---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:356381
- loss:CategoricalContrastiveLoss
widget:
- source_sentence: 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
sentences:
- 科目：コンクリート。名称：免震上部ｺﾝｸﾘｰﾄ。
- 科目：コンクリート。名称：立上り壁ｺﾝｸﾘｰﾄ。
- 科目：コンクリート。名称：地上部ｺﾝｸﾘｰﾄ。
- source_sentence: 科目：コンクリート。名称：高流動ｺﾝｸﾘｰﾄ。
sentences:
- 科目：タイル。名称：踊場床ﾀｲﾙ張り。
- 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。
- 科目：タイル。名称：海街ﾃﾞｯｷ床ﾀｲﾙ。
- source_sentence: 科目：コンクリート。名称：免震下部鉄筋ｺﾝｸﾘｰﾄ。
sentences:
- 科目：コンクリート。名称：捨てｺﾝｸﾘｰﾄ。
- 科目：コンクリート。名称：基礎ｺﾝｸﾘｰﾄ。
- 科目：コンクリート。名称：地上部ｺﾝｸﾘｰﾄ。
- source_sentence: 科目：タイル。名称：汚垂ﾀｲﾙ。
sentences:
- 科目：コンクリート。名称：構造体強度補正。
- 科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。
- 科目：タイル。名称：段鼻磁器質ﾀｲﾙ。
- source_sentence: 科目：タイル。名称：ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ。
sentences:
- 科目：タイル。名称：段鼻ﾀｲﾙ。
- 科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。
- 科目：タイル。名称：風除床磁器質ﾀｲﾙ。
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. 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
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
### 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: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_0_8_5")
# Run inference
sentences = [
'科目：タイル。名称：ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ。',
'科目：タイル。名称：屋外階段踊場ﾀｲﾙ張り。',
'科目：タイル。名称：段鼻ﾀｲﾙ。',
]
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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 356,381 training samples
* Columns: sentence1, sentence2, and label
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------|
| type | string | string | int |
| details | - min: 11 tokens
- mean: 13.78 tokens
- max: 19 tokens
| - min: 11 tokens
- mean: 14.8 tokens
- max: 23 tokens
| - 0: ~74.10%
- 1: ~2.60%
- 2: ~23.30%
|
* Samples:
| sentence1 | sentence2 | label |
|:-----------------------------------------|:-------------------------------------------------|:---------------|
| 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震BPL下部充填ｺﾝｸﾘｰﾄ打設手間。 | 0 |
| 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震下部ｺﾝｸﾘｰﾄ打設手間。 | 0 |
| 科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。 | 科目：コンクリート。名称：免震下部（外周基礎梁）ｺﾝｸﾘｰﾄ打設手間。 | 0 |
* Loss: sentence_transformer_lib.categorical_constrastive_loss.CategoricalContrastiveLoss
