---
base_model: sentence-transformers/all-MiniLM-L6-v2
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:510287
- loss:CoSENTLoss
widget:
- source_sentence: bag
sentences:
- bag
- summer colors bag
- carry all bag
- source_sentence: bean bag
sentences:
- bag
- havan bag
- black yellow shoes
- source_sentence: pyramid shaped cushion mattress
sentences:
- dress
- silver bag
- women shoes
- source_sentence: handcrafted rug
sentences:
- amaga cross bag - white
- handcrafted boots
- polyester top
- source_sentence: bean bag
sentences:
- bag
- v-neck dress
- bag
model-index:
- name: all-MiniLM-L6-v2-pair_score
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: -0.13726370961372045
name: Pearson Cosine
- type: spearman_cosine
value: -0.16645918619928507
name: Spearman Cosine
- type: pearson_manhattan
value: -0.1405300294713842
name: Pearson Manhattan
- type: spearman_manhattan
value: -0.16334559546016153
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.1432496898556385
name: Pearson Euclidean
- type: spearman_euclidean
value: -0.16645904911745338
name: Spearman Euclidean
- type: pearson_dot
value: -0.13726370008450378
name: Pearson Dot
- type: spearman_dot
value: -0.1664594964294906
name: Spearman Dot
- type: pearson_max
value: -0.13726370008450378
name: Pearson Max
- type: spearman_max
value: -0.16334559546016153
name: Spearman Max
---
# all-MiniLM-L6-v2-pair_score
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co./sentence-transformers/all-MiniLM-L6-v2)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'bean bag',
'bag',
'v-neck dress',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:------------|
| pearson_cosine | -0.1373 |
| **spearman_cosine** | **-0.1665** |
| pearson_manhattan | -0.1405 |
| spearman_manhattan | -0.1633 |
| pearson_euclidean | -0.1432 |
| spearman_euclidean | -0.1665 |
| pearson_dot | -0.1373 |
| spearman_dot | -0.1665 |
| pearson_max | -0.1373 |
| spearman_max | -0.1633 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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`: 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`: 4
- `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
- `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
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:-----------------------:|
| 0 | 0 | - | - | -0.1665 |
| 0.0063 | 100 | 11.9622 | - | - |
| 0.0125 | 200 | 11.265 | - | - |
| 0.0188 | 300 | 10.5195 | - | - |
| 0.0251 | 400 | 9.4744 | - | - |
| 0.0314 | 500 | 8.4815 | 8.6217 | - |
| 0.0376 | 600 | 7.6105 | - | - |
| 0.0439 | 700 | 6.8023 | - | - |
| 0.0502 | 800 | 6.1258 | - | - |
| 0.0564 | 900 | 5.5032 | - | - |
| 0.0627 | 1000 | 5.0397 | 5.1949 | - |
| 0.0690 | 1100 | 4.6909 | - | - |
| 0.0752 | 1200 | 4.5716 | - | - |
| 0.0815 | 1300 | 4.3983 | - | - |
| 0.0878 | 1400 | 4.2073 | - | - |
| 0.0941 | 1500 | 4.2164 | 4.1422 | - |
| 0.1003 | 1600 | 4.0921 | - | - |
| 0.1066 | 1700 | 4.1785 | - | - |
| 0.1129 | 1800 | 4.0503 | - | - |
| 0.1191 | 1900 | 3.8969 | - | - |
| 0.1254 | 2000 | 3.8538 | 3.9109 | - |
| 0.1317 | 2100 | 3.872 | - | - |
| 0.1380 | 2200 | 3.851 | - | - |
| 0.1442 | 2300 | 3.6301 | - | - |
| 0.1505 | 2400 | 3.5202 | - | - |
| 0.1568 | 2500 | 3.6759 | 3.6389 | - |
