073 | 1.3913 | 0.8475 | - |
| 1.2620 | 22000 | 1.9374 | 1.5215 | 0.8539 | - |
| 1.2907 | 22500 | 1.979 | 1.5082 | 0.8585 | - |
| 1.3193 | 23000 | 1.9629 | 1.4630 | 0.8454 | - |
| 1.3480 | 23500 | 2.0761 | 1.5292 | 0.8397 | - |
| 1.3767 | 24000 | 2.0052 | 1.5718 | 0.8464 | - |
| 1.4054 | 24500 | 2.0406 | 1.4504 | 0.8462 | - |
| 1.4341 | 25000 | 1.9931 | 1.6408 | 0.8470 | - |
| 1.4627 | 25500 | 2.0963 | 1.6715 | 0.8469 | - |
| 1.4914 | 26000 | 2.0744 | 1.7317 | 0.8344 | - |
| 1.5201 | 26500 | 2.0192 | 1.7196 | 0.8301 | - |
| 1.5488 | 27000 | 2.072 | 1.8939 | 0.8272 | - |
| 1.5775 | 27500 | 2.1233 | 1.6756 | 0.8381 | - |
| 1.6061 | 28000 | 2.1309 | 1.6725 | 0.8399 | - |
| 1.6348 | 28500 | 2.1307 | 1.7151 | 0.8337 | - |
| 1.6635 | 29000 | 2.0551 | 1.6822 | 0.8348 | - |
| 1.6922 | 29500 | 2.0623 | 1.6315 | 0.8463 | - |
| 1.7209 | 30000 | 2.1413 | 1.6249 | 0.8420 | - |
| 1.7496 | 30500 | 2.0578 | 1.8499 | 0.8327 | - |
| 1.7782 | 31000 | 2.1535 | 1.6687 | 0.8427 | - |
| 1.8069 | 31500 | 2.1894 | 1.6501 | 0.8369 | - |
| 1.8356 | 32000 | 1.979 | 1.7413 | 0.8341 | - |
| 1.8643 | 32500 | 2.1038 | 1.7312 | 0.8345 | - |
| 1.8930 | 33000 | 2.0746 | 1.7323 | 0.8313 | - |
| 1.9216 | 33500 | 2.2393 | 1.9724 | 0.8354 | - |
| 1.9503 | 34000 | 2.171 | 1.7513 | 0.8395 | - |
| 1.9790 | 34500 | 2.0399 | 1.7751 | 0.8345 | - |
| 2.0077 | 35000 | 2.0002 | 1.7431 | 0.8411 | - |
| 2.0364 | 35500 | 1.6843 | 1.7284 | 0.8365 | - |
| 2.0650 | 36000 | 1.772 | 1.8173 | 0.8394 | - |
| 2.0937 | 36500 | 1.7372 | 1.8277 | 0.8381 | - |
| 2.1224 | 37000 | 1.8665 | 1.7897 | 0.8392 | - |
| 2.1511 | 37500 | 1.8157 | 1.8601 | 0.8326 | - |
| 2.1798 | 38000 | 1.8641 | 1.6849 | 0.8388 | - |
| 2.2085 | 38500 | 1.7293 | 1.6760 | 0.8388 | - |
| 2.2371 | 39000 | 1.7038 | 1.6455 | 0.8348 | - |
| 2.2658 | 39500 | 1.8139 | 1.7665 | 0.8205 | - |
| 2.2945 | 40000 | 1.7791 | 1.7799 | 0.8302 | - |
| 2.3232 | 40500 | 2.8435 | 1.9061 | 0.8196 | - |
| 2.3519 | 41000 | 1.9696 | 1.8294 | 0.8344 | - |
| 2.3805 | 41500 | 1.9685 | 1.9805 | 0.8169 | - |
| 2.4092 | 42000 | 1.7893 | 1.8200 | 0.8283 | - |
| 2.4379 | 42500 | 1.74 | 1.7132 | 0.8366 | - |
| 2.4666 | 43000 | 1.7877 | 1.7723 | 0.8433 | - |
| 2.4953 | 43500 | 1.8317 | 1.6720 | 0.8367 | - |
| 2.5239 | 44000 | 1.7922 | 1.7199 | 0.8249 | - |
| 2.5526 | 44500 | 1.7841 | 1.7628 | 0.8300 | - |
| 2.5813 | 45000 | 1.8367 | 1.8752 | 0.8328 | - |
| 2.6100 | 45500 | 1.773 | 1.8062 | 0.8367 | - |
| 2.6387 | 46000 | 1.8124 | 1.8124 | 0.8347 | - |
| 2.6674 | 46500 | 1.7595 | 1.7697 | 0.8340 | - |
| 2.6960 | 47000 | 1.7422 | 1.8300 | 0.8231 | - |
| 2.7247 | 47500 | 1.8007 | 1.7629 | 0.8303 | - |
| 2.7534 | 48000 | 1.7744 | 1.7752 | 0.8287 | - |
| 2.7821 | 48500 | 1.6891 | 1.6854 | 0.8341 | - |
| 2.8108 | 49000 | 1.7044 | 1.8094 | 0.8213 | - |
| 2.8394 | 49500 | 1.6808 | 1.6874 | 0.8243 | - |
| 2.8681 | 50000 | 1.6774 | 1.7517 | 0.8229 | - |
| 2.8968 | 50500 | 1.684 | 1.7038 | 0.8314 | - |
| 2.9255 | 51000 | 1.7204 | 1.7657 | 0.8268 | - |
| 2.9542 | 51500 | 1.6877 | 1.7660 | 0.8306 | - |
| 2.9828 | 52000 | 1.8228 | 1.7883 | 0.8241 | - |
