---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:9358675
- loss:TripletLoss
widget:
- source_sentence: acanthocephala
sentences:
- superficial genital wound (epidermal only)
- spiny-headed worm, nos
- fosfodiesterazy 3',5'-cyklicznego amp
- source_sentence: androstan-3,17-diol
sentences:
- >-
forma no especificada del esteroide, normalmente un metabolito importante de
la testosterona con actividad androgénica. ha sido relacionado con la
regulación de la secreción de gonadotrofina.
- 3',5'-camp 5'-ヌクレオチドヒドロラーゼ
- hypopharyngeal fistula occluder (physical object)
- source_sentence: missbildningar, multipla
sentences:
- 3_3_amino_3_carboxypropyl_uridine is a modified uridine base feature.
- acetil-coa acilasa
- multiple congenital malformations
- source_sentence: acanthocephala
sentences:
- >-
tomografía computarizada de estructuras del sistema musculoesquelético
(procedimiento)
- tipo acanthocephala (organismo)
- massa; intra-abdominaal
- source_sentence: vägtrafikolyckor
sentences:
- trimeresurus andersoni
- mnohočetné malformace
- accidente vial
base_model:
- bioformers/bioformer-16L
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. 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
- **Maximum Sequence Length:** 1024 tokens
- **Output Dimensionality:** 384 tokens
- **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': 1024, '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})
)
```
## 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("pankajrajdeo/328500_bioformer_16L")
# Run inference
sentences = [
'vägtrafikolyckor',
'accidente vial',
'trimeresurus andersoni',
]
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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 9,358,675 training samples
* Columns: anchor, positive, and negative
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 6 tokens
- mean: 12.84 tokens
- max: 23 tokens
| - min: 3 tokens
- mean: 15.45 tokens
- max: 187 tokens
| - min: 3 tokens
- mean: 14.75 tokens
- max: 91 tokens
|
* Samples:
| anchor | positive | negative |
|:--------------------------------------------|:-------------------------------------------------------------------|:------------------------------------------------|
| (131)i-makroaggregerat albumin | macroagrégats d'albumine humaine marquée à l'iode 131 | 1-acylglycerophosphorylinositol |
| (131)i-makroaggregerat albumin | albumin, radio-iodinated serum | allo-aromadendrane-10alpha,14-diol |
| (131)i-makroaggregerat albumin | serum albumin, radio iodinated | acquired zygomatic hyperplasia |
* Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 820,102 evaluation samples
* Columns: anchor, positive, and negative
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 3 tokens
- mean: 10.54 tokens
- max: 20 tokens
| - min: 3 tokens
- mean: 13.21 tokens
- max: 183 tokens
| - min: 3 tokens
- mean: 14.98 tokens
- max: 322 tokens
|
* Samples:
| anchor | positive | negative |