| 0.1630 | 2600 | 3.4106 | - | - |
| 0.1693 | 2700 | 3.69 | - | - |
| 0.1756 | 2800 | 3.6336 | - | - |
| 0.1819 | 2900 | 3.4715 | - | - |
| 0.1881 | 3000 | 3.2166 | 3.2739 | - |
| 0.1944 | 3100 | 3.3844 | - | - |
| 0.2007 | 3200 | 3.4449 | - | - |
| 0.2069 | 3300 | 3.0811 | - | - |
| 0.2132 | 3400 | 3.2777 | - | - |
| 0.2195 | 3500 | 2.9505 | 3.0865 | - |
| 0.2257 | 3600 | 3.1534 | - | - |
| 0.2320 | 3700 | 2.9669 | - | - |
| 0.2383 | 3800 | 2.9416 | - | - |
| 0.2446 | 3900 | 2.9637 | - | - |
| 0.2508 | 4000 | 2.9322 | 2.8447 | - |
| 0.2571 | 4100 | 2.6926 | - | - |
| 0.2634 | 4200 | 2.9353 | - | - |
| 0.2696 | 4300 | 2.635 | - | - |
| 0.2759 | 4400 | 2.5692 | - | - |
| 0.2822 | 4500 | 3.0283 | 2.9033 | - |
| 0.2885 | 4600 | 2.5804 | - | - |
| 0.2947 | 4700 | 3.1374 | - | - |
| 0.3010 | 4800 | 2.8479 | - | - |
| 0.3073 | 4900 | 2.6809 | - | - |
| 0.3135 | 5000 | 2.8267 | 2.6946 | - |
| 0.3198 | 5100 | 2.7341 | - | - |
| 0.3261 | 5200 | 2.8157 | - | - |
| 0.3324 | 5300 | 2.5867 | - | - |
| 0.3386 | 5400 | 2.8622 | - | - |
| 0.3449 | 5500 | 2.9063 | 2.6115 | - |
| 0.3512 | 5600 | 2.1514 | - | - |
| 0.3574 | 5700 | 2.3755 | - | - |
| 0.3637 | 5800 | 2.5055 | - | - |
| 0.3700 | 5900 | 3.3237 | - | - |
| 0.3762 | 6000 | 2.561 | 2.7512 | - |
| 0.3825 | 6100 | 2.4351 | - | - |
| 0.3888 | 6200 | 2.8472 | - | - |
| 0.3951 | 6300 | 2.76 | - | - |
| 0.4013 | 6400 | 2.1947 | - | - |
| 0.4076 | 6500 | 2.6409 | 2.5367 | - |
| 0.4139 | 6600 | 2.7262 | - | - |
| 0.4201 | 6700 | 2.7781 | - | - |
| 0.4264 | 6800 | 2.4718 | - | - |
| 0.4327 | 6900 | 2.567 | - | - |
| 0.4390 | 7000 | 2.4215 | 2.3409 | - |
| 0.4452 | 7100 | 1.9308 | - | - |
| 0.4515 | 7200 | 2.1232 | - | - |
| 0.4578 | 7300 | 2.421 | - | - |
| 0.4640 | 7400 | 2.3232 | - | - |
| 0.4703 | 7500 | 2.8543 | 2.3706 | - |
| 0.4766 | 7600 | 2.4276 | - | - |
| 0.4828 | 7700 | 2.4507 | - | - |
| 0.4891 | 7800 | 2.1963 | - | - |
| 0.4954 | 7900 | 2.4247 | - | - |
| 0.5017 | 8000 | 2.1948 | 2.5729 | - |
| 0.5079 | 8100 | 2.4069 | - | - |
| 0.5142 | 8200 | 2.4328 | - | - |
| 0.5205 | 8300 | 2.2198 | - | - |
| 0.5267 | 8400 | 2.1746 | - | - |
| 0.5330 | 8500 | 2.2618 | 2.3459 | - |
| 0.5393 | 8600 | 2.3909 | - | - |
| 0.5456 | 8700 | 2.035 | - | - |
| 0.5518 | 8800 | 2.2626 | - | - |
| 0.5581 | 8900 | 2.1541 | - | - |
| 0.5644 | 9000 | 1.9424 | 2.1625 | - |
| 0.5706 | 9100 | 2.5152 | - | - |
| 0.5769 | 9200 | 2.0462 | - | - |
| 0.5832 | 9300 | 1.6124 | - | - |
| 0.5895 | 9400 | 2.2236 | - | - |
| 0.5957 | 9500 | 2.4706 | 2.0569 | - |
| 0.6020 | 9600 | 2.4612 | - | - |
| 0.6083 | 9700 | 2.2784 | - | - |
| 0.6145 | 9800 | 1.9335 | - | - |
| 0.6208 | 9900 | 2.3779 | - | - |
| 0.6271 | 10000 | 1.6778 | 2.1123 | - |
| 0.6333 | 10100 | 2.4721 | - | - |
| 0.6396 | 10200 | 1.7822 | - | - |
| 0.6459 | 10300 | 2.077 | - | - |
| 0.6522 | 10400 | 1.9223 | - | - |
| 0.6584 | 10500 | 2.3513 | 1.8403 | - |
| 0.6647 | 10600 | 2.1387 | - | - |
| 0.6710 | 10700 | 2.1853 | - | - |
| 0.6772 | 10800 | 1.8715 | - | - |
| 0.6835 | 10900 | 1.8581 | - | - |
| 0.6898 | 11000 | 2.0076 | 2.0063 | - |
| 0.6961 | 11100 | 2.3144 | - | - |
| 0.7023 | 11200 | 2.0942 | - | - |
| 0.7086 | 11300 | 1.9117 | - | - |
| 0.7149 | 11400 | 2.2214 | - | - |