| 3.0115 | 52500 | 1.5882 | 1.7963 | 0.8278 | - |
| 3.0402 | 53000 | 1.4159 | 1.8232 | 0.8262 | - |
| 3.0689 | 53500 | 1.4347 | 1.8152 | 0.8246 | - |
| 3.0976 | 54000 | 1.5007 | 1.8113 | 0.8216 | - |
| 3.1263 | 54500 | 1.5196 | 1.7677 | 0.8250 | - |
| 3.1549 | 55000 | 1.4994 | 1.7585 | 0.8302 | - |
| 3.1836 | 55500 | 1.5854 | 1.7113 | 0.8310 | - |
| 3.2123 | 56000 | 1.4578 | 1.8058 | 0.8238 | - |
| 3.2410 | 56500 | 1.525 | 1.7659 | 0.8255 | - |
| 3.2697 | 57000 | 1.4602 | 1.8074 | 0.8262 | - |
| 3.2983 | 57500 | 2.1095 | 1.7176 | 0.8306 | - |
| 3.3270 | 58000 | 1.4814 | 1.8732 | 0.8312 | - |
| 3.3557 | 58500 | 1.6221 | 1.7636 | 0.8297 | - |
| 3.3844 | 59000 | 1.4695 | 1.7405 | 0.8232 | - |
| 3.4131 | 59500 | 1.5805 | 1.7804 | 0.8376 | - |
| 3.4417 | 60000 | 1.4774 | 1.7737 | 0.8289 | - |
| 3.4704 | 60500 | 1.4614 | 1.7717 | 0.8369 | - |
| 3.4991 | 61000 | 1.5027 | 1.8026 | 0.8312 | - |
| 3.5278 | 61500 | 1.4788 | 1.8565 | 0.8187 | - |
| 3.5565 | 62000 | 1.5613 | 1.8170 | 0.8268 | - |
| 3.5852 | 62500 | 1.529 | 1.9040 | 0.8255 | - |
| 3.6138 | 63000 | 1.5549 | 2.0451 | 0.8263 | - |
| 3.6425 | 63500 | 1.5604 | 1.8017 | 0.8323 | - |
| 3.6712 | 64000 | 1.4462 | 1.8130 | 0.8291 | - |
| 3.6999 | 64500 | 1.5074 | 1.8368 | 0.8236 | - |
| 3.7286 | 65000 | 1.4982 | 1.8025 | 0.8251 | - |
| 3.7572 | 65500 | 1.5496 | 1.7867 | 0.8235 | - |
| 3.7859 | 66000 | 1.5688 | 1.8279 | 0.8200 | - |
| 3.8146 | 66500 | 1.4988 | 1.8225 | 0.8315 | - |
| 3.8433 | 67000 | 1.5178 | 1.7781 | 0.8211 | - |
| 3.8720 | 67500 | 1.4558 | 1.8183 | 0.8279 | - |
| 3.9006 | 68000 | 1.52 | 1.7993 | 0.8219 | - |
| 3.9293 | 68500 | 1.4339 | 1.7622 | 0.8211 | - |
| 3.9580 | 69000 | 1.4377 | 1.8122 | 0.8131 | - |
| 3.9867 | 69500 | 1.5208 | 1.8728 | 0.8200 | - |
| 4.0154 | 70000 | 1.3749 | 1.7789 | 0.8285 | - |
| 4.0441 | 70500 | 1.3293 | 1.9063 | 0.8301 | - |
| 4.0727 | 71000 | 1.2638 | 2.1195 | 0.8174 | - |
| 4.1014 | 71500 | 1.454 | 1.8508 | 0.8265 | - |
| 4.1301 | 72000 | 1.3227 | 1.8074 | 0.8169 | - |
| 4.1588 | 72500 | 1.3982 | 1.9820 | 0.8127 | - |
| 4.1875 | 73000 | 1.3168 | 1.9202 | 0.8223 | - |
| 4.2161 | 73500 | 1.2791 | 1.7751 | 0.8279 | - |
| 4.2448 | 74000 | 1.2821 | 1.7690 | 0.8259 | - |
| 4.2735 | 74500 | 1.2799 | 1.8505 | 0.8202 | - |
| 4.3022 | 75000 | 1.2453 | 1.7584 | 0.8334 | - |
| 4.3309 | 75500 | 1.2421 | 1.7923 | 0.8297 | - |
| 4.3595 | 76000 | 1.332 | 1.8744 | 0.8216 | - |
| 4.3882 | 76500 | 1.3413 | 1.8049 | 0.8292 | - |
| 4.4169 | 77000 | 1.3342 | 1.7446 | 0.8271 | - |
| 4.4456 | 77500 | 1.2565 | 1.7859 | 0.8229 | - |
| 4.4743 | 78000 | 1.2976 | 2.0875 | 0.8284 | - |
| 4.5030 | 78500 | 1.2861 | 1.8081 | 0.8260 | - |
| 4.5316 | 79000 | 1.2982 | 1.7828 | 0.8267 | - |
| 4.5603 | 79500 | 1.3014 | 1.7792 | 0.8200 | - |
| 4.5890 | 80000 | 1.2867 | 1.8072 | 0.8251 | - |
| 4.6177 | 80500 | 1.3247 | 1.7776 | 0.8256 | - |
| 4.6464 | 81000 | 1.3646 | 1.7684 | 0.8210 | - |
| 4.6750 | 81500 | 1.4309 | 1.8437 | 0.8186 | - |
| 4.7037 | 82000 | 1.3742 | 1.9158 | 0.8157 | - |
| 4.7324 | 82500 | 4.1451 | 1.8141 | 0.8249 | - |