|:-----------------------------------------|:------------------------------------------|:------------------------------------------------------|
| 15-ketosteryloleathydrolase | steroid esterase, lipoidal | glutamic acid-lysine-tyrosine terpolymer |
| 15-ketosteryloleathydrolase | hydrolase, cholesterol ester | unionicola parvipora |
| 15-ketosteryloleathydrolase | acylhydrolase, sterol ester | mayamaea fossalis var. fossalis |
* Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: 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`: 128
- `per_device_eval_batch_size`: 8
- `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`: 10
- `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`: True
- `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 |
|:------:|:------:|:-------------:|:------:|
| 0.0137 | 1000 | 2.7368 | - |
| 0.0274 | 2000 | 1.4396 | - |
| 0.0410 | 3000 | 0.8916 | - |
| 0.0547 | 4000 | 0.6669 | - |
| 0.0684 | 5000 | 0.553 | - |
| 0.0821 | 6000 | 0.4759 | - |
| 0.0957 | 7000 | 0.4206 | - |
| 0.1094 | 8000 | 0.3808 | - |
| 0.1231 | 9000 | 0.3543 | - |
| 0.1368 | 10000 | 0.3281 | - |
| 0.1504 | 11000 | 0.3126 | - |
| 0.1641 | 12000 | 0.2923 | - |
| 0.1778 | 13000 | 0.2762 | - |
| 0.1915 | 14000 | 0.2617 | - |
| 0.2052 | 15000 | 0.2488 | - |
| 0.2188 | 16000 | 0.2363 | - |
| 0.2325 | 17000 | 0.2291 | - |
| 0.2462 | 18000 | 0.2235 | - |
| 0.2599 | 19000 | 0.2175 | - |
| 0.2735 | 20000 | 0.2077 | - |
| 0.2872 | 21000 | 0.2014 | - |
| 0.3009 | 22000 | 0.1944 | - |
| 0.3146 | 23000 | 0.1895 | - |
| 0.3283 | 24000 | 0.1889 | - |
| 0.3419 | 25000 | 0.1795 | - |
| 0.3556 | 26000 | 0.1769 | - |
| 0.3693 | 27000 | 0.1743 | - |
| 0.3830 | 28000 | 0.1691 | - |
| 0.3966 | 29000 | 0.1652 | - |
| 0.4103 | 30000 | 0.1654 | - |
| 0.4240 | 31000 | 0.1625 | - |
| 0.4377 | 32000 | 0.1614 | - |
| 0.4513 | 33000 | 0.1513 | - |
| 0.4650 | 34000 | 0.1527 | - |
| 0.4787 | 35000 | 0.1496 | - |
| 0.4924 | 36000 | 0.143 | - |
| 0.4992 | 36500 | - | 0.1243 |
| 0.5061 | 37000 | 0.1493 | - |
| 0.5197 | 38000 | 0.1467 | - |
| 0.5334 | 39000 | 0.1407 | - |
| 0.5471 | 40000 | 0.1364 | - |
| 0.5608 | 41000 | 0.1333 | - |
| 0.5744 | 42000 | 0.1378 | - |
| 0.5881 | 43000 | 0.1322 | - |
| 0.6018 | 44000 | 0.1304 | - |
| 0.6155 | 45000 | 0.1308 | - |
| 0.6291 | 46000 | 0.1254 | - |
| 0.6428 | 47000 | 0.1251 | - |
| 0.6565 | 48000 | 0.1256 | - |
| 0.6702 | 49000 | 0.1247 | - |
| 0.6839 | 50000 | 0.1225 | - |
| 0.6975 | 51000 | 0.1194 | - |
| 0.7112 | 52000 | 0.125 | - |
| 0.7249 | 53000 | 0.1206 | - |
| 0.7386 | 54000 | 0.1184 | - |
| 0.7522 | 55000 | 0.1134 | - |
| 0.7659 | 56000 | 0.1192 | - |
| 0.7796 | 57000 | 0.1134 | - |
| 0.7933 | 58000 | 0.1133 | - |
| 0.8069 | 59000 | 0.1104 | - |
| 0.8206 | 60000 | 0.111 | - |
| 0.8343 | 61000 | 0.1129 | - |
| 0.8480 | 62000 | 0.1098 | - |
| 0.8617 | 63000 | 0.1078 | - |
| 0.8753 | 64000 | 0.1096 | - |
| 0.8890 | 65000 | 0.1027 | - |