| 0.7211 | 11500 | 1.9678 | 1.9029 | - |
| 0.7274 | 11600 | 1.7459 | - | - |
| 0.7337 | 11700 | 2.0616 | - | - |
| 0.7400 | 11800 | 1.6169 | - | - |
| 0.7462 | 11900 | 1.5674 | - | - |
| 0.7525 | 12000 | 1.4956 | 1.8267 | - |
| 0.7588 | 12100 | 2.3816 | - | - |
| 0.7650 | 12200 | 2.2387 | - | - |
| 0.7713 | 12300 | 1.4625 | - | - |
| 0.7776 | 12400 | 2.028 | - | - |
| 0.7838 | 12500 | 2.151 | 1.7581 | - |
| 0.7901 | 12600 | 1.6896 | - | - |
| 0.7964 | 12700 | 1.8526 | - | - |
| 0.8027 | 12800 | 1.9745 | - | - |
| 0.8089 | 12900 | 2.1042 | - | - |
| 0.8152 | 13000 | 1.83 | 1.5667 | - |
| 0.8215 | 13100 | 1.7451 | - | - |
| 0.8277 | 13200 | 1.568 | - | - |
| 0.8340 | 13300 | 1.4432 | - | - |
| 0.8403 | 13400 | 1.9172 | - | - |
| 0.8466 | 13500 | 1.9438 | 1.6055 | - |
| 0.8528 | 13600 | 1.6488 | - | - |
| 0.8591 | 13700 | 1.8166 | - | - |
| 0.8654 | 13800 | 1.5929 | - | - |
| 0.8716 | 13900 | 1.2476 | - | - |
| 0.8779 | 14000 | 1.5236 | 1.8921 | - |
| 0.8842 | 14100 | 1.6538 | - | - |
| 0.8904 | 14200 | 1.8689 | - | - |
| 0.8967 | 14300 | 1.0831 | - | - |
| 0.9030 | 14400 | 1.7765 | - | - |
| 0.9093 | 14500 | 1.3548 | 1.6683 | - |
| 0.9155 | 14600 | 1.7792 | - | - |
| 0.9218 | 14700 | 1.73 | - | - |
| 0.9281 | 14800 | 1.5979 | - | - |
| 0.9343 | 14900 | 1.3678 | - | - |
| 0.9406 | 15000 | 2.0664 | 1.5161 | - |
| 0.9469 | 15100 | 1.4472 | - | - |
| 0.9532 | 15200 | 1.447 | - | - |
| 0.9594 | 15300 | 1.7261 | - | - |
| 0.9657 | 15400 | 1.4881 | - | - |
| 0.9720 | 15500 | 1.313 | 1.6227 | - |
| 0.9782 | 15600 | 1.4587 | - | - |
| 0.9845 | 15700 | 2.0982 | - | - |
| 0.9908 | 15800 | 1.4854 | - | - |
| 0.9971 | 15900 | 1.343 | - | - |
| 1.0033 | 16000 | 1.1795 | 1.5639 | - |
| 1.0096 | 16100 | 1.4001 | - | - |
| 1.0159 | 16200 | 1.3867 | - | - |
| 1.0221 | 16300 | 1.5191 | - | - |
| 1.0284 | 16400 | 1.4693 | - | - |
| 1.0347 | 16500 | 1.628 | 1.4716 | - |
| 1.0409 | 16600 | 1.0041 | - | - |
| 1.0472 | 16700 | 1.7728 | - | - |
| 1.0535 | 16800 | 1.5586 | - | - |
| 1.0598 | 16900 | 1.7229 | - | - |
| 1.0660 | 17000 | 1.5556 | 1.4676 | - |
| 1.0723 | 17100 | 1.2529 | - | - |
| 1.0786 | 17200 | 1.4787 | - | - |
| 1.0848 | 17300 | 1.1947 | - | - |
| 1.0911 | 17400 | 1.3014 | - | - |
| 1.0974 | 17500 | 1.3743 | 1.4624 | - |
| 1.1037 | 17600 | 1.3397 | - | - |
| 1.1099 | 17700 | 1.3062 | - | - |
| 1.1162 | 17800 | 1.3288 | - | - |
| 1.1225 | 17900 | 2.0002 | - | - |
| 1.1287 | 18000 | 2.0294 | 1.4185 | - |
| 1.1350 | 18100 | 1.5053 | - | - |
| 1.1413 | 18200 | 1.3657 | - | - |
| 1.1476 | 18300 | 1.3877 | - | - |
| 1.1538 | 18400 | 1.9034 | - | - |
| 1.1601 | 18500 | 1.4001 | 1.3813 | - |
| 1.1664 | 18600 | 1.7503 | - | - |
| 1.1726 | 18700 | 1.1482 | - | - |
| 1.1789 | 18800 | 1.0958 | - | - |
| 1.1852 | 18900 | 1.2657 | - | - |
| 1.1914 | 19000 | 1.3721 | 1.4702 | - |
| 1.1977 | 19100 | 1.2361 | - | - |
| 1.2040 | 19200 | 1.003 | - | - |
| 1.2103 | 19300 | 1.3677 | - | - |
| 1.2165 | 19400 | 1.668 | - | - |
| 1.2228 | 19500 | 1.2026 | 1.3641 | - |
| 1.2291 | 19600 | 1.1754 | - | - |
| 1.2353 | 19700 | 1.3196 | - | - |
| 1.2416 | 19800 | 1.4766 | - | - |
| 1.2479 | 19900 | 1.389 | - | - |
| 1.2542 | 20000 | 1.6974 | 1.3344 | - |
| 1.2604 | 20100 | 1.5036 | - | - |