| 4.7611 | 83000 | 1.3416 | 1.7796 | 0.8277 | - |
| 4.7898 | 83500 | 1.3342 | 1.7990 | 0.8238 | - |
| 4.8184 | 84000 | 1.3027 | 1.9050 | 0.8180 | - |
| 4.8471 | 84500 | 1.3237 | 1.7734 | 0.8227 | - |
| 4.8758 | 85000 | 1.2319 | 1.8478 | 0.8243 | - |
| 4.9045 | 85500 | 1.279 | 1.7974 | 0.8255 | - |
| 4.9332 | 86000 | 1.2646 | 1.7283 | 0.8305 | - |
| 4.9619 | 86500 | 1.1886 | 1.9569 | 0.8212 | - |
| 4.9905 | 87000 | 1.2567 | 1.7428 | 0.8295 | - |
| 5.0192 | 87500 | 1.1228 | 1.8055 | 0.8306 | - |
| 5.0479 | 88000 | 1.0618 | 1.7539 | 0.8274 | - |
| 5.0766 | 88500 | 1.0226 | 1.8684 | 0.8298 | - |
| 5.1053 | 89000 | 1.0808 | 1.7666 | 0.8208 | - |
| 5.1339 | 89500 | 1.044 | 1.7659 | 0.8211 | - |
| 5.1626 | 90000 | 1.0438 | 1.7997 | 0.8260 | - |
| 5.1913 | 90500 | 1.1137 | 1.8361 | 0.8175 | - |
| 5.2200 | 91000 | 1.0646 | 1.8627 | 0.8264 | - |
| 5.2487 | 91500 | 1.0429 | 1.8203 | 0.8254 | - |
| 5.2773 | 92000 | 1.108 | 1.7993 | 0.8255 | - |
| 5.3060 | 92500 | 1.031 | 1.8521 | 0.8239 | - |
| 5.3347 | 93000 | 1.1251 | 1.8060 | 0.8244 | - |
| 5.3634 | 93500 | 1.1004 | 1.8634 | 0.8247 | - |
| 5.3921 | 94000 | 1.1488 | 1.8411 | 0.8288 | - |
| 5.4208 | 94500 | 1.0505 | 1.7374 | 0.8253 | - |
| 5.4494 | 95000 | 1.1083 | 1.8251 | 0.8238 | - |
| 5.4781 | 95500 | 1.1011 | 1.7409 | 0.8328 | - |
| 5.5068 | 96000 | 1.1082 | 1.6637 | 0.8354 | - |
| 5.5355 | 96500 | 1.105 | 1.8248 | 0.8252 | - |
| 5.5642 | 97000 | 1.1057 | 1.7803 | 0.8261 | - |
| 5.5928 | 97500 | 1.0842 | 1.8545 | 0.8195 | - |
| 5.6215 | 98000 | 1.0576 | 1.8561 | 0.8216 | - |
| 5.6502 | 98500 | 1.0779 | 1.7923 | 0.8272 | - |
| 5.6789 | 99000 | 1.0408 | 1.7872 | 0.8314 | - |
| 5.7076 | 99500 | 1.1003 | 1.7327 | 0.8320 | - |
| 5.7362 | 100000 | 1.123 | 1.8170 | 0.8191 | - |
| 5.7649 | 100500 | 1.0396 | 1.6897 | 0.8258 | - |
| 5.7936 | 101000 | 1.0426 | 1.7272 | 0.8331 | - |
| 5.8223 | 101500 | 1.0729 | 1.7506 | 0.8244 | - |
| 5.8510 | 102000 | 1.0641 | 1.7722 | 0.8306 | - |
| 5.8797 | 102500 | 1.0518 | 1.6785 | 0.8273 | - |
| 5.9083 | 103000 | 1.0955 | 1.9886 | 0.8288 | - |
| 5.9370 | 103500 | 1.21 | 2.0539 | 0.8261 | - |
| 5.9657 | 104000 | 3.942 | 1.7589 | 0.8341 | - |
| 5.9944 | 104500 | 1.1229 | 1.8700 | 0.8185 | - |
| 6.0231 | 105000 | 0.9885 | 1.8072 | 0.8278 | - |
| 6.0517 | 105500 | 0.9292 | 1.8001 | 0.8230 | - |
| 6.0804 | 106000 | 0.8982 | 1.8051 | 0.8317 | - |
| 6.1091 | 106500 | 0.8904 | 1.7529 | 0.8260 | - |
| 6.1378 | 107000 | 0.8534 | 1.7874 | 0.8190 | - |
| 6.1665 | 107500 | 0.9079 | 1.7166 | 0.8289 | - |
| 6.1951 | 108000 | 0.9005 | 1.7859 | 0.8209 | - |
| 6.2238 | 108500 | 0.9184 | 1.7757 | 0.8215 | - |
| 6.2525 | 109000 | 0.9333 | 1.7849 | 0.8261 | - |
| 6.2812 | 109500 | 0.9627 | 1.8212 | 0.8209 | - |
| 6.3099 | 110000 | 0.9174 | 1.7716 | 0.8239 | - |
| 6.3386 | 110500 | 0.9259 | 1.8290 | 0.8278 | - |
| 6.3672 | 111000 | 0.8882 | 1.7430 | 0.8272 | - |
| 6.3959 | 111500 | 0.8686 | 1.8061 | 0.8245 | - |
| 6.4246 | 112000 | 0.9222 | 1.8112 | 0.8221 | - |
| 6.4533 | 112500 | 0.9037 | 1.8119 | 0.8211 | - |
| 6.4820 | 113000 | 0.8855 | 1.8029 | 0.8118 | - |
