---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:449904
- loss:CosineSimilarityLoss
base_model: x2bee/ModernBERT-SimCSE-multitask_v03
widget:
- source_sentence: 우리는 움직이는 동행 우주 정지 좌표계에 비례하여 이동하고 있습니다 ... 약 371km / s에서 별자리 leo
쪽으로. "
sentences:
- 두 마리의 독수리가 가지에 앉는다.
- 다른 물체와는 관련이 없는 '정지'는 없다.
- 소녀는 버스의 열린 문 앞에 서 있다.
- source_sentence: 숲에는 개들이 있다.
sentences:
- 양을 보는 아이들.
- 여왕의 배우자를 "왕"이라고 부르지 않는 것은 아주 좋은 이유가 있다. 왜냐하면 그들은 왕이 아니기 때문이다.
- 개들은 숲속에 혼자 있다.
- source_sentence: '첫째, 두 가지 다른 종류의 대시가 있다는 것을 알아야 합니다 : en 대시와 em 대시.'
sentences:
- 그들은 그 물건들을 집 주변에 두고 가거나 집의 정리를 해칠 의도가 없다.
- 세미콜론은 혼자 있을 수 있는 문장에 참여하는데 사용되지만, 그들의 관계를 강조하기 위해 결합됩니다.
- 그의 남동생이 지켜보는 동안 집 앞에서 트럼펫을 연주하는 금발의 아이.
- source_sentence: 한 여성이 생선 껍질을 벗기고 있다.
sentences:
- 한 남자가 수영장으로 뛰어들었다.
- 한 여성이 프라이팬에 노란 혼합물을 부어 넣고 있다.
- 두 마리의 갈색 개가 눈 속에서 서로 놀고 있다.
- source_sentence: 버스가 바쁜 길을 따라 운전한다.
sentences:
- 우리와 같은 태양계가 은하계 밖에서 존재할 수도 있을 것입니다.
- 그 여자는 데이트하러 가는 중이다.
- 녹색 버스가 도로를 따라 내려간다.
datasets:
- x2bee/misc_sts_pairs_v2_kor_kosimcse
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_euclidean
- spearman_euclidean
- pearson_manhattan
- spearman_manhattan
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
model-index:
- name: SentenceTransformer based on x2bee/ModernBERT-SimCSE-multitask_v03
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts_dev
metrics:
- type: pearson_cosine
value: 0.8319192467999278
name: Pearson Cosine
- type: spearman_cosine
value: 0.8396159085327265
name: Spearman Cosine
- type: pearson_euclidean
value: 0.8198226408074469
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8285927601564604
name: Spearman Euclidean
- type: pearson_manhattan
value: 0.8199114649719743
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8295556212626334
name: Spearman Manhattan
- type: pearson_dot
value: 0.7234705763545461
name: Pearson Dot
- type: spearman_dot
value: 0.7094397491074207
name: Spearman Dot
- type: pearson_max
value: 0.8319192467999278
name: Pearson Max
- type: spearman_max
value: 0.8396159085327265
name: Spearman Max
---
# SentenceTransformer based on x2bee/ModernBERT-SimCSE-multitask_v03
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [x2bee/ModernBERT-SimCSE-multitask_v03](https://huggingface.co/x2bee/ModernBERT-SimCSE-multitask_v03) on the [misc_sts_pairs_v2_kor_kosimcse](https://huggingface.co/datasets/x2bee/misc_sts_pairs_v2_kor_kosimcse) 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:** [x2bee/ModernBERT-SimCSE-multitask_v03](https://huggingface.co/x2bee/ModernBERT-SimCSE-multitask_v03)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [misc_sts_pairs_v2_kor_kosimcse](https://huggingface.co/datasets/x2bee/misc_sts_pairs_v2_kor_kosimcse)