| 1.2667 | 20200 | 1.1728 | - | - |
| 1.2730 | 20300 | 1.6058 | - | - |
| 1.2792 | 20400 | 1.5191 | - | - |
| 1.2855 | 20500 | 1.4516 | 1.3210 | - |
| 1.2918 | 20600 | 1.3485 | - | - |
| 1.2980 | 20700 | 1.2598 | - | - |
| 1.3043 | 20800 | 1.5871 | - | - |
| 1.3106 | 20900 | 1.1965 | - | - |
| 1.3169 | 21000 | 1.3983 | 1.2517 | - |
| 1.3231 | 21100 | 1.2605 | - | - |
| 1.3294 | 21200 | 1.5629 | - | - |
| 1.3357 | 21300 | 1.0668 | - | - |
| 1.3419 | 21400 | 1.1879 | - | - |
| 1.3482 | 21500 | 1.132 | 1.3881 | - |
| 1.3545 | 21600 | 1.7231 | - | - |
| 1.3608 | 21700 | 1.7636 | - | - |
| 1.3670 | 21800 | 1.1193 | - | - |
| 1.3733 | 21900 | 1.4662 | - | - |
| 1.3796 | 22000 | 2.0394 | 1.1927 | - |
| 1.3858 | 22100 | 1.1535 | - | - |
| 1.3921 | 22200 | 1.4592 | - | - |
| 1.3984 | 22300 | 1.276 | - | - |
| 1.4047 | 22400 | 1.2984 | - | - |
| 1.4109 | 22500 | 0.9741 | 1.2707 | - |
| 1.4172 | 22600 | 1.4253 | - | - |
| 1.4235 | 22700 | 1.0769 | - | - |
| 1.4297 | 22800 | 0.8276 | - | - |
| 1.4360 | 22900 | 1.2689 | - | - |
| 1.4423 | 23000 | 1.4817 | 1.2095 | - |
| 1.4485 | 23100 | 1.1522 | - | - |
| 1.4548 | 23200 | 0.8978 | - | - |
| 1.4611 | 23300 | 1.015 | - | - |
| 1.4674 | 23400 | 1.0351 | - | - |
| 1.4736 | 23500 | 1.3959 | 1.1969 | - |
| 1.4799 | 23600 | 1.2879 | - | - |
| 1.4862 | 23700 | 1.0651 | - | - |
| 1.4924 | 23800 | 1.1601 | - | - |
| 1.4987 | 23900 | 1.0034 | - | - |
| 1.5050 | 24000 | 1.3386 | 1.1590 | - |
| 1.5113 | 24100 | 1.142 | - | - |
| 1.5175 | 24200 | 1.3495 | - | - |
| 1.5238 | 24300 | 0.9993 | - | - |
| 1.5301 | 24400 | 0.9363 | - | - |
| 1.5363 | 24500 | 1.4402 | 1.2178 | - |
| 1.5426 | 24600 | 1.0648 | - | - |
| 1.5489 | 24700 | 1.5102 | - | - |
| 1.5552 | 24800 | 1.3415 | - | - |
| 1.5614 | 24900 | 0.7441 | - | - |
| 1.5677 | 25000 | 0.901 | 1.1982 | - |
| 1.5740 | 25100 | 1.3147 | - | - |
| 1.5802 | 25200 | 0.971 | - | - |
| 1.5865 | 25300 | 0.9988 | - | - |
| 1.5928 | 25400 | 1.1445 | - | - |
| 1.5990 | 25500 | 1.1018 | 1.1423 | - |
| 1.6053 | 25600 | 1.0902 | - | - |
| 1.6116 | 25700 | 1.2577 | - | - |
| 1.6179 | 25800 | 1.2005 | - | - |
| 1.6241 | 25900 | 1.2839 | - | - |
| 1.6304 | 26000 | 1.4122 | 1.1125 | - |
| 1.6367 | 26100 | 0.7832 | - | - |
| 1.6429 | 26200 | 1.3278 | - | - |
| 1.6492 | 26300 | 1.2055 | - | - |
| 1.6555 | 26400 | 1.5814 | - | - |
| 1.6618 | 26500 | 1.0393 | 1.0946 | - |
| 1.6680 | 26600 | 1.4531 | - | - |
| 1.6743 | 26700 | 1.4162 | - | - |
| 1.6806 | 26800 | 0.8498 | - | - |
| 1.6868 | 26900 | 1.1318 | - | - |
| 1.6931 | 27000 | 1.3287 | 1.0439 | - |
| 1.6994 | 27100 | 1.0886 | - | - |
| 1.7056 | 27200 | 0.8991 | - | - |
| 1.7119 | 27300 | 0.7563 | - | - |
| 1.7182 | 27400 | 0.9284 | - | - |
| 1.7245 | 27500 | 1.3388 | 1.0940 | - |
| 1.7307 | 27600 | 1.2951 | - | - |
| 1.7370 | 27700 | 0.9789 | - | - |
| 1.7433 | 27800 | 1.2898 | - | - |
| 1.7495 | 27900 | 0.9915 | - | - |
| 1.7558 | 28000 | 1.5349 | 1.0266 | - |
| 1.7621 | 28100 | 1.124 | - | - |
| 1.7684 | 28200 | 0.809 | - | - |
| 1.7746 | 28300 | 0.9617 | - | - |
| 1.7809 | 28400 | 1.3061 | - | - |
| 1.7872 | 28500 | 1.1323 | 1.0488 | - |
| 1.7934 | 28600 | 1.2991 | - | - |
| 1.7997 | 28700 | 0.8708 | - | - |
| 1.8060 | 28800 | 0.7493 | - | - |