| 6.5106 | 113500 | 0.9046 | 1.8553 | 0.8245 | - |
| 6.5393 | 114000 | 0.9272 | 1.7863 | 0.8176 | - |
| 6.5680 | 114500 | 0.931 | 1.8363 | 0.8161 | - |
| 6.5967 | 115000 | 1.0015 | 1.9976 | 0.8130 | - |
| 6.6254 | 115500 | 1.0549 | 1.8178 | 0.8212 | - |
| 6.6540 | 116000 | 0.9827 | 1.7530 | 0.8265 | - |
| 6.6827 | 116500 | 0.9652 | 1.8149 | 0.8206 | - |
| 6.7114 | 117000 | 0.9022 | 1.8423 | 0.8259 | - |
| 6.7401 | 117500 | 0.9249 | 1.7947 | 0.8176 | - |
| 6.7688 | 118000 | 0.8837 | 1.8191 | 0.8204 | - |
| 6.7975 | 118500 | 0.9227 | 1.7489 | 0.8259 | - |
| 6.8261 | 119000 | 0.925 | 1.7993 | 0.8160 | - |
| 6.8548 | 119500 | 0.9141 | 1.8146 | 0.8196 | - |
| 6.8835 | 120000 | 0.8956 | 1.7155 | 0.8253 | - |
| 6.9122 | 120500 | 0.8889 | 1.7959 | 0.8347 | - |
| 6.9409 | 121000 | 0.9551 | 1.7828 | 0.8286 | - |
| 6.9695 | 121500 | 0.8899 | 1.7918 | 0.8205 | - |
| 6.9982 | 122000 | 0.9091 | 1.7596 | 0.8236 | - |
| 7.0269 | 122500 | 0.7642 | 1.8320 | 0.8206 | - |
| 7.0556 | 123000 | 0.9046 | 1.9385 | 0.8222 | - |
| 7.0843 | 123500 | 0.766 | 1.9060 | 0.8259 | - |
| 7.1129 | 124000 | 0.789 | 1.8289 | 0.8264 | - |
| 7.1416 | 124500 | 0.7838 | 1.8628 | 0.8281 | - |
| 7.1703 | 125000 | 0.778 | 1.7788 | 0.8154 | - |
| 7.1990 | 125500 | 0.7838 | 1.8501 | 0.8157 | - |
| 7.2277 | 126000 | 0.8169 | 1.7758 | 0.8273 | - |
| 7.2564 | 126500 | 0.7384 | 1.9127 | 0.8194 | - |
| 7.2850 | 127000 | 0.7868 | 1.9334 | 0.8216 | - |
| 7.3137 | 127500 | 0.7678 | 1.7547 | 0.8274 | - |
| 7.3424 | 128000 | 0.7264 | 1.7854 | 0.8275 | - |
| 7.3711 | 128500 | 0.7925 | 1.8186 | 0.8318 | - |
| 7.3998 | 129000 | 0.7739 | 1.8462 | 0.8284 | - |
| 7.4284 | 129500 | 0.7604 | 1.8237 | 0.8277 | - |
| 7.4571 | 130000 | 0.7492 | 1.8005 | 0.8266 | - |
| 7.4858 | 130500 | 0.7634 | 1.8099 | 0.8191 | - |
| 7.5145 | 131000 | 0.724 | 1.7840 | 0.8328 | - |
| 7.5432 | 131500 | 0.7595 | 1.8725 | 0.8219 | - |
| 7.5718 | 132000 | 0.7308 | 1.8795 | 0.8154 | - |
| 7.6005 | 132500 | 0.7312 | 1.7966 | 0.8291 | - |
| 7.6292 | 133000 | 0.743 | 1.7927 | 0.8278 | - |
| 7.6579 | 133500 | 0.7586 | 1.7952 | 0.8294 | - |
| 7.6866 | 134000 | 0.8312 | 1.7386 | 0.8213 | - |
| 7.7153 | 134500 | 0.7633 | 1.7157 | 0.8260 | - |
| 7.7439 | 135000 | 0.7448 | 1.8266 | 0.8261 | - |
| 7.7726 | 135500 | 0.818 | 1.7873 | 0.8275 | - |
| 7.8013 | 136000 | 0.8235 | 1.7485 | 0.8198 | - |
| 7.8300 | 136500 | 0.7899 | 1.8871 | 0.8176 | - |
| 7.8587 | 137000 | 0.8828 | 1.9689 | 0.8184 | - |
| 7.8873 | 137500 | 0.7736 | 1.7805 | 0.8213 | - |
| 7.9160 | 138000 | 0.7228 | 1.8282 | 0.8248 | - |
| 7.9447 | 138500 | 0.7677 | 1.7306 | 0.8244 | - |
| 7.9734 | 139000 | 0.7351 | 1.8036 | 0.8220 | - |
| 8.0021 | 139500 | 0.7599 | 1.7727 | 0.8148 | - |
| 8.0307 | 140000 | 0.6264 | 1.7673 | 0.8177 | - |
| 8.0594 | 140500 | 0.6125 | 1.7771 | 0.8227 | - |
| 8.0881 | 141000 | 0.6353 | 1.7675 | 0.8195 | - |
| 8.1168 | 141500 | 0.6346 | 1.7946 | 0.8229 | - |
| 8.1455 | 142000 | 0.6101 | 1.7527 | 0.8280 | - |
| 8.1742 | 142500 | 0.5788 | 1.7372 | 0.8236 | - |
| 8.2028 | 143000 | 0.6028 | 1.7798 | 0.8248 | - |