### 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': 512, 'do_lower_case': False}) with Transformer model: ModernBertModel
(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})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## 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("x2bee/ModernBERT-SimCSE-multitask_v03-beta")
# Run inference
sentences = [
'버스가 바쁜 길을 따라 운전한다.',
'녹색 버스가 도로를 따라 내려간다.',
'그 여자는 데이트하러 가는 중이다.',
]
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
#### 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 | 0.8319 |
| spearman_cosine | 0.8396 |
| pearson_euclidean | 0.8198 |
| spearman_euclidean | 0.8286 |
| pearson_manhattan | 0.8199 |
| spearman_manhattan | 0.8296 |
| pearson_dot | 0.7235 |
| spearman_dot | 0.7094 |
| pearson_max | 0.8319 |
| **spearman_max** | **0.8396** |
## Training Details
### Training Dataset
#### misc_sts_pairs_v2_kor_kosimcse
* Dataset: [misc_sts_pairs_v2_kor_kosimcse](https://huggingface.co/datasets/x2bee/misc_sts_pairs_v2_kor_kosimcse) at [e747415](https://huggingface.co/datasets/x2bee/misc_sts_pairs_v2_kor_kosimcse/tree/e747415cfe9ff51d1c1550b8a07e5014c01dea59)
* Size: 449,904 training samples
* Columns: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------|
| type | string | string | float |
| details | - min: 6 tokens
- mean: 18.3 tokens
- max: 69 tokens
| - min: 6 tokens
- mean: 18.69 tokens
- max: 66 tokens
| - min: 0.11
- mean: 0.77
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------|:-------------------------------------------|:--------------------------------|
| 주홍글씨는 언제 출판되었습니까? | 《주홍글씨》는 몇 년에 출판되었습니까? | 0.8638778924942017 |
| 폴란드에서 빨간색과 흰색은 무엇을 의미합니까? | 폴란드 국기의 색상은 무엇입니까? | 0.6773715019226074 |
| 노르만인들은 방어를 위해 모트와 베일리 성을 어떻게 사용했는가? | 11세기에는 어떻게 모트와 베일리 성을 만들었습니까? | 0.7460665702819824 |
* Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 1,500 evaluation samples
* Columns: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | - min: 7 tokens
- mean: 20.38 tokens
- max: 52 tokens
| - min: 6 tokens
- mean: 20.52 tokens
- max: 54 tokens
| - min: 0.0
- mean: 0.42
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------|:------------------------------------|:------------------|
| 안전모를 가진 한 남자가 춤을 추고 있다. | 안전모를 쓴 한 남자가 춤을 추고 있다. | 1.0 |
| 어린아이가 말을 타고 있다. | 아이가 말을 타고 있다. | 0.95 |
| 한 남자가 뱀에게 쥐를 먹이고 있다. | 남자가 뱀에게 쥐를 먹이고 있다. | 1.0 |
* Loss: [CosineSimilarityLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `overwrite_output_dir`: True