| 1.8123 | 28900 | 1.004 | - | - |
| 1.8185 | 29000 | 1.1477 | 1.0206 | - |
| 1.8248 | 29100 | 1.1826 | - | - |
| 1.8311 | 29200 | 1.0961 | - | - |
| 1.8373 | 29300 | 1.4743 | - | - |
| 1.8436 | 29400 | 0.8413 | - | - |
| 1.8499 | 29500 | 1.2623 | 1.0047 | - |
| 1.8561 | 29600 | 0.8486 | - | - |
| 1.8624 | 29700 | 1.4481 | - | - |
| 1.8687 | 29800 | 1.2704 | - | - |
| 1.8750 | 29900 | 1.1913 | - | - |
| 1.8812 | 30000 | 0.9369 | 1.0277 | - |
| 1.8875 | 30100 | 1.2427 | - | - |
| 1.8938 | 30200 | 1.0576 | - | - |
| 1.9000 | 30300 | 0.9188 | - | - |
| 1.9063 | 30400 | 1.3227 | - | - |
| 1.9126 | 30500 | 1.4614 | 1.0550 | - |
| 1.9189 | 30600 | 1.2316 | - | - |
| 1.9251 | 30700 | 0.9487 | - | - |
| 1.9314 | 30800 | 1.1651 | - | - |
| 1.9377 | 30900 | 1.1622 | - | - |
| 1.9439 | 31000 | 1.1801 | 0.9981 | - |
| 1.9502 | 31100 | 0.8798 | - | - |
| 1.9565 | 31200 | 0.7196 | - | - |
| 1.9628 | 31300 | 1.2003 | - | - |
| 1.9690 | 31400 | 1.1823 | - | - |
| 1.9753 | 31500 | 1.1453 | 1.0320 | - |
| 1.9816 | 31600 | 1.4751 | - | - |
| 1.9878 | 31700 | 0.8502 | - | - |
| 1.9941 | 31800 | 0.8757 | - | - |
| 2.0004 | 31900 | 1.0489 | - | - |
| 2.0066 | 32000 | 1.4672 | 1.0571 | - |
| 2.0129 | 32100 | 0.9474 | - | - |
| 2.0192 | 32200 | 0.8037 | - | - |
| 2.0255 | 32300 | 0.9782 | - | - |
| 2.0317 | 32400 | 0.6943 | - | - |
| 2.0380 | 32500 | 1.0097 | 0.9797 | - |
| 2.0443 | 32600 | 0.9067 | - | - |
| 2.0505 | 32700 | 1.09 | - | - |
| 2.0568 | 32800 | 0.8464 | - | - |
| 2.0631 | 32900 | 0.9359 | - | - |
| 2.0694 | 33000 | 0.813 | 0.9907 | - |
| 2.0756 | 33100 | 0.8738 | - | - |
| 2.0819 | 33200 | 0.8178 | - | - |
| 2.0882 | 33300 | 1.1704 | - | - |
| 2.0944 | 33400 | 1.0073 | - | - |
| 2.1007 | 33500 | 1.1849 | 0.9582 | - |
| 2.1070 | 33600 | 0.7795 | - | - |
| 2.1133 | 33700 | 0.7688 | - | - |
| 2.1195 | 33800 | 0.9465 | - | - |
| 2.1258 | 33900 | 1.0883 | - | - |
| 2.1321 | 34000 | 0.7711 | 0.9557 | - |
| 2.1383 | 34100 | 0.9767 | - | - |
| 2.1446 | 34200 | 0.6702 | - | - |
| 2.1509 | 34300 | 0.9444 | - | - |
| 2.1571 | 34400 | 0.8741 | - | - |
| 2.1634 | 34500 | 1.0717 | 0.9526 | - |
| 2.1697 | 34600 | 0.8584 | - | - |
| 2.1760 | 34700 | 0.8926 | - | - |
| 2.1822 | 34800 | 0.8567 | - | - |
| 2.1885 | 34900 | 0.71 | - | - |
| 2.1948 | 35000 | 1.1285 | 0.9589 | - |
| 2.2010 | 35100 | 0.8999 | - | - |
| 2.2073 | 35200 | 0.8459 | - | - |
| 2.2136 | 35300 | 1.0608 | - | - |
| 2.2199 | 35400 | 0.6115 | - | - |
| 2.2261 | 35500 | 1.2468 | 0.9769 | - |
| 2.2324 | 35600 | 0.9987 | - | - |
| 2.2387 | 35700 | 0.9186 | - | - |
| 2.2449 | 35800 | 1.0505 | - | - |
| 2.2512 | 35900 | 0.6253 | - | - |
| 2.2575 | 36000 | 0.6523 | 0.9501 | - |
| 2.2637 | 36100 | 0.8252 | - | - |
| 2.2700 | 36200 | 0.9793 | - | - |
| 2.2763 | 36300 | 0.8845 | - | - |
| 2.2826 | 36400 | 1.0121 | - | - |
| 2.2888 | 36500 | 0.9849 | 0.9245 | - |
| 2.2951 | 36600 | 1.2937 | - | - |
| 2.3014 | 36700 | 1.0484 | - | - |
| 2.3076 | 36800 | 0.8801 | - | - |
| 2.3139 | 36900 | 0.7552 | - | - |
| 2.3202 | 37000 | 0.7641 | 0.9280 | - |
| 2.3265 | 37100 | 0.883 | - | - |
| 2.3327 | 37200 | 0.77 | - | - |
| 2.3390 | 37300 | 1.2699 | - | - |
| 2.3453 | 37400 | 0.8766 | - | - |
| 2.3515 | 37500 | 1.1154 | 0.9623 | - |