| 8.2315 | 143500 | 0.649 | 1.7616 | 0.8198 | - |
| 8.2602 | 144000 | 0.6672 | 1.7052 | 0.8319 | - |
| 8.2889 | 144500 | 0.665 | 1.8043 | 0.8249 | - |
| 8.3176 | 145000 | 0.619 | 1.8087 | 0.8207 | - |
| 8.3462 | 145500 | 0.6151 | 1.7635 | 0.8305 | - |
| 8.3749 | 146000 | 0.6022 | 1.7403 | 0.8313 | - |
| 8.4036 | 146500 | 0.6258 | 1.7289 | 0.8250 | - |
| 8.4323 | 147000 | 0.6407 | 1.7225 | 0.8277 | - |
| 8.4610 | 147500 | 0.6372 | 1.7056 | 0.8284 | - |
| 8.4896 | 148000 | 0.6761 | 1.7248 | 0.8212 | - |
| 8.5183 | 148500 | 0.6568 | 1.7226 | 0.8265 | - |
| 8.5470 | 149000 | 0.6383 | 1.6703 | 0.8281 | - |
| 8.5757 | 149500 | 0.624 | 1.7020 | 0.8245 | - |
| 8.6044 | 150000 | 0.6188 | 1.7051 | 0.8293 | - |
| 8.6331 | 150500 | 0.6376 | 1.6799 | 0.8298 | - |
| 8.6617 | 151000 | 0.6795 | 1.7103 | 0.8247 | - |
| 8.6904 | 151500 | 0.6274 | 1.6895 | 0.8208 | - |
| 8.7191 | 152000 | 0.6165 | 1.7270 | 0.8241 | - |
| 8.7478 | 152500 | 0.6016 | 1.7310 | 0.8217 | - |
| 8.7765 | 153000 | 0.5853 | 1.7136 | 0.8252 | - |
| 8.8051 | 153500 | 0.666 | 1.7093 | 0.8288 | - |
| 8.8338 | 154000 | 0.61 | 1.7469 | 0.8250 | - |
| 8.8625 | 154500 | 0.6542 | 1.7309 | 0.8237 | - |
| 8.8912 | 155000 | 0.6038 | 1.6728 | 0.8213 | - |
| 8.9199 | 155500 | 0.6195 | 1.6677 | 0.8189 | - |
| 8.9485 | 156000 | 0.646 | 1.7323 | 0.8253 | - |
| 8.9772 | 156500 | 0.6538 | 1.6865 | 0.8238 | - |
| 9.0059 | 157000 | 0.592 | 1.7343 | 0.8209 | - |
| 9.0346 | 157500 | 0.5138 | 1.7442 | 0.8233 | - |
| 9.0633 | 158000 | 0.4933 | 1.7031 | 0.8232 | - |
| 9.0920 | 158500 | 0.4745 | 1.7306 | 0.8272 | - |
| 9.1206 | 159000 | 0.4669 | 1.7311 | 0.8289 | - |
| 9.1493 | 159500 | 0.5194 | 1.6786 | 0.8285 | - |
| 9.1780 | 160000 | 0.536 | 1.7298 | 0.8257 | - |
| 9.2067 | 160500 | 0.4942 | 1.7287 | 0.8260 | - |
| 9.2354 | 161000 | 0.5187 | 1.6976 | 0.8235 | - |
| 9.2640 | 161500 | 0.4831 | 1.6702 | 0.8305 | - |
| 9.2927 | 162000 | 0.5253 | 1.7145 | 0.8242 | - |
| 9.3214 | 162500 | 0.4667 | 1.6928 | 0.8245 | - |
| 9.3501 | 163000 | 0.5022 | 1.6803 | 0.8252 | - |
| 9.3788 | 163500 | 0.5203 | 1.7851 | 0.8236 | - |
| 9.4074 | 164000 | 0.4864 | 1.6996 | 0.8217 | - |
| 9.4361 | 164500 | 0.5125 | 1.7387 | 0.8176 | - |
| 9.4648 | 165000 | 0.4808 | 1.6818 | 0.8287 | - |
| 9.4935 | 165500 | 0.5257 | 1.7030 | 0.8255 | - |
| 9.5222 | 166000 | 0.4963 | 1.7088 | 0.8237 | - |
| 9.5509 | 166500 | 0.5304 | 1.6953 | 0.8275 | - |
| 9.5795 | 167000 | 0.5243 | 1.6535 | 0.8236 | - |
| 9.6082 | 167500 | 0.5012 | 1.6995 | 0.8259 | - |
| 9.6369 | 168000 | 0.5155 | 1.6797 | 0.8267 | - |
| 9.6656 | 168500 | 0.511 | 1.6843 | 0.8258 | - |
| 9.6943 | 169000 | 0.4822 | 1.6736 | 0.8308 | - |
| 9.7229 | 169500 | 0.4908 | 1.6450 | 0.8233 | - |
| 9.7516 | 170000 | 0.5098 | 1.6952 | 0.8243 | - |
| 9.7803 | 170500 | 0.5232 | 1.7315 | 0.8263 | - |
| 9.8090 | 171000 | 0.5174 | 1.7310 | 0.8273 | - |
| 9.8377 | 171500 | 0.5064 | 1.6783 | 0.8290 | - |
| 9.8663 | 172000 | 0.5096 | 1.7544 | 0.8248 | - |
| 9.8950 | 172500 | 0.4885 | 1.6620 | 0.8270 | - |
| 9.9237 | 173000 | 0.4612 | 1.6874 | 0.8210 | - |