| 0.9027 | 66000 | 0.1097 | - |
| 0.9164 | 67000 | 0.109 | - |
| 0.9300 | 68000 | 0.1075 | - |
| 0.9437 | 69000 | 0.1036 | - |
| 0.9574 | 70000 | 0.1025 | - |
| 0.9711 | 71000 | 0.1056 | - |
| 0.9848 | 72000 | 0.1055 | - |
| 0.9984 | 73000 | 0.1021 | 0.0950 |
| 1.0121 | 74000 | 0.097 | - |
| 1.0258 | 75000 | 0.0931 | - |
| 1.0395 | 76000 | 0.089 | - |
| 1.0531 | 77000 | 0.0927 | - |
| 1.0668 | 78000 | 0.09 | - |
| 1.0805 | 79000 | 0.0922 | - |
| 1.0942 | 80000 | 0.0905 | - |
| 1.1078 | 81000 | 0.0907 | - |
| 1.1215 | 82000 | 0.0885 | - |
| 1.1352 | 83000 | 0.0877 | - |
| 1.1489 | 84000 | 0.085 | - |
| 1.1626 | 85000 | 0.0859 | - |
| 1.1762 | 86000 | 0.087 | - |
| 1.1899 | 87000 | 0.0851 | - |
| 1.2036 | 88000 | 0.0878 | - |
| 1.2173 | 89000 | 0.0873 | - |
| 1.2309 | 90000 | 0.0876 | - |
| 1.2446 | 91000 | 0.0838 | - |
| 1.2583 | 92000 | 0.0856 | - |
| 1.2720 | 93000 | 0.0818 | - |
| 1.2856 | 94000 | 0.0835 | - |
| 1.2993 | 95000 | 0.081 | - |
| 1.3130 | 96000 | 0.0797 | - |
| 1.3267 | 97000 | 0.0811 | - |
| 1.3404 | 98000 | 0.0802 | - |
| 1.3540 | 99000 | 0.0844 | - |
| 1.3677 | 100000 | 0.0787 | - |
| 1.3814 | 101000 | 0.0773 | - |
| 1.3951 | 102000 | 0.0802 | - |
| 1.4087 | 103000 | 0.0801 | - |
| 1.4224 | 104000 | 0.0762 | - |
| 1.4361 | 105000 | 0.0755 | - |
| 1.4498 | 106000 | 0.0791 | - |
| 1.4634 | 107000 | 0.0806 | - |
| 1.4771 | 108000 | 0.0756 | - |
| 1.4908 | 109000 | 0.0771 | - |
| 1.4976 | 109500 | - | 0.0779 |
| 1.5045 | 110000 | 0.0773 | - |
| 1.5182 | 111000 | 0.0769 | - |
| 1.5318 | 112000 | 0.0738 | - |
| 1.5455 | 113000 | 0.0765 | - |
| 1.5592 | 114000 | 0.0758 | - |
| 1.5729 | 115000 | 0.0759 | - |
| 1.5865 | 116000 | 0.0766 | - |
| 1.6002 | 117000 | 0.077 | - |
| 1.6139 | 118000 | 0.0755 | - |
| 1.6276 | 119000 | 0.0733 | - |
| 1.6413 | 120000 | 0.0753 | - |
| 1.6549 | 121000 | 0.0747 | - |
| 1.6686 | 122000 | 0.0733 | - |
| 1.6823 | 123000 | 0.0729 | - |
| 1.6960 | 124000 | 0.0705 | - |
| 1.7096 | 125000 | 0.0745 | - |
| 1.7233 | 126000 | 0.0726 | - |
| 1.7370 | 127000 | 0.0717 | - |
| 1.7507 | 128000 | 0.0687 | - |
| 1.7643 | 129000 | 0.0715 | - |
| 1.7780 | 130000 | 0.0701 | - |
| 1.7917 | 131000 | 0.0671 | - |
| 1.8054 | 132000 | 0.07 | - |
| 1.8191 | 133000 | 0.0683 | - |
| 1.8327 | 134000 | 0.0684 | - |
| 1.8464 | 135000 | 0.0668 | - |
| 1.8601 | 136000 | 0.0681 | - |
| 1.8738 | 137000 | 0.0668 | - |
| 1.8874 | 138000 | 0.0655 | - |
| 1.9011 | 139000 | 0.0698 | - |
| 1.9148 | 140000 | 0.0692 | - |
| 1.9285 | 141000 | 0.0667 | - |
| 1.9421 | 142000 | 0.0662 | - |
| 1.9558 | 143000 | 0.0695 | - |
| 1.9695 | 144000 | 0.0663 | - |
| 1.9832 | 145000 | 0.0669 | - |
| 1.9969 | 146000 | 0.0661 | 0.0686 |
| 2.0105 | 147000 | 0.0553 | - |
| 2.0242 | 148000 | 0.0521 | - |
| 2.0379 | 149000 | 0.053 | - |
| 2.0516 | 150000 | 0.0531 | - |
| 2.0652 | 151000 | 0.0529 | - |
| 2.0789 | 152000 | 0.0519 | - |
| 2.0926 | 153000 | 0.0548 | - |
| 2.1063 | 154000 | 0.0549 | - |