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 4
- `warmup_ratio`: 0.2
- `fp16`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `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`: 0.2
- `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`: 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`: 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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.0072 | 10 | 0.2157 |
| 0.0144 | 20 | 0.1965 |
| 0.0215 | 30 | 0.164 |
| 0.0287 | 40 | 0.1199 |
| 0.0359 | 50 | 0.0913 |
| 0.0431 | 60 | 0.0687 |
| 0.0503 | 70 | 0.0462 |
| 0.0574 | 80 | 0.0459 |
| 0.0646 | 90 | 0.0424 |
| 0.0718 | 100 | 0.0416 |
| 0.0790 | 110 | 0.0377 |
| 0.0861 | 120 | 0.0472 |
| 0.0933 | 130 | 0.0437 |
| 0.1005 | 140 | 0.0332 |
| 0.1077 | 150 | 0.0411 |
| 0.1149 | 160 | 0.0361 |
| 0.1220 | 170 | 0.037 |
| 0.1292 | 180 | 0.0325 |
| 0.1364 | 190 | 0.0386 |
| 0.1436 | 200 | 0.0398 |
| 0.1508 | 210 | 0.0415 |
| 0.1579 | 220 | 0.0327 |
| 0.1651 | 230 | 0.0425 |
| 0.1723 | 240 | 0.0437 |
| 0.1795 | 250 | 0.0365 |
| 0.1866 | 260 | 0.028 |
| 0.1938 | 270 | 0.0412 |
| 0.2010 | 280 | 0.0424 |
| 0.2082 | 290 | 0.0382 |
| 0.2154 | 300 | 0.0282 |
| 0.2225 | 310 | 0.0358 |
| 0.2297 | 320 | 0.0311 |
| 0.2369 | 330 | 0.0339 |
| 0.2441 | 340 | 0.0313 |
| 0.2513 | 350 | 0.0333 |
| 0.2584 | 360 | 0.0238 |
| 0.2656 | 370 | 0.0367 |
| 0.2728 | 380 | 0.0295 |
| 0.2800 | 390 | 0.0286 |
| 0.2872 | 400 | 0.0358 |
| 0.2943 | 410 | 0.0288 |
| 0.3015 | 420 | 0.032 |
| 0.3087 | 430 | 0.0323 |
| 0.3159 | 440 | 0.0284 |
| 0.3230 | 450 | 0.0297 |
| 0.3302 | 460 | 0.0266 |
| 0.3374 | 470 | 0.0317 |
| 0.3446 | 480 | 0.0298 |
| 0.3518 | 490 | 0.0272 |
| 0.3589 | 500 | 0.0307 |
| 0.3661 | 510 | 0.0337 |
| 0.3733 | 520 | 0.0268 |
| 0.3805 | 530 | 0.0286 |
| 0.3877 | 540 | 0.0283 |
| 0.3948 | 550 | 0.0293 |
| 0.4020 | 560 | 0.0299 |
| 0.4092 | 570 | 0.0231 |
| 0.4164 | 580 | 0.0308 |
| 0.4235 | 590 | 0.0294 |
| 0.4307 | 600 | 0.0309 |
| 0.4379 | 610 | 0.0255 |
| 0.4451 | 620 | 0.0269 |
| 0.4523 | 630 | 0.0226 |
| 0.4594 | 640 | 0.028 |
| 0.4666 | 650 | 0.027 |
| 0.4738 | 660 | 0.0365 |
| 0.4810 | 670 | 0.0264 |
| 0.4882 | 680 | 0.0212 |
| 0.4953 | 690 | 0.0311 |
| 0.5025 | 700 | 0.0266 |
| 0.5097 | 710 | 0.0203 |
| 0.5169 | 720 | 0.0207 |
| 0.5240 | 730 | 0.0348 |