| 2.3578 | 37600 | 1.0634 | - | - |
| 2.3641 | 37700 | 0.8822 | - | - |
| 2.3704 | 37800 | 0.839 | - | - |
| 2.3766 | 37900 | 0.684 | - | - |
| 2.3829 | 38000 | 0.8051 | 0.9198 | - |
| 2.3892 | 38100 | 0.9585 | - | - |
| 2.3954 | 38200 | 0.7156 | - | - |
| 2.4017 | 38300 | 0.5271 | - | - |
| 2.4080 | 38400 | 0.805 | - | - |
| 2.4142 | 38500 | 0.7898 | 0.8785 | - |
| 2.4205 | 38600 | 0.6935 | - | - |
| 2.4268 | 38700 | 0.8011 | - | - |
| 2.4331 | 38800 | 0.9812 | - | - |
| 2.4393 | 38900 | 0.4427 | - | - |
| 2.4456 | 39000 | 0.492 | 0.9313 | - |
| 2.4519 | 39100 | 0.47 | - | - |
| 2.4581 | 39200 | 1.1876 | - | - |
| 2.4644 | 39300 | 0.5778 | - | - |
| 2.4707 | 39400 | 0.6763 | - | - |
| 2.4770 | 39500 | 0.6896 | 0.8978 | - |
| 2.4832 | 39600 | 0.8905 | - | - |
| 2.4895 | 39700 | 0.7845 | - | - |
| 2.4958 | 39800 | 0.8691 | - | - |
| 2.5020 | 39900 | 0.55 | - | - |
| 2.5083 | 40000 | 0.6978 | 0.9054 | - |
| 2.5146 | 40100 | 0.6378 | - | - |
| 2.5209 | 40200 | 0.895 | - | - |
| 2.5271 | 40300 | 0.9683 | - | - |
| 2.5334 | 40400 | 0.9373 | - | - |
| 2.5397 | 40500 | 0.7406 | 0.9128 | - |
| 2.5459 | 40600 | 0.8917 | - | - |
| 2.5522 | 40700 | 1.0552 | - | - |
| 2.5585 | 40800 | 0.5281 | - | - |
| 2.5647 | 40900 | 0.9064 | - | - |
| 2.5710 | 41000 | 0.6886 | 0.9049 | - |
| 2.5773 | 41100 | 0.7166 | - | - |
| 2.5836 | 41200 | 0.8343 | - | - |
| 2.5898 | 41300 | 0.9468 | - | - |
| 2.5961 | 41400 | 0.8529 | - | - |
| 2.6024 | 41500 | 0.8092 | 0.8954 | - |
| 2.6086 | 41600 | 0.8501 | - | - |
| 2.6149 | 41700 | 0.9877 | - | - |
| 2.6212 | 41800 | 0.8592 | - | - |
| 2.6275 | 41900 | 0.8632 | - | - |
| 2.6337 | 42000 | 0.6766 | 0.8707 | - |
| 2.6400 | 42100 | 0.7587 | - | - |
| 2.6463 | 42200 | 0.8949 | - | - |
| 2.6525 | 42300 | 0.4173 | - | - |
| 2.6588 | 42400 | 0.5995 | - | - |
| 2.6651 | 42500 | 0.8157 | 0.8681 | - |
| 2.6713 | 42600 | 0.92 | - | - |
| 2.6776 | 42700 | 0.9118 | - | - |
| 2.6839 | 42800 | 0.7446 | - | - |
| 2.6902 | 42900 | 0.6835 | - | - |
| 2.6964 | 43000 | 0.6157 | 0.8691 | - |
| 2.7027 | 43100 | 0.5423 | - | - |
| 2.7090 | 43200 | 0.8098 | - | - |
| 2.7152 | 43300 | 0.8908 | - | - |
| 2.7215 | 43400 | 1.1275 | - | - |
| 2.7278 | 43500 | 1.0345 | 0.8884 | - |
| 2.7341 | 43600 | 0.6198 | - | - |
| 2.7403 | 43700 | 0.8315 | - | - |
| 2.7466 | 43800 | 0.9317 | - | - |
| 2.7529 | 43900 | 0.516 | - | - |
| 2.7591 | 44000 | 0.8229 | 0.8659 | - |
| 2.7654 | 44100 | 0.7989 | - | - |
| 2.7717 | 44200 | 0.9291 | - | - |
| 2.7780 | 44300 | 0.5954 | - | - |
| 2.7842 | 44400 | 0.8537 | - | - |
| 2.7905 | 44500 | 0.9506 | 0.8657 | - |
| 2.7968 | 44600 | 0.5789 | - | - |
| 2.8030 | 44700 | 0.4861 | - | - |
| 2.8093 | 44800 | 0.9614 | - | - |
| 2.8156 | 44900 | 1.0069 | - | - |
| 2.8218 | 45000 | 0.5599 | 0.8619 | - |
| 2.8281 | 45100 | 1.3747 | - | - |
| 2.8344 | 45200 | 0.5638 | - | - |
| 2.8407 | 45300 | 1.2095 | - | - |
| 2.8469 | 45400 | 0.7364 | - | - |
| 2.8532 | 45500 | 0.5692 | 0.8818 | - |
| 2.8595 | 45600 | 0.8848 | - | - |
| 2.8657 | 45700 | 0.9063 | - | - |
| 2.8720 | 45800 | 0.8675 | - | - |
| 2.8783 | 45900 | 0.9703 | - | - |
| 2.8846 | 46000 | 0.6657 | 0.8424 | - |
| 2.8908 | 46100 | 0.6564 | - | - |
| 2.8971 | 46200 | 0.7945 | - | - |
| 2.9034 | 46300 | 0.6341 | - | - |
