---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:1310129
- loss:MultipleNegativesRankingLoss
base_model: unsloth/Qwen3-Embedding-0.6B
widget:
- source_sentence: 닥터브로너스 [페이셜&바디워시] 닥터브로너스 퓨어 캐스틸 솝 475ml 12종 택1
sentences:
- 露得清卸妆油
- Versace Man Eau Fraiche
- ピュアキャスティールソープ
- source_sentence: 베르사체 베르사체 맨오프레쉬 30ml 단품/기획 택1
sentences:
- ピーリングジェル
- Versace Bright
- Man Eau Fraiche single
- source_sentence: 랑방 랑방 루머 2 로즈 50ml
sentences:
- 캐스틸 솝 475ml
- 랑방 루머
- 防晒霜
- source_sentence: 케어존 케어존 데일리&패밀리 선크림 80ml (SPF50+/PA+++)
sentences:
- 伊丽莎白雅顿 100毫升
- Rumeur Rose perfume
- 패밀리 선크림
- source_sentence: 랑방 랑방 메리미 EDP 50ml
sentences:
- マリーミー EDP
- 浪凡 EDP
- ケアゾーン デイリー日焼け止め
datasets:
- dkqjrm/olive-product
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on unsloth/Qwen3-Embedding-0.6B
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [unsloth/Qwen3-Embedding-0.6B](https://huggingface.co/unsloth/Qwen3-Embedding-0.6B) on the [olive-product](https://huggingface.co/datasets/dkqjrm/olive-product) 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:** [unsloth/Qwen3-Embedding-0.6B](https://huggingface.co/unsloth/Qwen3-Embedding-0.6B)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [olive-product](https://huggingface.co/datasets/dkqjrm/olive-product)
### 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': 'PeftModelForFeatureExtraction'})
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, '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': True, '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("dkqjrm/lora_model")
# Run inference
sentences = [
'랑방 랑방 메리미 EDP 50ml',
'浪凡 EDP',
'マリーミー EDP',
]
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, 0.6852, 0.6339],
# [0.6852, 1.0000, 0.3744],
# [0.6339, 0.3744, 1.0000]])
```
## Training Details
### Training Dataset
#### olive-product
* Dataset: [olive-product](https://huggingface.co/datasets/dkqjrm/olive-product) at [8d1f081](https://huggingface.co/datasets/dkqjrm/olive-product/tree/8d1f0813721299cb95f6d5cc09b2ef9317e8d06c)
* Size: 1,310,129 training samples
* Columns: anchor and positive
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 16 tokens
- mean: 25.67 tokens
- max: 47 tokens
| - min: 2 tokens
- mean: 7.19 tokens
- max: 38 tokens
|
* Samples:
| anchor | positive |
|:-----------------------------------------|:-----------------------------------|
| 베르사체 베르사체 브라이트 크리스탈 50ml 택1 | 베르사체 브라이트 크리스탈 50ml 1 |
| 베르사체 베르사체 브라이트 크리스탈 50ml 택1 | 베르사체 브라이트 |