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 8e-05
- `num_train_epochs`: 2.0
- `warmup_ratio`: 0.2
- `push_to_hub`: True
- `hub_model_id`: x2bee/ModernBERT-SimCSE-multitask_v03-beta
- `hub_strategy`: checkpoint
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: True
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 8e-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`: 2.0
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: x2bee/ModernBERT-SimCSE-multitask_v03-beta
- `hub_strategy`: checkpoint
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | Validation Loss | sts_dev_spearman_max |
|:------:|:----:|:-------------:|:---------------:|:--------------------:|
| 0.0028 | 10 | 0.0216 | - | - |
| 0.0057 | 20 | 0.0204 | - | - |
| 0.0085 | 30 | 0.0194 | - | - |
| 0.0114 | 40 | 0.0195 | - | - |
| 0.0142 | 50 | 0.0182 | - | - |
| 0.0171 | 60 | 0.0161 | - | - |
| 0.0199 | 70 | 0.015 | - | - |
| 0.0228 | 80 | 0.0153 | - | - |
| 0.0256 | 90 | 0.0137 | - | - |
| 0.0285 | 100 | 0.014 | - | - |
| 0.0313 | 110 | 0.0122 | - | - |
| 0.0341 | 120 | 0.0114 | - | - |
| 0.0370 | 130 | 0.0109 | - | - |
| 0.0398 | 140 | 0.0097 | - | - |
| 0.0427 | 150 | 0.0085 | - | - |
| 0.0455 | 160 | 0.0084 | - | - |
| 0.0484 | 170 | 0.0083 | - | - |
| 0.0512 | 180 | 0.0078 | - | - |
| 0.0541 | 190 | 0.008 | - | - |
| 0.0569 | 200 | 0.0073 | - | - |
| 0.0597 | 210 | 0.0079 | - | - |
| 0.0626 | 220 | 0.0073 | - | - |
| 0.0654 | 230 | 0.0079 | - | - |
| 0.0683 | 240 | 0.0068 | - | - |
| 0.0711 | 250 | 0.0068 | 0.0333 | 0.8229 |
| 0.0740 | 260 | 0.0073 | - | - |
| 0.0768 | 270 | 0.0077 | - | - |
| 0.0797 | 280 | 0.0067 | - | - |
| 0.0825 | 290 | 0.007 | - | - |
| 0.0854 | 300 | 0.0065 | - | - |
| 0.0882 | 310 | 0.0072 | - | - |
| 0.0910 | 320 | 0.0068 | - | - |
| 0.0939 | 330 | 0.0064 | - | - |
| 0.0967 | 340 | 0.0074 | - | - |
| 0.0996 | 350 | 0.0071 | - | - |
| 0.1024 | 360 | 0.0065 | - | - |
| 0.1053 | 370 | 0.0067 | - | - |
| 0.1081 | 380 | 0.0063 | - | - |
| 0.1110 | 390 | 0.0062 | - | - |
| 0.1138 | 400 | 0.0068 | - | - |
| 0.1166 | 410 | 0.0064 | - | - |
| 0.1195 | 420 | 0.0064 | - | - |
| 0.1223 | 430 | 0.0064 | - | - |
| 0.1252 | 440 | 0.0074 | - | - |
| 0.1280 | 450 | 0.0069 | - | - |
| 0.1309 | 460 | 0.0065 | - | - |
| 0.1337 | 470 | 0.0067 | - | - |
| 0.1366 | 480 | 0.0068 | - | - |
| 0.1394 | 490 | 0.0057 | - | - |
| 0.1423 | 500 | 0.0065 | 0.0343 | 0.8284 |
| 0.1451 | 510 | 0.0069 | - | - |
| 0.1479 | 520 | 0.0068 | - | - |
| 0.1508 | 530 | 0.0065 | - | - |
| 0.1536 | 540 | 0.0065 | - | - |
| 0.1565 | 550 | 0.0063 | - | - |
| 0.1593 | 560 | 0.0058 | - | - |