| 0.5312 | 740 | 0.0227 |
| 0.5384 | 750 | 0.0237 |
| 0.5456 | 760 | 0.0201 |
| 0.5528 | 770 | 0.0257 |
| 0.5599 | 780 | 0.0266 |
| 0.5671 | 790 | 0.0276 |
| 0.5743 | 800 | 0.0271 |
| 0.5815 | 810 | 0.0238 |
| 0.5887 | 820 | 0.0217 |
| 0.5958 | 830 | 0.018 |
| 0.6030 | 840 | 0.0223 |
| 0.6102 | 850 | 0.0208 |
| 0.6174 | 860 | 0.0248 |
| 0.6246 | 870 | 0.0264 |
| 0.6317 | 880 | 0.0198 |
| 0.6389 | 890 | 0.0215 |
| 0.6461 | 900 | 0.0193 |
| 0.6533 | 910 | 0.0191 |
| 0.6604 | 920 | 0.0205 |
| 0.6676 | 930 | 0.0219 |
| 0.6748 | 940 | 0.0229 |
| 0.6820 | 950 | 0.0234 |
| 0.6892 | 960 | 0.0225 |
| 0.6963 | 970 | 0.0185 |
| 0.7035 | 980 | 0.0174 |
| 0.7107 | 990 | 0.0169 |
| 0.7179 | 1000 | 0.0218 |
| 0.7251 | 1010 | 0.0141 |
| 0.7322 | 1020 | 0.0221 |
| 0.7394 | 1030 | 0.0185 |
| 0.7466 | 1040 | 0.0219 |
| 0.7538 | 1050 | 0.0183 |
| 0.7609 | 1060 | 0.0153 |
| 0.7681 | 1070 | 0.0168 |
| 0.7753 | 1080 | 0.0177 |
| 0.7825 | 1090 | 0.0177 |
| 0.7897 | 1100 | 0.0179 |
| 0.7968 | 1110 | 0.0181 |
| 0.8040 | 1120 | 0.02 |
| 0.8112 | 1130 | 0.0186 |
| 0.8184 | 1140 | 0.0185 |
| 0.8256 | 1150 | 0.0162 |
| 0.8327 | 1160 | 0.0156 |
| 0.8399 | 1170 | 0.0141 |
| 0.8471 | 1180 | 0.0152 |
| 0.8543 | 1190 | 0.0146 |
| 0.8615 | 1200 | 0.018 |
| 0.8686 | 1210 | 0.0194 |
| 0.8758 | 1220 | 0.0148 |
| 0.8830 | 1230 | 0.0183 |
| 0.8902 | 1240 | 0.0124 |
| 0.8973 | 1250 | 0.0141 |
| 0.9045 | 1260 | 0.0193 |
| 0.9117 | 1270 | 0.0169 |
| 0.9189 | 1280 | 0.0165 |
| 0.9261 | 1290 | 0.0101 |
| 0.9332 | 1300 | 0.0195 |
| 0.9404 | 1310 | 0.0168 |
| 0.9476 | 1320 | 0.0207 |
| 0.9548 | 1330 | 0.018 |
| 0.9620 | 1340 | 0.0116 |
| 0.9691 | 1350 | 0.0175 |
| 0.9763 | 1360 | 0.0138 |
| 0.9835 | 1370 | 0.0209 |
| 0.9907 | 1380 | 0.0145 |
| 0.9978 | 1390 | 0.0138 |
| 1.0050 | 1400 | 0.0123 |
| 1.0122 | 1410 | 0.0145 |
| 1.0194 | 1420 | 0.0135 |
| 1.0266 | 1430 | 0.0115 |
| 1.0337 | 1440 | 0.014 |
| 1.0409 | 1450 | 0.0106 |
| 1.0481 | 1460 | 0.0102 |
| 1.0553 | 1470 | 0.0133 |
| 1.0625 | 1480 | 0.008 |
| 1.0696 | 1490 | 0.0134 |
| 1.0768 | 1500 | 0.0106 |
| 1.0840 | 1510 | 0.0151 |
| 1.0912 | 1520 | 0.0168 |
| 1.0983 | 1530 | 0.0093 |
| 1.1055 | 1540 | 0.0132 |
| 1.1127 | 1550 | 0.0115 |
| 1.1199 | 1560 | 0.0096 |
| 1.1271 | 1570 | 0.012 |
| 1.1342 | 1580 | 0.0119 |
| 1.1414 | 1590 | 0.0108 |