| 베르사체 베르사체 브라이트 크리스탈 50ml 택1 | 베르사체 크리스탈 |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim",
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 32
- `gradient_accumulation_steps`: 4
- `learning_rate`: 3e-05
- `num_train_epochs`: 2
- `lr_scheduler_type`: constant_with_warmup
- `warmup_ratio`: 0.03
- `fp16`: True
- `push_to_hub`: True
- `hub_model_id`: dkqjrm/qwen3-embedding-olive-lora
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 4
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-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
- `max_steps`: -1
- `lr_scheduler_type`: constant_with_warmup
- `lr_scheduler_kwargs`: None
- `warmup_ratio`: 0.03
- `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`: 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}
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: dkqjrm/qwen3-embedding-olive-lora
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
Click to expand
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0049 | 50 | 1.5027 |
| 0.0098 | 100 | 0.8366 |
| 0.0147 | 150 | 0.6713 |
| 0.0195 | 200 | 0.5863 |
| 0.0244 | 250 | 0.53 |
| 0.0293 | 300 | 0.4562 |
| 0.0342 | 350 | 0.4061 |
| 0.0391 | 400 | 0.3899 |
| 0.0440 | 450 | 0.3417 |
| 0.0488 | 500 | 0.3367 |
| 0.0537 | 550 | 0.2948 |
| 0.0586 | 600 | 0.281 |
| 0.0635 | 650 | 0.2808 |
| 0.0684 | 700 | 0.2414 |
| 0.0733 | 750 | 0.2448 |
| 0.0782 | 800 | 0.2307 |
| 0.0830 | 850 | 0.2174 |
| 0.0879 | 900 | 0.2129 |
| 0.0928 | 950 | 0.2139 |
| 0.0977 | 1000 | 0.198 |
| 0.1026 | 1050 | 0.1797 |
| 0.1075 | 1100 | 0.1923 |
| 0.1124 | 1150 | 0.1887 |
| 0.1172 | 1200 | 0.1789 |
| 0.1221 | 1250 | 0.1833 |
| 0.1270 | 1300 | 0.168 |
| 0.1319 | 1350 | 0.1683 |
| 0.1368 | 1400 | 0.1536 |
| 0.1417 | 1450 | 0.1632 |
| 0.1465 | 1500 | 0.155 |
| 0.1514 | 1550 | 0.1533 |
| 0.1563 | 1600 | 0.1442 |
| 0.1612 | 1650 | 0.1407 |
| 0.1661 | 1700 | 0.1396 |
| 0.1710 | 1750 | 0.1388 |
| 0.1759 | 1800 | 0.1375 |
| 0.1807 | 1850 | 0.1356 |
| 0.1856 | 1900 | 0.1335 |
| 0.1905 | 1950 | 0.1296 |
| 0.1954 | 2000 | 0.1281 |
| 0.2003 | 2050 | 0.1379 |
| 0.2052 | 2100 | 0.1213 |
| 0.2101 | 2150 | 0.1209 |
| 0.2149 | 2200 | 0.1142 |
| 0.2198 | 2250 | 0.1305 |
| 0.2247 | 2300 | 0.115 |
| 0.2296 | 2350 | 0.1125 |
| 0.2345 | 2400 | 0.1159 |
| 0.2394 | 2450 | 0.1131 |
| 0.2442 | 2500 | 0.1133 |
| 0.2491 | 2550 | 0.1126 |
| 0.2540 | 2600 | 0.109 |
| 0.2589 | 2650 | 0.1135 |
| 0.2638 | 2700 | 0.0986 |