| 0.1622 | 570 | 0.0064 | - | - |
| 0.1650 | 580 | 0.0062 | - | - |
| 0.1679 | 590 | 0.0061 | - | - |
| 0.1707 | 600 | 0.0062 | - | - |
| 0.1735 | 610 | 0.0057 | - | - |
| 0.1764 | 620 | 0.0066 | - | - |
| 0.1792 | 630 | 0.0061 | - | - |
| 0.1821 | 640 | 0.0054 | - | - |
| 0.1849 | 650 | 0.0066 | - | - |
| 0.1878 | 660 | 0.0059 | - | - |
| 0.1906 | 670 | 0.0063 | - | - |
| 0.1935 | 680 | 0.0065 | - | - |
| 0.1963 | 690 | 0.0065 | - | - |
| 0.1992 | 700 | 0.0058 | - | - |
| 0.2020 | 710 | 0.006 | - | - |
| 0.2048 | 720 | 0.0062 | - | - |
| 0.2077 | 730 | 0.0058 | - | - |
| 0.2105 | 740 | 0.0058 | - | - |
| 0.2134 | 750 | 0.0056 | 0.0356 | 0.8302 |
| 0.2162 | 760 | 0.0067 | - | - |
| 0.2191 | 770 | 0.0063 | - | - |
| 0.2219 | 780 | 0.0063 | - | - |
| 0.2248 | 790 | 0.0063 | - | - |
| 0.2276 | 800 | 0.0056 | - | - |
| 0.2304 | 810 | 0.0058 | - | - |
| 0.2333 | 820 | 0.0053 | - | - |
| 0.2361 | 830 | 0.0057 | - | - |
| 0.2390 | 840 | 0.0055 | - | - |
| 0.2418 | 850 | 0.0054 | - | - |
| 0.2447 | 860 | 0.0065 | - | - |
| 0.2475 | 870 | 0.0054 | - | - |
| 0.2504 | 880 | 0.0051 | - | - |
| 0.2532 | 890 | 0.0057 | - | - |
| 0.2561 | 900 | 0.0056 | - | - |
| 0.2589 | 910 | 0.0055 | - | - |
| 0.2617 | 920 | 0.0051 | - | - |
| 0.2646 | 930 | 0.0055 | - | - |
| 0.2674 | 940 | 0.0059 | - | - |
| 0.2703 | 950 | 0.005 | - | - |
| 0.2731 | 960 | 0.0058 | - | - |
| 0.2760 | 970 | 0.005 | - | - |
| 0.2788 | 980 | 0.0055 | - | - |
| 0.2817 | 990 | 0.0054 | - | - |
| 0.2845 | 1000 | 0.0055 | 0.0360 | 0.8319 |
| 0.2874 | 1010 | 0.0059 | - | - |
| 0.2902 | 1020 | 0.0049 | - | - |
| 0.2930 | 1030 | 0.0052 | - | - |
| 0.2959 | 1040 | 0.0051 | - | - |
| 0.2987 | 1050 | 0.006 | - | - |
| 0.3016 | 1060 | 0.0048 | - | - |
| 0.3044 | 1070 | 0.0055 | - | - |
| 0.3073 | 1080 | 0.0052 | - | - |
| 0.3101 | 1090 | 0.0051 | - | - |
| 0.3130 | 1100 | 0.0051 | - | - |
| 0.3158 | 1110 | 0.005 | - | - |
| 0.3186 | 1120 | 0.0054 | - | - |
| 0.3215 | 1130 | 0.0051 | - | - |
| 0.3243 | 1140 | 0.0054 | - | - |
| 0.3272 | 1150 | 0.0056 | - | - |
| 0.3300 | 1160 | 0.0053 | - | - |
| 0.3329 | 1170 | 0.0052 | - | - |
| 0.3357 | 1180 | 0.0051 | - | - |
| 0.3386 | 1190 | 0.0051 | - | - |
| 0.3414 | 1200 | 0.0048 | - | - |
| 0.3443 | 1210 | 0.005 | - | - |
| 0.3471 | 1220 | 0.0055 | - | - |
| 0.3499 | 1230 | 0.0049 | - | - |
| 0.3528 | 1240 | 0.0053 | - | - |
| 0.3556 | 1250 | 0.0052 | 0.0364 | 0.8330 |
| 0.3585 | 1260 | 0.0051 | - | - |
| 0.3613 | 1270 | 0.005 | - | - |
| 0.3642 | 1280 | 0.005 | - | - |
| 0.3670 | 1290 | 0.0045 | - | - |
| 0.3699 | 1300 | 0.0055 | - | - |