| 1.1486 | 1600 | 0.013 |
| 1.1558 | 1610 | 0.0109 |
| 1.1630 | 1620 | 0.0131 |
| 1.1701 | 1630 | 0.0093 |
| 1.1773 | 1640 | 0.0126 |
| 1.1845 | 1650 | 0.009 |
| 1.1917 | 1660 | 0.0106 |
| 1.1989 | 1670 | 0.0102 |
| 1.2060 | 1680 | 0.0089 |
| 1.2132 | 1690 | 0.0096 |
| 1.2204 | 1700 | 0.0084 |
| 1.2276 | 1710 | 0.0099 |
| 1.2347 | 1720 | 0.0074 |
| 1.2419 | 1730 | 0.0131 |
| 1.2491 | 1740 | 0.0125 |
| 1.2563 | 1750 | 0.0102 |
| 1.2635 | 1760 | 0.0117 |
| 1.2706 | 1770 | 0.0099 |
| 1.2778 | 1780 | 0.0078 |
| 1.2850 | 1790 | 0.0095 |
| 1.2922 | 1800 | 0.0079 |
| 1.2994 | 1810 | 0.0069 |
| 1.3065 | 1820 | 0.0121 |
| 1.3137 | 1830 | 0.0101 |
| 1.3209 | 1840 | 0.0151 |
| 1.3281 | 1850 | 0.0107 |
| 1.3352 | 1860 | 0.0125 |
| 1.3424 | 1870 | 0.0111 |
| 1.3496 | 1880 | 0.0091 |
| 1.3568 | 1890 | 0.0082 |
| 1.3640 | 1900 | 0.0092 |
| 1.3711 | 1910 | 0.0107 |
| 1.3783 | 1920 | 0.0066 |
| 1.3855 | 1930 | 0.0141 |
| 1.3927 | 1940 | 0.0126 |
| 1.3999 | 1950 | 0.009 |
| 1.4070 | 1960 | 0.0116 |
| 1.4142 | 1970 | 0.0121 |
| 1.4214 | 1980 | 0.0098 |
| 1.4286 | 1990 | 0.0108 |
| 1.4358 | 2000 | 0.0103 |
| 1.4429 | 2010 | 0.0118 |
| 1.4501 | 2020 | 0.0143 |
| 1.4573 | 2030 | 0.0082 |
| 1.4645 | 2040 | 0.0077 |
| 1.4716 | 2050 | 0.0102 |
| 1.4788 | 2060 | 0.0093 |
| 1.4860 | 2070 | 0.0084 |
| 1.4932 | 2080 | 0.0105 |
| 1.5004 | 2090 | 0.0091 |
| 1.5075 | 2100 | 0.0094 |
| 1.5147 | 2110 | 0.0092 |
| 1.5219 | 2120 | 0.0117 |
| 1.5291 | 2130 | 0.0085 |
| 1.5363 | 2140 | 0.0069 |
| 1.5434 | 2150 | 0.0114 |
| 1.5506 | 2160 | 0.0077 |
| 1.5578 | 2170 | 0.0092 |
| 1.5650 | 2180 | 0.0093 |
| 1.5721 | 2190 | 0.0076 |
| 1.5793 | 2200 | 0.0098 |
| 1.5865 | 2210 | 0.01 |
| 1.5937 | 2220 | 0.01 |
| 1.6009 | 2230 | 0.0092 |
| 1.6080 | 2240 | 0.0096 |
| 1.6152 | 2250 | 0.0077 |
| 1.6224 | 2260 | 0.0147 |
| 1.6296 | 2270 | 0.0087 |
| 1.6368 | 2280 | 0.0106 |
| 1.6439 | 2290 | 0.007 |
| 1.6511 | 2300 | 0.0091 |
| 1.6583 | 2310 | 0.0083 |
| 1.6655 | 2320 | 0.0113 |
| 1.6726 | 2330 | 0.0076 |
| 1.6798 | 2340 | 0.0096 |
| 1.6870 | 2350 | 0.0087 |
| 1.6942 | 2360 | 0.0068 |
| 1.7014 | 2370 | 0.0064 |
| 1.7085 | 2380 | 0.0088 |
| 1.7157 | 2390 | 0.0052 |
| 1.7229 | 2400 | 0.0088 |
| 1.7301 | 2410 | 0.0068 |
| 1.7373 | 2420 | 0.0072 |
| 1.7444 | 2430 | 0.0076 |
| 1.7516 | 2440 | 0.0078 |