| 0.2687 | 2750 | 0.1127 |
| 0.2736 | 2800 | 0.114 |
| 0.2784 | 2850 | 0.1079 |
| 0.2833 | 2900 | 0.1106 |
| 0.2882 | 2950 | 0.1112 |
| 0.2931 | 3000 | 0.1006 |
| 0.2980 | 3050 | 0.1051 |
| 0.3029 | 3100 | 0.1105 |
| 0.3078 | 3150 | 0.1046 |
| 0.3126 | 3200 | 0.1011 |
| 0.3175 | 3250 | 0.0962 |
| 0.3224 | 3300 | 0.1002 |
| 0.3273 | 3350 | 0.1066 |
| 0.3322 | 3400 | 0.0907 |
| 0.3371 | 3450 | 0.0894 |
| 0.3419 | 3500 | 0.1002 |
| 0.3468 | 3550 | 0.0894 |
| 0.3517 | 3600 | 0.0897 |
| 0.3566 | 3650 | 0.0995 |
| 0.3615 | 3700 | 0.0949 |
| 0.3664 | 3750 | 0.0914 |
| 0.3713 | 3800 | 0.0929 |
| 0.3761 | 3850 | 0.0841 |
| 0.3810 | 3900 | 0.0847 |
| 0.3859 | 3950 | 0.0964 |
| 0.3908 | 4000 | 0.0937 |
| 0.3957 | 4050 | 0.0874 |
| 0.4006 | 4100 | 0.0911 |
| 0.4055 | 4150 | 0.093 |
| 0.4103 | 4200 | 0.0867 |
| 0.4152 | 4250 | 0.0841 |
| 0.4201 | 4300 | 0.083 |
| 0.4250 | 4350 | 0.0908 |
| 0.4299 | 4400 | 0.0829 |
| 0.4348 | 4450 | 0.0871 |
| 0.4396 | 4500 | 0.0799 |
| 0.4445 | 4550 | 0.0777 |
| 0.4494 | 4600 | 0.0873 |
| 0.4543 | 4650 | 0.0805 |
| 0.4592 | 4700 | 0.0851 |
| 0.4641 | 4750 | 0.0855 |
| 0.4690 | 4800 | 0.0763 |
| 0.4738 | 4850 | 0.082 |
| 0.4787 | 4900 | 0.0699 |
| 0.4836 | 4950 | 0.0802 |
| 0.4885 | 5000 | 0.0807 |
| 0.4934 | 5050 | 0.0746 |
| 0.4983 | 5100 | 0.0705 |
| 0.5032 | 5150 | 0.0707 |
| 0.5080 | 5200 | 0.0827 |
| 0.5129 | 5250 | 0.0808 |
| 0.5178 | 5300 | 0.0835 |
| 0.5227 | 5350 | 0.0782 |
| 0.5276 | 5400 | 0.0698 |
| 0.5325 | 5450 | 0.0755 |
| 0.5373 | 5500 | 0.0743 |
| 0.5422 | 5550 | 0.0744 |
| 0.5471 | 5600 | 0.0724 |
| 0.5520 | 5650 | 0.0781 |
| 0.5569 | 5700 | 0.0712 |
| 0.5618 | 5750 | 0.0738 |
| 0.5667 | 5800 | 0.0692 |
| 0.5715 | 5850 | 0.0747 |
| 0.5764 | 5900 | 0.0686 |
| 0.5813 | 5950 | 0.0761 |
| 0.5862 | 6000 | 0.0696 |
| 0.5911 | 6050 | 0.0681 |
| 0.5960 | 6100 | 0.0714 |
| 0.6008 | 6150 | 0.0682 |
| 0.6057 | 6200 | 0.0746 |
| 0.6106 | 6250 | 0.0638 |
| 0.6155 | 6300 | 0.0672 |
| 0.6204 | 6350 | 0.0727 |
| 0.6253 | 6400 | 0.0711 |
| 0.6302 | 6450 | 0.0716 |
| 0.6350 | 6500 | 0.0609 |
| 0.6399 | 6550 | 0.066 |
| 0.6448 | 6600 | 0.0709 |
| 0.6497 | 6650 | 0.0687 |
| 0.6546 | 6700 | 0.0629 |
| 0.6595 | 6750 | 0.0693 |
| 0.6644 | 6800 | 0.0678 |
| 0.6692 | 6850 | 0.0612 |
| 0.6741 | 6900 | 0.0653 |
| 0.6790 | 6950 | 0.0642 |
| 0.6839 | 7000 | 0.068 |
| 0.6888 | 7050 | 0.0626 |
| 0.6937 | 7100 | 0.0623 |
| 0.6985 | 7150 | 0.0622 |
| 0.7034 | 7200 | 0.0661 |