| 2.9096 | 46400 | 1.042 | - | - |
| 2.9159 | 46500 | 1.0812 | 0.8510 | - |
| 2.9222 | 46600 | 0.9787 | - | - |
| 2.9285 | 46700 | 0.8732 | - | - |
| 2.9347 | 46800 | 1.1872 | - | - |
| 2.9410 | 46900 | 0.989 | - | - |
| 2.9473 | 47000 | 0.874 | 0.8215 | - |
| 2.9535 | 47100 | 1.0229 | - | - |
| 2.9598 | 47200 | 0.9888 | - | - |
| 2.9661 | 47300 | 0.4883 | - | - |
| 2.9723 | 47400 | 0.7474 | - | - |
| 2.9786 | 47500 | 0.7615 | 0.8218 | - |
| 2.9849 | 47600 | 0.6208 | - | - |
| 2.9912 | 47700 | 0.8332 | - | - |
| 2.9974 | 47800 | 0.6734 | - | - |
| 3.0037 | 47900 | 0.5095 | - | - |
| 3.0100 | 48000 | 0.7709 | 0.8220 | - |
| 3.0162 | 48100 | 0.5449 | - | - |
| 3.0225 | 48200 | 0.772 | - | - |
| 3.0288 | 48300 | 0.8582 | - | - |
| 3.0351 | 48400 | 0.5742 | - | - |
| 3.0413 | 48500 | 0.5584 | 0.8493 | - |
| 3.0476 | 48600 | 0.9766 | - | - |
| 3.0539 | 48700 | 0.6473 | - | - |
| 3.0601 | 48800 | 0.5861 | - | - |
| 3.0664 | 48900 | 0.6377 | - | - |
| 3.0727 | 49000 | 0.8393 | 0.8430 | - |
| 3.0789 | 49100 | 0.8385 | - | - |
| 3.0852 | 49200 | 0.5523 | - | - |
| 3.0915 | 49300 | 0.6217 | - | - |
| 3.0978 | 49400 | 0.5515 | - | - |
| 3.1040 | 49500 | 0.851 | 0.8000 | - |
| 3.1103 | 49600 | 0.9247 | - | - |
| 3.1166 | 49700 | 0.655 | - | - |
| 3.1228 | 49800 | 0.4979 | - | - |
| 3.1291 | 49900 | 0.7521 | - | - |
| 3.1354 | 50000 | 0.53 | 0.8105 | - |
| 3.1417 | 50100 | 0.5943 | - | - |
| 3.1479 | 50200 | 0.4659 | - | - |
| 3.1542 | 50300 | 0.4843 | - | - |
| 3.1605 | 50400 | 0.7577 | - | - |
| 3.1667 | 50500 | 0.3448 | 0.8055 | - |
| 3.1730 | 50600 | 0.8392 | - | - |
| 3.1793 | 50700 | 0.75 | - | - |
| 3.1856 | 50800 | 0.5195 | - | - |
| 3.1918 | 50900 | 0.617 | - | - |
| 3.1981 | 51000 | 0.6892 | 0.8293 | - |
| 3.2044 | 51100 | 0.497 | - | - |
| 3.2106 | 51200 | 0.6793 | - | - |
| 3.2169 | 51300 | 0.7251 | - | - |
| 3.2232 | 51400 | 0.6471 | - | - |
| 3.2294 | 51500 | 0.775 | 0.8013 | - |
| 3.2357 | 51600 | 0.7289 | - | - |
| 3.2420 | 51700 | 0.6894 | - | - |
| 3.2483 | 51800 | 0.5677 | - | - |
| 3.2545 | 51900 | 0.317 | - | - |
| 3.2608 | 52000 | 0.5376 | 0.7853 | - |
| 3.2671 | 52100 | 0.4582 | - | - |
| 3.2733 | 52200 | 0.8505 | - | - |
| 3.2796 | 52300 | 0.6236 | - | - |
| 3.2859 | 52400 | 0.7388 | - | - |
| 3.2922 | 52500 | 0.7061 | 0.7863 | - |
| 3.2984 | 52600 | 0.5411 | - | - |
| 3.3047 | 52700 | 0.9511 | - | - |
| 3.3110 | 52800 | 0.5364 | - | - |
| 3.3172 | 52900 | 0.5795 | - | - |
| 3.3235 | 53000 | 0.5305 | 0.7876 | - |
| 3.3298 | 53100 | 0.8051 | - | - |
| 3.3361 | 53200 | 0.5342 | - | - |
| 3.3423 | 53300 | 0.4567 | - | - |
| 3.3486 | 53400 | 0.9751 | - | - |
| 3.3549 | 53500 | 0.4413 | 0.8008 | - |
| 3.3611 | 53600 | 0.6011 | - | - |
| 3.3674 | 53700 | 0.4708 | - | - |
| 3.3737 | 53800 | 0.6167 | - | - |
| 3.3799 | 53900 | 0.7653 | - | - |
| 3.3862 | 54000 | 0.7781 | 0.7897 | - |
| 3.3925 | 54100 | 0.9323 | - | - |
| 3.3988 | 54200 | 0.6003 | - | - |
| 3.4050 | 54300 | 0.5268 | - | - |
| 3.4113 | 54400 | 0.6639 | - | - |
| 3.4176 | 54500 | 0.388 | 0.7855 | - |
| 3.4238 | 54600 | 0.7258 | - | - |
| 3.4301 | 54700 | 0.6475 | - | - |
| 3.4364 | 54800 | 0.795 | - | - |
| 3.4427 | 54900 | 0.4978 | - | - |
| 3.4489 | 55000 | 0.6259 | 0.7705 | - |