| 1.7588 | 2450 | 0.0066 |
| 1.7660 | 2460 | 0.0086 |
| 1.7732 | 2470 | 0.0051 |
| 1.7803 | 2480 | 0.0115 |
| 1.7875 | 2490 | 0.0059 |
| 1.7947 | 2500 | 0.0088 |
| 1.8019 | 2510 | 0.0078 |
| 1.8090 | 2520 | 0.0057 |
| 1.8162 | 2530 | 0.0076 |
| 1.8234 | 2540 | 0.0077 |
| 1.8306 | 2550 | 0.009 |
| 1.8378 | 2560 | 0.0073 |
| 1.8449 | 2570 | 0.009 |
| 1.8521 | 2580 | 0.0094 |
| 1.8593 | 2590 | 0.0068 |
| 1.8665 | 2600 | 0.0081 |
| 1.8737 | 2610 | 0.004 |
| 1.8808 | 2620 | 0.0077 |
| 1.8880 | 2630 | 0.0072 |
| 1.8952 | 2640 | 0.0061 |
| 1.9024 | 2650 | 0.0077 |
| 1.9095 | 2660 | 0.0074 |
| 1.9167 | 2670 | 0.0077 |
| 1.9239 | 2680 | 0.0073 |
| 1.9311 | 2690 | 0.0096 |
| 1.9383 | 2700 | 0.006 |
| 1.9454 | 2710 | 0.0092 |
| 1.9526 | 2720 | 0.005 |
| 1.9598 | 2730 | 0.0045 |
| 1.9670 | 2740 | 0.0071 |
| 1.9742 | 2750 | 0.0061 |
| 1.9813 | 2760 | 0.0073 |
| 1.9885 | 2770 | 0.0073 |
| 1.9957 | 2780 | 0.0067 |
| 2.0029 | 2790 | 0.0054 |
| 2.0101 | 2800 | 0.0044 |
| 2.0172 | 2810 | 0.0045 |
| 2.0244 | 2820 | 0.005 |
| 2.0316 | 2830 | 0.0066 |
| 2.0388 | 2840 | 0.0038 |
| 2.0459 | 2850 | 0.0051 |
| 2.0531 | 2860 | 0.0039 |
| 2.0603 | 2870 | 0.0051 |
| 2.0675 | 2880 | 0.0056 |
| 2.0747 | 2890 | 0.0054 |
| 2.0818 | 2900 | 0.0069 |
| 2.0890 | 2910 | 0.006 |
| 2.0962 | 2920 | 0.0074 |
| 2.1034 | 2930 | 0.0067 |
| 2.1106 | 2940 | 0.0044 |
| 2.1177 | 2950 | 0.0065 |
| 2.1249 | 2960 | 0.0066 |
| 2.1321 | 2970 | 0.0044 |
| 2.1393 | 2980 | 0.0041 |
| 2.1464 | 2990 | 0.0066 |
| 2.1536 | 3000 | 0.0046 |
| 2.1608 | 3010 | 0.0061 |
| 2.1680 | 3020 | 0.0039 |
| 2.1752 | 3030 | 0.0048 |
| 2.1823 | 3040 | 0.0059 |
| 2.1895 | 3050 | 0.0067 |
| 2.1967 | 3060 | 0.005 |
| 2.2039 | 3070 | 0.0028 |
| 2.2111 | 3080 | 0.0055 |
| 2.2182 | 3090 | 0.0032 |
| 2.2254 | 3100 | 0.0074 |
| 2.2326 | 3110 | 0.0052 |
| 2.2398 | 3120 | 0.0058 |
| 2.2469 | 3130 | 0.0067 |
| 2.2541 | 3140 | 0.0065 |
| 2.2613 | 3150 | 0.0036 |
| 2.2685 | 3160 | 0.005 |
| 2.2757 | 3170 | 0.0083 |
| 2.2828 | 3180 | 0.0038 |
| 2.2900 | 3190 | 0.0044 |
| 2.2972 | 3200 | 0.0057 |
| 2.3044 | 3210 | 0.0042 |
| 2.3116 | 3220 | 0.0037 |
| 2.3187 | 3230 | 0.0061 |
| 2.3259 | 3240 | 0.0038 |
| 2.3331 | 3250 | 0.0051 |
| 2.3403 | 3260 | 0.0076 |
| 2.3475 | 3270 | 0.005 |
| 2.3546 | 3280 | 0.0042 |
| 2.3618 | 3290 | 0.005 |
| 2.3690 | 3300 | 0.0077 |