| 0.7083 | 7250 | 0.0597 |
| 0.7132 | 7300 | 0.0584 |
| 0.7181 | 7350 | 0.0595 |
| 0.7230 | 7400 | 0.0647 |
| 0.7279 | 7450 | 0.0664 |
| 0.7327 | 7500 | 0.0682 |
| 0.7376 | 7550 | 0.0621 |
| 0.7425 | 7600 | 0.0603 |
| 0.7474 | 7650 | 0.0617 |
| 0.7523 | 7700 | 0.0554 |
| 0.7572 | 7750 | 0.056 |
| 0.7621 | 7800 | 0.0594 |
| 0.7669 | 7850 | 0.0594 |
| 0.7718 | 7900 | 0.0618 |
| 0.7767 | 7950 | 0.0638 |
| 0.7816 | 8000 | 0.0556 |
| 0.7865 | 8050 | 0.0608 |
| 0.7914 | 8100 | 0.0624 |
| 0.7962 | 8150 | 0.0621 |
| 0.8011 | 8200 | 0.0653 |
| 0.8060 | 8250 | 0.0648 |
| 0.8109 | 8300 | 0.0533 |
| 0.8158 | 8350 | 0.0584 |
| 0.8207 | 8400 | 0.0552 |
| 0.8256 | 8450 | 0.066 |
| 0.8304 | 8500 | 0.0616 |
| 0.8353 | 8550 | 0.0648 |
| 0.8402 | 8600 | 0.0618 |
| 0.8451 | 8650 | 0.0587 |
| 0.8500 | 8700 | 0.0616 |
| 0.8549 | 8750 | 0.0544 |
| 0.8598 | 8800 | 0.0637 |
| 0.8646 | 8850 | 0.0621 |
| 0.8695 | 8900 | 0.0574 |
| 0.8744 | 8950 | 0.0587 |
| 0.8793 | 9000 | 0.0606 |
| 0.8842 | 9050 | 0.0595 |
| 0.8891 | 9100 | 0.0627 |
| 0.8939 | 9150 | 0.0564 |
| 0.8988 | 9200 | 0.0542 |
| 0.9037 | 9250 | 0.0538 |
| 0.9086 | 9300 | 0.055 |
| 0.9135 | 9350 | 0.0562 |
| 0.9184 | 9400 | 0.0547 |
| 0.9233 | 9450 | 0.0514 |
| 0.9281 | 9500 | 0.0574 |
| 0.9330 | 9550 | 0.0503 |
| 0.9379 | 9600 | 0.0647 |
| 0.9428 | 9650 | 0.0554 |
| 0.9477 | 9700 | 0.0532 |
| 0.9526 | 9750 | 0.056 |
| 0.9575 | 9800 | 0.0554 |
| 0.9623 | 9850 | 0.0535 |
| 0.9672 | 9900 | 0.0553 |
| 0.9721 | 9950 | 0.0581 |
| 0.9770 | 10000 | 0.05 |
| 0.9819 | 10050 | 0.0571 |
| 0.9868 | 10100 | 0.0534 |
| 0.9916 | 10150 | 0.0462 |
| 0.9965 | 10200 | 0.0508 |
| 1.0014 | 10250 | 0.0506 |
| 1.0063 | 10300 | 0.0548 |
| 1.0111 | 10350 | 0.0476 |
| 1.0160 | 10400 | 0.0504 |
| 1.0209 | 10450 | 0.0433 |
| 1.0258 | 10500 | 0.0499 |
| 1.0307 | 10550 | 0.0453 |
| 1.0356 | 10600 | 0.0494 |
| 1.0404 | 10650 | 0.0456 |
| 1.0453 | 10700 | 0.0499 |
| 1.0502 | 10750 | 0.049 |
| 1.0551 | 10800 | 0.0464 |
| 1.0600 | 10850 | 0.0483 |
| 1.0649 | 10900 | 0.0487 |
| 1.0698 | 10950 | 0.0461 |
| 1.0746 | 11000 | 0.0433 |
| 1.0795 | 11050 | 0.0474 |
| 1.0844 | 11100 | 0.0485 |
| 1.0893 | 11150 | 0.0462 |
| 1.0942 | 11200 | 0.0396 |
| 1.0991 | 11250 | 0.0479 |
| 1.1040 | 11300 | 0.0471 |
| 1.1088 | 11350 | 0.0473 |
| 1.1137 | 11400 | 0.0482 |
| 1.1186 | 11450 | 0.0412 |
| 1.1235 | 11500 | 0.0455 |
| 1.1284 | 11550 | 0.0448 |
| 1.1333 | 11600 | 0.0531 |
| 1.1381 | 11650 | 0.0466 |
| 1.1430 | 11700 | 0.0527 |