| 2.1199 | 155000 | 0.0525 | - |
| 2.1336 | 156000 | 0.056 | - |
| 2.1473 | 157000 | 0.0514 | - |
| 2.1610 | 158000 | 0.0526 | - |
| 2.1747 | 159000 | 0.0512 | - |
| 2.1883 | 160000 | 0.0526 | - |
| 2.2020 | 161000 | 0.0524 | - |
| 2.2157 | 162000 | 0.052 | - |
| 2.2294 | 163000 | 0.0526 | - |
| 2.2430 | 164000 | 0.0531 | - |
| 2.2567 | 165000 | 0.0522 | - |
| 2.2704 | 166000 | 0.0536 | - |
| 2.2841 | 167000 | 0.0505 | - |
| 2.2978 | 168000 | 0.0521 | - |
| 2.3114 | 169000 | 0.0518 | - |
| 2.3251 | 170000 | 0.0497 | - |
| 2.3388 | 171000 | 0.0534 | - |
| 2.3525 | 172000 | 0.0518 | - |
| 2.3661 | 173000 | 0.0502 | - |
| 2.3798 | 174000 | 0.053 | - |
| 2.3935 | 175000 | 0.0515 | - |
| 2.4072 | 176000 | 0.0503 | - |
| 2.4208 | 177000 | 0.0526 | - |
| 2.4345 | 178000 | 0.0497 | - |
| 2.4482 | 179000 | 0.0524 | - |
| 2.4619 | 180000 | 0.0517 | - |
| 2.4756 | 181000 | 0.0522 | - |
| 2.4892 | 182000 | 0.0536 | - |
| 2.4961 | 182500 | - | 0.0635 |
| 2.5029 | 183000 | 0.0474 | - |
| 2.5166 | 184000 | 0.0519 | - |
| 2.5303 | 185000 | 0.0474 | - |
| 2.5439 | 186000 | 0.0503 | - |
| 2.5576 | 187000 | 0.0506 | - |
| 2.5713 | 188000 | 0.0489 | - |
| 2.5850 | 189000 | 0.0497 | - |
| 2.5986 | 190000 | 0.0501 | - |
| 2.6123 | 191000 | 0.0516 | - |
| 2.6260 | 192000 | 0.052 | - |
| 2.6397 | 193000 | 0.0477 | - |
| 2.6534 | 194000 | 0.049 | - |
| 2.6670 | 195000 | 0.0497 | - |
| 2.6807 | 196000 | 0.049 | - |
| 2.6944 | 197000 | 0.0496 | - |
| 2.7081 | 198000 | 0.0522 | - |
| 2.7217 | 199000 | 0.0475 | - |
| 2.7354 | 200000 | 0.0499 | - |
| 2.7491 | 201000 | 0.0501 | - |
| 2.7628 | 202000 | 0.0468 | - |
| 2.7764 | 203000 | 0.0491 | - |
| 2.7901 | 204000 | 0.0515 | - |
| 2.8038 | 205000 | 0.0485 | - |
| 2.8175 | 206000 | 0.0458 | - |
| 2.8312 | 207000 | 0.0502 | - |
| 2.8448 | 208000 | 0.048 | - |
| 2.8585 | 209000 | 0.0485 | - |
| 2.8722 | 210000 | 0.0493 | - |
| 2.8859 | 211000 | 0.0462 | - |
| 2.8995 | 212000 | 0.048 | - |
| 2.9132 | 213000 | 0.0475 | - |
| 2.9269 | 214000 | 0.0459 | - |
| 2.9406 | 215000 | 0.0487 | - |
| 2.9543 | 216000 | 0.0487 | - |
| 2.9679 | 217000 | 0.047 | - |
| 2.9816 | 218000 | 0.048 | - |
| 2.9953 | 219000 | 0.0472 | 0.0592 |
| 3.0090 | 220000 | 0.0398 | - |
| 3.0226 | 221000 | 0.0353 | - |
| 3.0363 | 222000 | 0.0354 | - |
| 3.0500 | 223000 | 0.0361 | - |
| 3.0637 | 224000 | 0.0367 | - |
| 3.0773 | 225000 | 0.0375 | - |
| 3.0910 | 226000 | 0.037 | - |
| 3.1047 | 227000 | 0.0358 | - |
| 3.1184 | 228000 | 0.0372 | - |
| 3.1321 | 229000 | 0.0365 | - |
| 3.1457 | 230000 | 0.0389 | - |
| 3.1594 | 231000 | 0.0372 | - |
| 3.1731 | 232000 | 0.0345 | - |
| 3.1868 | 233000 | 0.0383 | - |
| 3.2004 | 234000 | 0.0337 | - |
| 3.2141 | 235000 | 0.0348 | - |
| 3.2278 | 236000 | 0.0376 | - |
| 3.2415 | 237000 | 0.0394 | - |
| 3.2551 | 238000 | 0.0378 | - |
| 3.2688 | 239000 | 0.0358 | - |
| 3.2825 | 240000 | 0.0344 | - |
| 3.2962 | 241000 | 0.0363 | - |