| 2.3762 | 3310 | 0.0067 |
| 2.3833 | 3320 | 0.008 |
| 2.3905 | 3330 | 0.0077 |
| 2.3977 | 3340 | 0.0052 |
| 2.4049 | 3350 | 0.0055 |
| 2.4121 | 3360 | 0.0059 |
| 2.4192 | 3370 | 0.0042 |
| 2.4264 | 3380 | 0.0044 |
| 2.4336 | 3390 | 0.0055 |
| 2.4408 | 3400 | 0.0048 |
| 2.4480 | 3410 | 0.0035 |
| 2.4551 | 3420 | 0.0068 |
| 2.4623 | 3430 | 0.007 |
| 2.4695 | 3440 | 0.0059 |
| 2.4767 | 3450 | 0.0037 |
| 2.4838 | 3460 | 0.0049 |
| 2.4910 | 3470 | 0.0042 |
| 2.4982 | 3480 | 0.004 |
| 2.5054 | 3490 | 0.0033 |
| 2.5126 | 3500 | 0.004 |
| 2.5197 | 3510 | 0.0055 |
| 2.5269 | 3520 | 0.0057 |
| 2.5341 | 3530 | 0.0059 |
| 2.5413 | 3540 | 0.0031 |
| 2.5485 | 3550 | 0.0039 |
| 2.5556 | 3560 | 0.0046 |
| 2.5628 | 3570 | 0.0035 |
| 2.5700 | 3580 | 0.0037 |
| 2.5772 | 3590 | 0.0045 |
| 2.5844 | 3600 | 0.006 |
| 2.5915 | 3610 | 0.0058 |
| 2.5987 | 3620 | 0.0053 |
| 2.6059 | 3630 | 0.0045 |
| 2.6131 | 3640 | 0.0031 |
| 2.6202 | 3650 | 0.0063 |
| 2.6274 | 3660 | 0.004 |
| 2.6346 | 3670 | 0.0043 |
| 2.6418 | 3680 | 0.0055 |
| 2.6490 | 3690 | 0.0044 |
| 2.6561 | 3700 | 0.0025 |
| 2.6633 | 3710 | 0.0047 |
| 2.6705 | 3720 | 0.0043 |
| 2.6777 | 3730 | 0.0041 |
| 2.6849 | 3740 | 0.0064 |
| 2.6920 | 3750 | 0.0055 |
| 2.6992 | 3760 | 0.0038 |
| 2.7064 | 3770 | 0.0059 |
| 2.7136 | 3780 | 0.0059 |
| 2.7207 | 3790 | 0.0039 |
| 2.7279 | 3800 | 0.0051 |
| 2.7351 | 3810 | 0.0061 |
| 2.7423 | 3820 | 0.0029 |
| 2.7495 | 3830 | 0.0043 |
| 2.7566 | 3840 | 0.0044 |
| 2.7638 | 3850 | 0.0047 |
| 2.7710 | 3860 | 0.0041 |
| 2.7782 | 3870 | 0.0033 |
| 2.7854 | 3880 | 0.0028 |
| 2.7925 | 3890 | 0.0049 |
| 2.7997 | 3900 | 0.0048 |
| 2.8069 | 3910 | 0.0042 |
| 2.8141 | 3920 | 0.0047 |
| 2.8212 | 3930 | 0.0043 |
| 2.8284 | 3940 | 0.0034 |
| 2.8356 | 3950 | 0.0034 |
| 2.8428 | 3960 | 0.0036 |
| 2.8500 | 3970 | 0.0057 |
| 2.8571 | 3980 | 0.0067 |
| 2.8643 | 3990 | 0.0053 |
| 2.8715 | 4000 | 0.0045 |
| 2.8787 | 4010 | 0.0044 |
| 2.8859 | 4020 | 0.0045 |
| 2.8930 | 4030 | 0.0028 |
| 2.9002 | 4040 | 0.0032 |
| 2.9074 | 4050 | 0.0054 |
| 2.9146 | 4060 | 0.005 |
| 2.9218 | 4070 | 0.0039 |
| 2.9289 | 4080 | 0.003 |
| 2.9361 | 4090 | 0.0036 |
| 2.9433 | 4100 | 0.003 |
| 2.9505 | 4110 | 0.0052 |
| 2.9576 | 4120 | 0.0029 |
| 2.9648 | 4130 | 0.0038 |
| 2.9720 | 4140 | 0.0048 |
| 2.9792 | 4150 | 0.0046 |