| 0.3727 | 1310 | 0.0049 | - | - |
| 0.3755 | 1320 | 0.0049 | - | - |
| 0.3784 | 1330 | 0.0053 | - | - |
| 0.3812 | 1340 | 0.005 | - | - |
| 0.3841 | 1350 | 0.0048 | - | - |
| 0.3869 | 1360 | 0.0049 | - | - |
| 0.3898 | 1370 | 0.0046 | - | - |
| 0.3926 | 1380 | 0.0049 | - | - |
| 0.3955 | 1390 | 0.0052 | - | - |
| 0.3983 | 1400 | 0.005 | - | - |
| 0.4012 | 1410 | 0.0052 | - | - |
| 0.4040 | 1420 | 0.0052 | - | - |
| 0.4068 | 1430 | 0.0045 | - | - |
| 0.4097 | 1440 | 0.0046 | - | - |
| 0.4125 | 1450 | 0.0056 | - | - |
| 0.4154 | 1460 | 0.0056 | - | - |
| 0.4182 | 1470 | 0.005 | - | - |
| 0.4211 | 1480 | 0.0051 | - | - |
| 0.4239 | 1490 | 0.0049 | - | - |
| 0.4268 | 1500 | 0.0048 | 0.0374 | 0.8334 |
| 0.4296 | 1510 | 0.0053 | - | - |
| 0.4324 | 1520 | 0.0054 | - | - |
| 0.4353 | 1530 | 0.0048 | - | - |
| 0.4381 | 1540 | 0.005 | - | - |
| 0.4410 | 1550 | 0.0045 | - | - |
| 0.4438 | 1560 | 0.0046 | - | - |
| 0.4467 | 1570 | 0.0045 | - | - |
| 0.4495 | 1580 | 0.0049 | - | - |
| 0.4524 | 1590 | 0.0048 | - | - |
| 0.4552 | 1600 | 0.005 | - | - |
| 0.4581 | 1610 | 0.0045 | - | - |
| 0.4609 | 1620 | 0.0049 | - | - |
| 0.4637 | 1630 | 0.0044 | - | - |
| 0.4666 | 1640 | 0.0048 | - | - |
| 0.4694 | 1650 | 0.0049 | - | - |
| 0.4723 | 1660 | 0.0048 | - | - |
| 0.4751 | 1670 | 0.0051 | - | - |
| 0.4780 | 1680 | 0.0047 | - | - |
| 0.4808 | 1690 | 0.0048 | - | - |
| 0.4837 | 1700 | 0.0047 | - | - |
| 0.4865 | 1710 | 0.0044 | - | - |
| 0.4893 | 1720 | 0.0049 | - | - |
| 0.4922 | 1730 | 0.0049 | - | - |
| 0.4950 | 1740 | 0.0051 | - | - |
| 0.4979 | 1750 | 0.0043 | 0.0392 | 0.8352 |
| 0.5007 | 1760 | 0.0043 | - | - |
| 0.5036 | 1770 | 0.0045 | - | - |
| 0.5064 | 1780 | 0.0046 | - | - |
| 0.5093 | 1790 | 0.0042 | - | - |
| 0.5121 | 1800 | 0.0047 | - | - |
| 0.5150 | 1810 | 0.0047 | - | - |
| 0.5178 | 1820 | 0.0046 | - | - |
| 0.5206 | 1830 | 0.0044 | - | - |
| 0.5235 | 1840 | 0.0046 | - | - |
| 0.5263 | 1850 | 0.0047 | - | - |
| 0.5292 | 1860 | 0.0044 | - | - |
| 0.5320 | 1870 | 0.0047 | - | - |
| 0.5349 | 1880 | 0.0049 | - | - |
| 0.5377 | 1890 | 0.0049 | - | - |
| 0.5406 | 1900 | 0.0047 | - | - |
| 0.5434 | 1910 | 0.0045 | - | - |
| 0.5462 | 1920 | 0.0044 | - | - |
| 0.5491 | 1930 | 0.0048 | - | - |
| 0.5519 | 1940 | 0.0041 | - | - |
| 0.5548 | 1950 | 0.004 | - | - |
| 0.5576 | 1960 | 0.0048 | - | - |
| 0.5605 | 1970 | 0.0042 | - | - |
| 0.5633 | 1980 | 0.0048 | - | - |
| 0.5662 | 1990 | 0.0045 | - | - |
| 0.5690 | 2000 | 0.0043 | 0.0375 | 0.8359 |
| 0.5719 | 2010 | 0.005 | - | - |
| 0.5747 | 2020 | 0.0049 | - | - |
| 0.5775 | 2030 | 0.0044 | - | - |