| 3.4552 | 55100 | 0.791 | - | - |
| 3.4615 | 55200 | 0.7602 | - | - |
| 3.4677 | 55300 | 0.2236 | - | - |
| 3.4740 | 55400 | 0.5577 | - | - |
| 3.4803 | 55500 | 0.4214 | 0.7683 | - |
| 3.4865 | 55600 | 0.7335 | - | - |
| 3.4928 | 55700 | 0.7536 | - | - |
| 3.4991 | 55800 | 0.4577 | - | - |
| 3.5054 | 55900 | 0.5869 | - | - |
| 3.5116 | 56000 | 0.8563 | 0.7587 | - |
| 3.5179 | 56100 | 0.9291 | - | - |
| 3.5242 | 56200 | 0.4387 | - | - |
| 3.5304 | 56300 | 0.4491 | - | - |
| 3.5367 | 56400 | 0.506 | - | - |
| 3.5430 | 56500 | 0.6626 | 0.7634 | - |
| 3.5493 | 56600 | 0.8654 | - | - |
| 3.5555 | 56700 | 0.4455 | - | - |
| 3.5618 | 56800 | 0.4593 | - | - |
| 3.5681 | 56900 | 0.878 | - | - |
| 3.5743 | 57000 | 0.3737 | 0.7617 | - |
| 3.5806 | 57100 | 0.377 | - | - |
| 3.5869 | 57200 | 0.6894 | - | - |
| 3.5932 | 57300 | 0.6635 | - | - |
| 3.5994 | 57400 | 0.9224 | - | - |
| 3.6057 | 57500 | 0.635 | 0.7669 | - |
| 3.6120 | 57600 | 0.6797 | - | - |
| 3.6182 | 57700 | 0.9814 | - | - |
| 3.6245 | 57800 | 0.9893 | - | - |
| 3.6308 | 57900 | 0.6753 | - | - |
| 3.6370 | 58000 | 0.8349 | 0.7501 | - |
| 3.6433 | 58100 | 0.8523 | - | - |
| 3.6496 | 58200 | 0.2962 | - | - |
| 3.6559 | 58300 | 0.6585 | - | - |
| 3.6621 | 58400 | 1.0247 | - | - |
| 3.6684 | 58500 | 0.8638 | 0.7577 | - |
| 3.6747 | 58600 | 0.9456 | - | - |
| 3.6809 | 58700 | 0.5401 | - | - |
| 3.6872 | 58800 | 0.6602 | - | - |
| 3.6935 | 58900 | 0.7543 | - | - |
| 3.6998 | 59000 | 0.7893 | 0.7600 | - |
| 3.7060 | 59100 | 0.7746 | - | - |
| 3.7123 | 59200 | 0.6539 | - | - |
| 3.7186 | 59300 | 0.8083 | - | - |
| 3.7248 | 59400 | 0.3429 | - | - |
| 3.7311 | 59500 | 0.5005 | 0.7445 | - |
| 3.7374 | 59600 | 0.6238 | - | - |
| 3.7437 | 59700 | 0.4343 | - | - |
| 3.7499 | 59800 | 0.8189 | - | - |
| 3.7562 | 59900 | 0.6272 | - | - |
| 3.7625 | 60000 | 0.2982 | 0.7597 | - |
| 3.7687 | 60100 | 0.7028 | - | - |
| 3.7750 | 60200 | 0.9447 | - | - |
| 3.7813 | 60300 | 0.6175 | - | - |
| 3.7875 | 60400 | 0.5856 | - | - |
| 3.7938 | 60500 | 0.8249 | 0.7505 | - |
| 3.8001 | 60600 | 0.6617 | - | - |
| 3.8064 | 60700 | 0.5767 | - | - |
| 3.8126 | 60800 | 1.0094 | - | - |
| 3.8189 | 60900 | 0.471 | - | - |
| 3.8252 | 61000 | 0.6313 | 0.7489 | - |
| 3.8314 | 61100 | 0.6545 | - | - |
| 3.8377 | 61200 | 0.699 | - | - |
| 3.8440 | 61300 | 0.6272 | - | - |
| 3.8503 | 61400 | 0.7375 | - | - |
| 3.8565 | 61500 | 0.4213 | 0.7490 | - |
| 3.8628 | 61600 | 0.6631 | - | - |
| 3.8691 | 61700 | 0.552 | - | - |
| 3.8753 | 61800 | 0.7041 | - | - |
| 3.8816 | 61900 | 0.8457 | - | - |
| 3.8879 | 62000 | 0.8104 | 0.7477 | - |
| 3.8941 | 62100 | 0.4494 | - | - |
| 3.9004 | 62200 | 0.6947 | - | - |
| 3.9067 | 62300 | 0.8061 | - | - |
| 3.9130 | 62400 | 0.416 | - | - |
| 3.9192 | 62500 | 0.7359 | 0.7468 | - |
| 3.9255 | 62600 | 0.7408 | - | - |
| 3.9318 | 62700 | 0.6255 | - | - |
| 3.9380 | 62800 | 0.7865 | - | - |
| 3.9443 | 62900 | 0.4879 | - | - |
| 3.9506 | 63000 | 0.5196 | 0.7485 | - |
| 3.9569 | 63100 | 0.5683 | - | - |
| 3.9631 | 63200 | 0.5141 | - | - |
| 3.9694 | 63300 | 0.6068 | - | - |
| 3.9757 | 63400 | 0.5929 | - | - |
| 3.9819 | 63500 | 0.7513 | 0.7482 | - |
| 3.9882 | 63600 | 0.5053 | - | - |
| 3.9945 | 63700 | 0.5707 | - | - |