| 2.9864 | 4160 | 0.005 |
| 2.9935 | 4170 | 0.0047 |
| 3.0007 | 4180 | 0.0048 |
| 3.0079 | 4190 | 0.0033 |
| 3.0151 | 4200 | 0.0026 |
| 3.0223 | 4210 | 0.0031 |
| 3.0294 | 4220 | 0.0043 |
| 3.0366 | 4230 | 0.0034 |
| 3.0438 | 4240 | 0.0038 |
| 3.0510 | 4250 | 0.0023 |
| 3.0581 | 4260 | 0.0036 |
| 3.0653 | 4270 | 0.0045 |
| 3.0725 | 4280 | 0.0028 |
| 3.0797 | 4290 | 0.0025 |
| 3.0869 | 4300 | 0.0036 |
| 3.0940 | 4310 | 0.0055 |
| 3.1012 | 4320 | 0.0041 |
| 3.1084 | 4330 | 0.0027 |
| 3.1156 | 4340 | 0.0048 |
| 3.1228 | 4350 | 0.0049 |
| 3.1299 | 4360 | 0.0028 |
| 3.1371 | 4370 | 0.0052 |
| 3.1443 | 4380 | 0.0029 |
| 3.1515 | 4390 | 0.0039 |
| 3.1587 | 4400 | 0.0029 |
| 3.1658 | 4410 | 0.0045 |
| 3.1730 | 4420 | 0.0031 |
| 3.1802 | 4430 | 0.004 |
| 3.1874 | 4440 | 0.0042 |
| 3.1945 | 4450 | 0.0039 |
| 3.2017 | 4460 | 0.0027 |
| 3.2089 | 4470 | 0.0031 |
| 3.2161 | 4480 | 0.0043 |
| 3.2233 | 4490 | 0.0027 |
| 3.2304 | 4500 | 0.0035 |
| 3.2376 | 4510 | 0.0034 |
| 3.2448 | 4520 | 0.0039 |
| 3.2520 | 4530 | 0.0026 |
| 3.2592 | 4540 | 0.0035 |
| 3.2663 | 4550 | 0.0041 |
| 3.2735 | 4560 | 0.0021 |
| 3.2807 | 4570 | 0.0032 |
| 3.2879 | 4580 | 0.0032 |
| 3.2950 | 4590 | 0.0026 |
| 3.3022 | 4600 | 0.0045 |
| 3.3094 | 4610 | 0.0046 |
| 3.3166 | 4620 | 0.0014 |
| 3.3238 | 4630 | 0.0026 |
| 3.3309 | 4640 | 0.0026 |
| 3.3381 | 4650 | 0.002 |
| 3.3453 | 4660 | 0.0043 |
| 3.3525 | 4670 | 0.0051 |
| 3.3597 | 4680 | 0.0041 |
| 3.3668 | 4690 | 0.0021 |
| 3.3740 | 4700 | 0.0059 |
| 3.3812 | 4710 | 0.006 |
| 3.3884 | 4720 | 0.0049 |
| 3.3955 | 4730 | 0.0035 |
| 3.4027 | 4740 | 0.004 |
| 3.4099 | 4750 | 0.0039 |
| 3.4171 | 4760 | 0.0024 |
| 3.4243 | 4770 | 0.0026 |
| 3.4314 | 4780 | 0.0038 |
| 3.4386 | 4790 | 0.0029 |
| 3.4458 | 4800 | 0.0045 |
| 3.4530 | 4810 | 0.0025 |
| 3.4602 | 4820 | 0.0031 |
| 3.4673 | 4830 | 0.0044 |
| 3.4745 | 4840 | 0.0018 |
| 3.4817 | 4850 | 0.0035 |
| 3.4889 | 4860 | 0.0031 |
| 3.4961 | 4870 | 0.0058 |
| 3.5032 | 4880 | 0.0032 |
| 3.5104 | 4890 | 0.0028 |
| 3.5176 | 4900 | 0.0029 |
| 3.5248 | 4910 | 0.0038 |
| 3.5319 | 4920 | 0.0026 |
| 3.5391 | 4930 | 0.0028 |
| 3.5463 | 4940 | 0.0034 |
| 3.5535 | 4950 | 0.0044 |
| 3.5607 | 4960 | 0.003 |
| 3.5678 | 4970 | 0.0028 |
| 3.5750 | 4980 | 0.0031 |
| 3.5822 | 4990 | 0.003 |
| 3.5894 | 5000 | 0.0028 |