| 1.1479 | 11750 | 0.0465 |
| 1.1528 | 11800 | 0.0536 |
| 1.1577 | 11850 | 0.0474 |
| 1.1626 | 11900 | 0.0515 |
| 1.1675 | 11950 | 0.0429 |
| 1.1723 | 12000 | 0.0464 |
| 1.1772 | 12050 | 0.0463 |
| 1.1821 | 12100 | 0.0491 |
| 1.1870 | 12150 | 0.0433 |
| 1.1919 | 12200 | 0.0466 |
| 1.1968 | 12250 | 0.0522 |
| 1.2017 | 12300 | 0.0463 |
| 1.2065 | 12350 | 0.0528 |
| 1.2114 | 12400 | 0.0451 |
| 1.2163 | 12450 | 0.0449 |
| 1.2212 | 12500 | 0.0475 |
| 1.2261 | 12550 | 0.0468 |
| 1.2310 | 12600 | 0.0456 |
| 1.2358 | 12650 | 0.0411 |
| 1.2407 | 12700 | 0.0439 |
| 1.2456 | 12750 | 0.0434 |
| 1.2505 | 12800 | 0.0475 |
| 1.2554 | 12850 | 0.0468 |
| 1.2603 | 12900 | 0.046 |
| 1.2652 | 12950 | 0.0467 |
| 1.2700 | 13000 | 0.0429 |
| 1.2749 | 13050 | 0.0437 |
| 1.2798 | 13100 | 0.048 |
| 1.2847 | 13150 | 0.0429 |
| 1.2896 | 13200 | 0.0507 |
| 1.2945 | 13250 | 0.0426 |
| 1.2994 | 13300 | 0.0408 |
| 1.3042 | 13350 | 0.0468 |
| 1.3091 | 13400 | 0.0389 |
| 1.3140 | 13450 | 0.0458 |
| 1.3189 | 13500 | 0.044 |
| 1.3238 | 13550 | 0.0417 |
| 1.3287 | 13600 | 0.0437 |
| 1.3335 | 13650 | 0.0427 |
| 1.3384 | 13700 | 0.0444 |
| 1.3433 | 13750 | 0.0496 |
| 1.3482 | 13800 | 0.0443 |
| 1.3531 | 13850 | 0.0421 |
| 1.3580 | 13900 | 0.0431 |
| 1.3629 | 13950 | 0.0474 |
| 1.3677 | 14000 | 0.0423 |
| 1.3726 | 14050 | 0.0437 |
| 1.3775 | 14100 | 0.038 |
| 1.3824 | 14150 | 0.0457 |
| 1.3873 | 14200 | 0.0459 |
| 1.3922 | 14250 | 0.0421 |
| 1.3970 | 14300 | 0.0482 |
| 1.4019 | 14350 | 0.0496 |
| 1.4068 | 14400 | 0.0436 |
| 1.4117 | 14450 | 0.0437 |
| 1.4166 | 14500 | 0.0463 |
| 1.4215 | 14550 | 0.04 |
| 1.4264 | 14600 | 0.046 |
| 1.4312 | 14650 | 0.0451 |
| 1.4361 | 14700 | 0.044 |
| 1.4410 | 14750 | 0.0436 |
| 1.4459 | 14800 | 0.0411 |
| 1.4508 | 14850 | 0.0453 |
| 1.4557 | 14900 | 0.0402 |
| 1.4606 | 14950 | 0.0437 |
| 1.4654 | 15000 | 0.0451 |
| 1.4703 | 15050 | 0.0454 |
| 1.4752 | 15100 | 0.0433 |
| 1.4801 | 15150 | 0.0399 |
| 1.4850 | 15200 | 0.0389 |
| 1.4899 | 15250 | 0.0451 |
| 1.4947 | 15300 | 0.0417 |
| 1.4996 | 15350 | 0.0411 |
| 1.5045 | 15400 | 0.0415 |
| 1.5094 | 15450 | 0.044 |
| 1.5143 | 15500 | 0.045 |
| 1.5192 | 15550 | 0.0414 |
| 1.5241 | 15600 | 0.0439 |
| 1.5289 | 15650 | 0.0381 |
| 1.5338 | 15700 | 0.0425 |
| 1.5387 | 15750 | 0.0439 |
| 1.5436 | 15800 | 0.0405 |
| 1.5485 | 15850 | 0.0407 |
| 1.5534 | 15900 | 0.04 |
| 1.5583 | 15950 | 0.0404 |
| 1.5631 | 16000 | 0.0392 |
| 1.5680 | 16050 | 0.0432 |