| 9.9524 | 173500 | 0.5025 | 1.7113 | 0.8221 | - |
| 9.9811 | 174000 | 0.5071 | 1.7020 | 0.8237 | - |
| 10.0098 | 174500 | 0.4593 | 1.7157 | 0.8234 | - |
| 10.0384 | 175000 | 0.3894 | 1.7493 | 0.8260 | - |
| 10.0671 | 175500 | 0.3875 | 1.7702 | 0.8223 | - |
| 10.0958 | 176000 | 0.4322 | 1.8000 | 0.8227 | - |
| 10.1245 | 176500 | 0.4227 | 1.7576 | 0.8276 | - |
| 10.1532 | 177000 | 0.4368 | 1.7613 | 0.8285 | - |
| 10.1818 | 177500 | 0.4236 | 1.7270 | 0.8299 | - |
| 10.2105 | 178000 | 0.4149 | 1.7487 | 0.8298 | - |
| 10.2392 | 178500 | 0.4108 | 1.7098 | 0.8274 | - |
| 10.2679 | 179000 | 0.4116 | 1.6843 | 0.8284 | - |
| 10.2966 | 179500 | 0.3987 | 1.6513 | 0.8284 | - |
| 10.3252 | 180000 | 0.4667 | 1.7409 | 0.8261 | - |
| 10.3539 | 180500 | 0.4278 | 1.7262 | 0.8249 | - |
| 10.3826 | 181000 | 0.4427 | 1.7291 | 0.8250 | - |
| 10.4113 | 181500 | 0.4157 | 1.6731 | 0.8270 | - |
| 10.4400 | 182000 | 0.4301 | 1.6889 | 0.8266 | - |
| 10.4687 | 182500 | 0.3917 | 1.7171 | 0.8221 | - |
| 10.4973 | 183000 | 0.3984 | 1.6740 | 0.8204 | - |
| 10.5260 | 183500 | 0.3972 | 1.6973 | 0.8226 | - |
| 10.5547 | 184000 | 0.3958 | 1.7018 | 0.8276 | - |
| 10.5834 | 184500 | 0.4144 | 1.7134 | 0.8218 | - |
| 10.6121 | 185000 | 0.3967 | 1.7309 | 0.8196 | - |
| 10.6407 | 185500 | 0.43 | 1.6597 | 0.8273 | - |
| 10.6694 | 186000 | 0.409 | 1.7010 | 0.8212 | - |
| 10.6981 | 186500 | 0.4209 | 1.6872 | 0.8224 | - |
| 10.7268 | 187000 | 0.4165 | 1.6793 | 0.8230 | - |
| 10.7555 | 187500 | 0.3769 | 1.6417 | 0.8254 | - |
| 10.7841 | 188000 | 0.4232 | 1.6933 | 0.8187 | - |
| 10.8128 | 188500 | 0.3872 | 1.6862 | 0.8225 | - |
| 10.8415 | 189000 | 0.4396 | 1.6578 | 0.8212 | - |
| 10.8702 | 189500 | 0.409 | 1.6828 | 0.8242 | - |
| 10.8989 | 190000 | 0.3897 | 1.6644 | 0.8239 | - |
| 10.9276 | 190500 | 0.4072 | 1.6723 | 0.8299 | - |
| 10.9562 | 191000 | 0.4102 | 1.7174 | 0.8263 | - |
| 10.9849 | 191500 | 0.4437 | 1.6481 | 0.8246 | - |
| 11.0136 | 192000 | 0.3587 | 1.7165 | 0.8224 | - |
| 11.0423 | 192500 | 0.3295 | 1.6652 | 0.8264 | - |
| 11.0710 | 193000 | 0.3662 | 1.7058 | 0.8245 | - |
| 11.0996 | 193500 | 0.3254 | 1.6834 | 0.8217 | - |
| 11.1283 | 194000 | 0.3416 | 1.6786 | 0.8236 | - |
| 11.1570 | 194500 | 0.3161 | 1.7102 | 0.8244 | - |
| 11.1857 | 195000 | 0.3641 | 1.7259 | 0.8240 | - |
| 11.2144 | 195500 | 0.3503 | 1.7683 | 0.8211 | - |
| 11.2430 | 196000 | 0.3574 | 1.7092 | 0.8207 | - |
| 11.2717 | 196500 | 0.3519 | 1.7105 | 0.8204 | - |
| 11.3004 | 197000 | 0.3439 | 1.6659 | 0.8255 | - |
| 11.3291 | 197500 | 0.3401 | 1.6938 | 0.8194 | - |
| 11.3578 | 198000 | 0.3542 | 1.6713 | 0.8204 | - |
| 11.3865 | 198500 | 0.3451 | 1.6958 | 0.8229 | - |
| 11.4151 | 199000 | 0.3548 | 1.6717 | 0.8213 | - |
| 11.4438 | 199500 | 0.3607 | 1.6450 | 0.8270 | - |
| 11.4725 | 200000 | 0.3242 | 1.7143 | 0.8214 | - |
| 11.5012 | 200500 | 0.3547 | 1.6688 | 0.8206 | - |
| 11.5299 | 201000 | 0.3443 | 1.6909 | 0.8218 | - |
| 11.5585 | 201500 | 0.3799 | 1.6252 | 0.8224 | - |
| 11.5872 | 202000 | 0.3599 | 1.6647 | 0.8211 | - |
| 11.6159 | 202500 | 0.3385 | 1.6586 | 0.8227 | - |