| 0.5804 | 2040 | 0.0045 | - | - |
| 0.5832 | 2050 | 0.0043 | - | - |
| 0.5861 | 2060 | 0.0045 | - | - |
| 0.5889 | 2070 | 0.004 | - | - |
| 0.5918 | 2080 | 0.0042 | - | - |
| 0.5946 | 2090 | 0.0044 | - | - |
| 0.5975 | 2100 | 0.0043 | - | - |
| 0.6003 | 2110 | 0.0041 | - | - |
| 0.6032 | 2120 | 0.0046 | - | - |
| 0.6060 | 2130 | 0.0048 | - | - |
| 0.6088 | 2140 | 0.0048 | - | - |
| 0.6117 | 2150 | 0.0041 | - | - |
| 0.6145 | 2160 | 0.0044 | - | - |
| 0.6174 | 2170 | 0.0045 | - | - |
| 0.6202 | 2180 | 0.0044 | - | - |
| 0.6231 | 2190 | 0.0044 | - | - |
| 0.6259 | 2200 | 0.0046 | - | - |
| 0.6288 | 2210 | 0.0048 | - | - |
| 0.6316 | 2220 | 0.0045 | - | - |
| 0.6344 | 2230 | 0.004 | - | - |
| 0.6373 | 2240 | 0.0041 | - | - |
| 0.6401 | 2250 | 0.0044 | 0.0391 | 0.8369 |
| 0.6430 | 2260 | 0.0044 | - | - |
| 0.6458 | 2270 | 0.0045 | - | - |
| 0.6487 | 2280 | 0.0041 | - | - |
| 0.6515 | 2290 | 0.0042 | - | - |
| 0.6544 | 2300 | 0.0043 | - | - |
| 0.6572 | 2310 | 0.004 | - | - |
| 0.6601 | 2320 | 0.0042 | - | - |
| 0.6629 | 2330 | 0.0041 | - | - |
| 0.6657 | 2340 | 0.0045 | - | - |
| 0.6686 | 2350 | 0.0045 | - | - |
| 0.6714 | 2360 | 0.0042 | - | - |
| 0.6743 | 2370 | 0.0045 | - | - |
| 0.6771 | 2380 | 0.0044 | - | - |
| 0.6800 | 2390 | 0.0044 | - | - |
| 0.6828 | 2400 | 0.0041 | - | - |
| 0.6857 | 2410 | 0.0045 | - | - |
| 0.6885 | 2420 | 0.0046 | - | - |
| 0.6913 | 2430 | 0.0041 | - | - |
| 0.6942 | 2440 | 0.0048 | - | - |
| 0.6970 | 2450 | 0.0041 | - | - |
| 0.6999 | 2460 | 0.0043 | - | - |
| 0.7027 | 2470 | 0.0043 | - | - |
| 0.7056 | 2480 | 0.0037 | - | - |
| 0.7084 | 2490 | 0.0042 | - | - |
| 0.7113 | 2500 | 0.0043 | 0.0405 | 0.8365 |
| 0.7141 | 2510 | 0.0045 | - | - |
| 0.7170 | 2520 | 0.0044 | - | - |
| 0.7198 | 2530 | 0.0042 | - | - |
| 0.7226 | 2540 | 0.0042 | - | - |
| 0.7255 | 2550 | 0.0041 | - | - |
| 0.7283 | 2560 | 0.0042 | - | - |
| 0.7312 | 2570 | 0.0041 | - | - |
| 0.7340 | 2580 | 0.0042 | - | - |
| 0.7369 | 2590 | 0.0041 | - | - |
| 0.7397 | 2600 | 0.0047 | - | - |
| 0.7426 | 2610 | 0.0038 | - | - |
| 0.7454 | 2620 | 0.0041 | - | - |
| 0.7482 | 2630 | 0.0042 | - | - |
| 0.7511 | 2640 | 0.0042 | - | - |
| 0.7539 | 2650 | 0.0042 | - | - |
| 0.7568 | 2660 | 0.0041 | - | - |
| 0.7596 | 2670 | 0.0042 | - | - |
| 0.7625 | 2680 | 0.0044 | - | - |
| 0.7653 | 2690 | 0.0039 | - | - |
| 0.7682 | 2700 | 0.0037 | - | - |
| 0.7710 | 2710 | 0.0044 | - | - |
| 0.7739 | 2720 | 0.0043 | - | - |
| 0.7767 | 2730 | 0.0042 | - | - |
| 0.7795 | 2740 | 0.0041 | - | - |
| 0.7824 | 2750 | 0.0039 | 0.0387 | 0.8376 |
| 0.7852 | 2760 | 0.0047 | - | - |
| 0.7881 | 2770 | 0.004 | - | - |