| 1.5729 | 16100 | 0.0374 |
| 1.5778 | 16150 | 0.044 |
| 1.5827 | 16200 | 0.0429 |
| 1.5876 | 16250 | 0.0394 |
| 1.5924 | 16300 | 0.0446 |
| 1.5973 | 16350 | 0.0389 |
| 1.6022 | 16400 | 0.0429 |
| 1.6071 | 16450 | 0.0442 |
| 1.6120 | 16500 | 0.0394 |
| 1.6169 | 16550 | 0.0403 |
| 1.6218 | 16600 | 0.0414 |
| 1.6266 | 16650 | 0.0386 |
| 1.6315 | 16700 | 0.0401 |
| 1.6364 | 16750 | 0.0415 |
| 1.6413 | 16800 | 0.0427 |
| 1.6462 | 16850 | 0.0412 |
| 1.6511 | 16900 | 0.0404 |
| 1.6560 | 16950 | 0.0402 |
| 1.6608 | 17000 | 0.0394 |
| 1.6657 | 17050 | 0.0429 |
| 1.6706 | 17100 | 0.0452 |
| 1.6755 | 17150 | 0.0438 |
| 1.6804 | 17200 | 0.0433 |
| 1.6853 | 17250 | 0.0393 |
| 1.6901 | 17300 | 0.0405 |
| 1.6950 | 17350 | 0.044 |
| 1.6999 | 17400 | 0.042 |
| 1.7048 | 17450 | 0.0401 |
| 1.7097 | 17500 | 0.0417 |
| 1.7146 | 17550 | 0.0351 |
| 1.7195 | 17600 | 0.0367 |
| 1.7243 | 17650 | 0.0436 |
| 1.7292 | 17700 | 0.0392 |
| 1.7341 | 17750 | 0.04 |
| 1.7390 | 17800 | 0.0415 |
| 1.7439 | 17850 | 0.0418 |
| 1.7488 | 17900 | 0.0366 |
| 1.7537 | 17950 | 0.0433 |
| 1.7585 | 18000 | 0.0391 |
| 1.7634 | 18050 | 0.0377 |
| 1.7683 | 18100 | 0.0398 |
| 1.7732 | 18150 | 0.0396 |
| 1.7781 | 18200 | 0.0404 |
| 1.7830 | 18250 | 0.0405 |
| 1.7878 | 18300 | 0.0381 |
| 1.7927 | 18350 | 0.04 |
| 1.7976 | 18400 | 0.0404 |
| 1.8025 | 18450 | 0.0348 |
| 1.8074 | 18500 | 0.0397 |
| 1.8123 | 18550 | 0.042 |
| 1.8172 | 18600 | 0.0454 |
| 1.8220 | 18650 | 0.0384 |
| 1.8269 | 18700 | 0.0387 |
| 1.8318 | 18750 | 0.042 |
| 1.8367 | 18800 | 0.0413 |
| 1.8416 | 18850 | 0.0403 |
| 1.8465 | 18900 | 0.0417 |
| 1.8514 | 18950 | 0.0386 |
| 1.8562 | 19000 | 0.0417 |
| 1.8611 | 19050 | 0.0396 |
| 1.8660 | 19100 | 0.039 |
| 1.8709 | 19150 | 0.0403 |
| 1.8758 | 19200 | 0.0402 |
| 1.8807 | 19250 | 0.044 |
| 1.8855 | 19300 | 0.0413 |
| 1.8904 | 19350 | 0.0379 |
| 1.8953 | 19400 | 0.042 |
| 1.9002 | 19450 | 0.0389 |
| 1.9051 | 19500 | 0.0399 |
| 1.9100 | 19550 | 0.0405 |
| 1.9149 | 19600 | 0.0414 |
| 1.9197 | 19650 | 0.0406 |
| 1.9246 | 19700 | 0.037 |
| 1.9295 | 19750 | 0.0406 |
| 1.9344 | 19800 | 0.0433 |
| 1.9393 | 19850 | 0.0357 |
| 1.9442 | 19900 | 0.038 |
| 1.9490 | 19950 | 0.0444 |
| 1.9539 | 20000 | 0.0406 |
| 1.9588 | 20050 | 0.0343 |
| 1.9637 | 20100 | 0.0414 |
| 1.9686 | 20150 | 0.0359 |
| 1.9735 | 20200 | 0.0421 |
| 1.9784 | 20250 | 0.0352 |
| 1.9832 | 20300 | 0.0406 |
| 1.9881 | 20350 | 0.0403 |
| 1.9930 | 20400 | 0.0396 |
| 1.9979 | 20450 | 0.0378 |