| 3.3099 | 242000 | 0.0373 | - |
| 3.3235 | 243000 | 0.0371 | - |
| 3.3372 | 244000 | 0.0375 | - |
| 3.3509 | 245000 | 0.0365 | - |
| 3.3646 | 246000 | 0.0362 | - |
| 3.3782 | 247000 | 0.0365 | - |
| 3.3919 | 248000 | 0.0386 | - |
| 3.4056 | 249000 | 0.0337 | - |
| 3.4193 | 250000 | 0.0382 | - |
| 3.4329 | 251000 | 0.0353 | - |
| 3.4466 | 252000 | 0.0349 | - |
| 3.4603 | 253000 | 0.0373 | - |
| 3.4740 | 254000 | 0.0374 | - |
| 3.4877 | 255000 | 0.036 | - |
| 3.4945 | 255500 | - | 0.0561 |
| 3.5013 | 256000 | 0.0357 | - |
| 3.5150 | 257000 | 0.0375 | - |
| 3.5287 | 258000 | 0.0372 | - |
| 3.5424 | 259000 | 0.0371 | - |
| 3.5560 | 260000 | 0.0364 | - |
| 3.5697 | 261000 | 0.037 | - |
| 3.5834 | 262000 | 0.0375 | - |
| 3.5971 | 263000 | 0.0369 | - |
| 3.6108 | 264000 | 0.0367 | - |
| 3.6244 | 265000 | 0.0359 | - |
| 3.6381 | 266000 | 0.0353 | - |
| 3.6518 | 267000 | 0.0356 | - |
| 3.6655 | 268000 | 0.0362 | - |
| 3.6791 | 269000 | 0.0365 | - |
| 3.6928 | 270000 | 0.0395 | - |
| 3.7065 | 271000 | 0.0352 | - |
| 3.7202 | 272000 | 0.0366 | - |
| 3.7338 | 273000 | 0.0357 | - |
| 3.7475 | 274000 | 0.0372 | - |
| 3.7612 | 275000 | 0.0379 | - |
| 3.7749 | 276000 | 0.0365 | - |
| 3.7886 | 277000 | 0.0374 | - |
| 3.8022 | 278000 | 0.0355 | - |
| 3.8159 | 279000 | 0.0362 | - |
| 3.8296 | 280000 | 0.036 | - |
| 3.8433 | 281000 | 0.036 | - |
| 3.8569 | 282000 | 0.0337 | - |
| 3.8706 | 283000 | 0.0374 | - |
| 3.8843 | 284000 | 0.0353 | - |
| 3.8980 | 285000 | 0.0344 | - |
| 3.9116 | 286000 | 0.0355 | - |
| 3.9253 | 287000 | 0.0342 | - |
| 3.9390 | 288000 | 0.0361 | - |
| 3.9527 | 289000 | 0.0361 | - |
| 3.9664 | 290000 | 0.0376 | - |
| 3.9800 | 291000 | 0.0363 | - |
| 3.9937 | 292000 | 0.0363 | 0.0561 |
| 4.0074 | 293000 | 0.0313 | - |
| 4.0211 | 294000 | 0.0273 | - |
| 4.0347 | 295000 | 0.0277 | - |
| 4.0484 | 296000 | 0.0248 | - |
| 4.0621 | 297000 | 0.0268 | - |
| 4.0758 | 298000 | 0.0259 | - |
| 4.0894 | 299000 | 0.027 | - |
| 4.1031 | 300000 | 0.0256 | - |
| 4.1168 | 301000 | 0.0283 | - |
| 4.1305 | 302000 | 0.0294 | - |
| 4.1442 | 303000 | 0.0263 | - |
| 4.1578 | 304000 | 0.0261 | - |
| 4.1715 | 305000 | 0.0257 | - |
| 4.1852 | 306000 | 0.0255 | - |
| 4.1989 | 307000 | 0.0279 | - |
| 4.2125 | 308000 | 0.0273 | - |
| 4.2262 | 309000 | 0.0263 | - |
| 4.2399 | 310000 | 0.0276 | - |
| 4.2536 | 311000 | 0.0262 | - |
| 4.2673 | 312000 | 0.029 | - |
| 4.2809 | 313000 | 0.0261 | - |
| 4.2946 | 314000 | 0.0264 | - |
| 4.3083 | 315000 | 0.0252 | - |
| 4.3220 | 316000 | 0.0265 | - |
| 4.3356 | 317000 | 0.0281 | - |
| 4.3493 | 318000 | 0.0249 | - |
| 4.3630 | 319000 | 0.0278 | - |
| 4.3767 | 320000 | 0.0272 | - |
| 4.3903 | 321000 | 0.0285 | - |
| 4.4040 | 322000 | 0.0279 | - |
| 4.4177 | 323000 | 0.0265 | - |
| 4.4314 | 324000 | 0.0268 | - |
| 4.4451 | 325000 | 0.0257 | - |
| 4.4587 | 326000 | 0.0273 | - |
| 4.4724 | 327000 | 0.027 | - |
| 4.4861 | 328000 | 0.0275 | - |
| 4.4929 | 328500 | - | 0.0548 |