| 0.7909 | 2780 | 0.0039 | - | - |
| 0.7938 | 2790 | 0.0039 | - | - |
| 0.7966 | 2800 | 0.0039 | - | - |
| 0.7995 | 2810 | 0.0039 | - | - |
| 0.8023 | 2820 | 0.0039 | - | - |
| 0.8051 | 2830 | 0.0041 | - | - |
| 0.8080 | 2840 | 0.0037 | - | - |
| 0.8108 | 2850 | 0.0044 | - | - |
| 0.8137 | 2860 | 0.0043 | - | - |
| 0.8165 | 2870 | 0.0041 | - | - |
| 0.8194 | 2880 | 0.0043 | - | - |
| 0.8222 | 2890 | 0.0039 | - | - |
| 0.8251 | 2900 | 0.0041 | - | - |
| 0.8279 | 2910 | 0.0044 | - | - |
| 0.8308 | 2920 | 0.004 | - | - |
| 0.8336 | 2930 | 0.0042 | - | - |
| 0.8364 | 2940 | 0.0039 | - | - |
| 0.8393 | 2950 | 0.004 | - | - |
| 0.8421 | 2960 | 0.0042 | - | - |
| 0.8450 | 2970 | 0.004 | - | - |
| 0.8478 | 2980 | 0.0039 | - | - |
| 0.8507 | 2990 | 0.0037 | - | - |
| 0.8535 | 3000 | 0.0039 | 0.0386 | 0.8386 |
| 0.8564 | 3010 | 0.0041 | - | - |
| 0.8592 | 3020 | 0.0043 | - | - |
| 0.8621 | 3030 | 0.0041 | - | - |
| 0.8649 | 3040 | 0.0041 | - | - |
| 0.8677 | 3050 | 0.0043 | - | - |
| 0.8706 | 3060 | 0.0042 | - | - |
| 0.8734 | 3070 | 0.0039 | - | - |
| 0.8763 | 3080 | 0.004 | - | - |
| 0.8791 | 3090 | 0.0039 | - | - |
| 0.8820 | 3100 | 0.0039 | - | - |
| 0.8848 | 3110 | 0.004 | - | - |
| 0.8877 | 3120 | 0.0039 | - | - |
| 0.8905 | 3130 | 0.0038 | - | - |
| 0.8933 | 3140 | 0.0036 | - | - |
| 0.8962 | 3150 | 0.0039 | - | - |
| 0.8990 | 3160 | 0.0039 | - | - |
| 0.9019 | 3170 | 0.0038 | - | - |
| 0.9047 | 3180 | 0.0039 | - | - |
| 0.9076 | 3190 | 0.0041 | - | - |
| 0.9104 | 3200 | 0.004 | - | - |
| 0.9133 | 3210 | 0.0041 | - | - |
| 0.9161 | 3220 | 0.0042 | - | - |
| 0.9190 | 3230 | 0.004 | - | - |
| 0.9218 | 3240 | 0.0041 | - | - |
| 0.9246 | 3250 | 0.0041 | 0.0420 | 0.8408 |
| 0.9275 | 3260 | 0.0041 | - | - |
| 0.9303 | 3270 | 0.004 | - | - |
| 0.9332 | 3280 | 0.0042 | - | - |
| 0.9360 | 3290 | 0.004 | - | - |
| 0.9389 | 3300 | 0.0037 | - | - |
| 0.9417 | 3310 | 0.0038 | - | - |
| 0.9446 | 3320 | 0.0039 | - | - |
| 0.9474 | 3330 | 0.004 | - | - |
| 0.9502 | 3340 | 0.0037 | - | - |
| 0.9531 | 3350 | 0.0038 | - | - |
| 0.9559 | 3360 | 0.0037 | - | - |
| 0.9588 | 3370 | 0.0042 | - | - |
| 0.9616 | 3380 | 0.0042 | - | - |
| 0.9645 | 3390 | 0.0042 | - | - |
| 0.9673 | 3400 | 0.0037 | - | - |
| 0.9702 | 3410 | 0.0038 | - | - |
| 0.9730 | 3420 | 0.0039 | - | - |
| 0.9759 | 3430 | 0.0038 | - | - |
| 0.9787 | 3440 | 0.0041 | - | - |
| 0.9815 | 3450 | 0.004 | - | - |
| 0.9844 | 3460 | 0.0039 | - | - |
| 0.9872 | 3470 | 0.0036 | - | - |
| 0.9901 | 3480 | 0.0037 | - | - |
| 0.9929 | 3490 | 0.0039 | - | - |
| 0.9958 | 3500 | 0.0036 | 0.0403 | 0.8396 |