| 11.6446 | 203000 | 0.3176 | 1.6887 | 0.8225 | - |
| 11.6733 | 203500 | 0.3387 | 1.7232 | 0.8247 | - |
| 11.7019 | 204000 | 0.3399 | 1.6772 | 0.8265 | - |
| 11.7306 | 204500 | 0.3491 | 1.7123 | 0.8213 | - |
| 11.7593 | 205000 | 0.3416 | 1.6950 | 0.8233 | - |
| 11.7880 | 205500 | 0.3029 | 1.6988 | 0.8207 | - |
| 11.8167 | 206000 | 0.3348 | 1.6667 | 0.8259 | - |
| 11.8454 | 206500 | 0.3491 | 1.6693 | 0.8238 | - |
| 11.8740 | 207000 | 0.3096 | 1.6617 | 0.8236 | - |
| 11.9027 | 207500 | 0.2888 | 1.6873 | 0.8261 | - |
| 11.9314 | 208000 | 0.3492 | 1.6676 | 0.8253 | - |
| 11.9601 | 208500 | 0.344 | 1.6592 | 0.8254 | - |
| 11.9888 | 209000 | 0.2991 | 1.6427 | 0.8289 | - |
| 12.0174 | 209500 | 0.2895 | 1.6966 | 0.8244 | - |
| 12.0461 | 210000 | 0.2764 | 1.6716 | 0.8227 | - |
| 12.0748 | 210500 | 0.3001 | 1.6863 | 0.8220 | - |
| 12.1035 | 211000 | 0.2832 | 1.6749 | 0.8250 | - |
| 12.1322 | 211500 | 0.2937 | 1.6697 | 0.8267 | - |
| 12.1608 | 212000 | 0.2737 | 1.6615 | 0.8236 | - |
| 12.1895 | 212500 | 0.2909 | 1.7160 | 0.8206 | - |
| 12.2182 | 213000 | 0.2847 | 1.6509 | 0.8268 | - |
| 12.2469 | 213500 | 0.2711 | 1.6814 | 0.8233 | - |
| 12.2756 | 214000 | 0.2868 | 1.6701 | 0.8241 | - |
| 12.3043 | 214500 | 0.2898 | 1.6717 | 0.8223 | - |
| 12.3329 | 215000 | 0.2847 | 1.7059 | 0.8233 | - |
| 12.3616 | 215500 | 0.3015 | 1.6790 | 0.8240 | - |
| 12.3903 | 216000 | 0.2793 | 1.6922 | 0.8261 | - |
| 12.4190 | 216500 | 0.2803 | 1.7192 | 0.8230 | - |
| 12.4477 | 217000 | 0.2892 | 1.6702 | 0.8260 | - |
| 12.4763 | 217500 | 0.2903 | 1.6929 | 0.8237 | - |
| 12.5050 | 218000 | 0.295 | 1.6340 | 0.8264 | - |
| 12.5337 | 218500 | 0.293 | 1.6505 | 0.8270 | - |
| 12.5624 | 219000 | 0.2701 | 1.6945 | 0.8271 | - |
| 12.5911 | 219500 | 0.267 | 1.6784 | 0.8278 | - |
| 12.6197 | 220000 | 0.3009 | 1.6514 | 0.8269 | - |
| 12.6484 | 220500 | 0.266 | 1.6717 | 0.8261 | - |
| 12.6771 | 221000 | 0.3 | 1.6844 | 0.8280 | - |
| 12.7058 | 221500 | 0.3059 | 1.6771 | 0.8314 | - |
| 12.7345 | 222000 | 0.2901 | 1.6663 | 0.8319 | - |
| 12.7632 | 222500 | 0.279 | 1.6392 | 0.8314 | - |
| 12.7918 | 223000 | 0.2949 | 1.6556 | 0.8270 | - |
| 12.8205 | 223500 | 0.2616 | 1.6746 | 0.8265 | - |
| 12.8492 | 224000 | 0.2809 | 1.6477 | 0.8284 | - |
| 12.8779 | 224500 | 0.2609 | 1.6443 | 0.8281 | - |
| 12.9066 | 225000 | 0.2799 | 1.6440 | 0.8274 | - |
| 12.9352 | 225500 | 0.2869 | 1.6878 | 0.8258 | - |
| 12.9639 | 226000 | 0.253 | 1.6778 | 0.8246 | - |
| 12.9926 | 226500 | 0.2926 | 1.6454 | 0.8255 | - |
| 13.0213 | 227000 | 0.2348 | 1.6859 | 0.8242 | - |
| 13.0500 | 227500 | 0.2353 | 1.6554 | 0.8231 | - |
| 13.0786 | 228000 | 0.2488 | 1.6847 | 0.8226 | - |
| 13.1073 | 228500 | 0.259 | 1.6820 | 0.8255 | - |
| 13.1360 | 229000 | 0.2341 | 1.6892 | 0.8237 | - |
| 13.1647 | 229500 | 0.2603 | 1.7153 | 0.8228 | - |
| 13.1934 | 230000 | 0.2411 | 1.6844 | 0.8235 | - |
| 13.2221 | 230500 | 0.2626 | 1.6940 | 0.8240 | - |
| 13.2507 | 231000 | 0.241 | 1.6811 | 0.8247 | - |
| 13.2794 | 231500 | 0.2342 | 1.6801 | 0.8262 | - |
| 13.3081 | 232000 | 0.2334 | 1.6911 | 0.8261 | - |
| 13.3368 | 232500 | 0.2575 | 1.6722 | 0.8236 | - |
| 13.3655 | 233000 | 0.2329 | 1.6650 | 0.8244 | - |
| 13.3941 | 233500 | 0.2547 | 1.6775 | 0.8251 | - |
| 13.4228 | 234000 | 0.2234 | 1.6631 | 0.8239 | - |
| 13.4515 | 234500 | 0.2365 | 1.6691 | 0.8235 | - |
| 13.4802 | 235000 | 0.2268 | 1.7275 | 0.8231 | - |
| 13.5089 | 235500 | 0.2306 | 1.6805 | 0.8245 | - |
| 13.5375 | 236000 | 0.2388 | 1.6765 | 0.8258 | - |
| 13.5662 | 236500 | 0.2474 | 1.6769 | 0.8240 | - |
| 13.5949 | 237000 | 0.2499 | 1.7176 | 0.8228 | - |
| 13.6236 | 237500 | 0.2406 | 1.6807 | 0.8241 | - |
| 13.6523 | 238000 | 0.2481 | 1.7075 | 0.8234 | - |
| 13.6809 | 238500 | 0.2472 | 1.6630 | 0.8234 | - |
| 13.7096 | 239000 | 0.231 | 1.7123 | 0.8238 | - |
| 13.7383 | 239500 | 0.2294 | 1.6875 | 0.8243 | - |
| 13.7670 | 240000 | 0.2459 | 1.7007 | 0.8250 | - |
| 13.7957 | 240500 | 0.2512 | 1.6751 | 0.8278 | - |
| 13.8244 | 241000 | 0.2355 | 1.7079 | 0.8262 | - |
| 13.8530 | 241500 | 0.2265 | 1.7144 | 0.8263 | - |
| 13.8817 | 242000 | 0.2324 | 1.7026 | 0.8268 | - |
| 13.9104 | 242500 | 0.2299 | 1.6978 | 0.8273 | - |
| 13.9391 | 243000 | 0.2362 | 1.7243 | 0.8267 | - |
| 13.9678 | 243500 | 0.2315 | 1.6821 | 0.8290 | - |
| 13.9964 | 244000 | 0.2386 | 1.7134 | 0.8270 | - |
| 14.0251 | 244500 | 0.2062 | 1.6998 | 0.8269 | - |
| 14.0538 | 245000 | 0.219 | 1.7169 | 0.8249 | - |
| 14.0825 | 245500 | 0.2071 | 1.7173 | 0.8264 | - |
| 14.1112 | 246000 | 0.2178 | 1.7058 | 0.8257 | - |
| 14.1398 | 246500 | 0.2071 | 1.7181 | 0.8251 | - |
| 14.1685 | 247000 | 0.1918 | 1.7252 | 0.8243 | - |
| 14.1972 | 247500 | 0.2307 | 1.7096 | 0.8241 | - |
| 14.2259 | 248000 | 0.2288 | 1.7527 | 0.8235 | - |
| 14.2546 | 248500 | 0.2097 | 1.7030 | 0.8250 | - |
| 14.2833 | 249000 | 0.2275 | 1.7006 | 0.8249 | - |
| 14.3119 | 249500 | 0.2361 | 1.7337 | 0.8235 | - |
| 14.3406 | 250000 | 0.2023 | 1.7084 | 0.8234 | - |
| 14.3693 | 250500 | 0.2112 | 1.7090 | 0.8232 | - |
| 14.3980 | 251000 | 0.2193 | 1.7033 | 0.8241 | - |
| 14.4267 | 251500 | 0.2157 | 1.7041 | 0.8236 | - |
| 14.4553 | 252000 | 0.2059 | 1.7023 | 0.8236 | - |
| 14.4840 | 252500 | 0.194 | 1.7170 | 0.8240 | - |
| 14.5127 | 253000 | 0.1852 | 1.7050 | 0.8246 | - |
| 14.5414 | 253500 | 0.2043 | 1.7011 | 0.8246 | - |
| 14.5701 | 254000 | 0.2103 | 1.7024 | 0.8245 | - |
| 14.5987 | 254500 | 0.1906 | 1.7177 | 0.8242 | - |
| 14.6274 | 255000 | 0.2176 | 1.7233 | 0.8237 | - |
| 14.6561 | 255500 | 0.2065 | 1.7247 | 0.8231 | - |
| 14.6848 | 256000 | 0.222 | 1.7163 | 0.8236 | - |
| 14.7135 | 256500 | 0.2234 | 1.7166 | 0.8232 | - |
| 14.7422 | 257000 | 0.2093 | 1.7230 | 0.8223 | - |
| 14.7708 | 257500 | 0.2321 | 1.7148 | 0.8222 | - |
| 14.7995 | 258000 | 0.2046 | 1.7102 | 0.8225 | - |
| 14.8282 | 258500 | 0.1773 | 1.7073 | 0.8230 | - |
| 14.8569 | 259000 | 0.1961 | 1.7131 | 0.8231 | - |
| 14.8856 | 259500 | 0.2092 | 1.7097 | 0.8232 | - |
| 14.9142 | 260000 | 0.208 | 1.7093 | 0.8232 | - |
| 14.9429 | 260500 | 0.2159 | 1.7110 | 0.8230 | - |
| 14.9716 | 261000 | 0.2106 | 1.7138 | 0.8229 | - |
| -1 | -1 | - | - | - | 0.7977 |