---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:329355
- loss:MultipleNegativesRankingLoss
base_model: NeuML/pubmedbert-base-embeddings
widget:
- source_sentence: 'CONTEXT: check your other micronutrients B12, folate, your vitamin
D malabsorbed after surgery
REASONING: Folate listed among micronutrients to check.'
sentences:
- XR FOOT LT 3 View* (HUD)
- Folate, Serum (Request)* (Orchard)
- Wound Cleansing/Irrigation, Clinic
- source_sentence: 'COMMAND: Submit a 99213 established patient office visit charge
for today’s evaluation and counseling.'
sentences:
- '[''99213 office o/p est low 20-29 min (Charge)'''
- 99213 office o/p est low 20-29 min (Charge)
- clotrimazole 1% topical cream
- source_sentence: 'CONTEXT: pediatric patient with fever, sore throat, headache exam
of ears, throat, lungs; counseling on strep care school note discussed; pharmacy
confirmed; results to be called'
sentences:
- meloxicam 7.5 mg oral tablet
- 99213 office o/p est low 20-29 min (Charge)
- 99214 office o/p est mod 30-39 min (Charge)
- source_sentence: 'CONTEXT: established patient follow-up style visit anxiety, tremor
assessment and medication counseling 30–40 minute detailed discussion management
options and side effects reviewed
REASONING: Extended medical decision-making on anxiety and essential tremor with
medication options and counseling consistent with a moderate complexity, mid-length
established patient visit.'
sentences:
- 99214 office o/p est mod 30-39 min (Charge)
- '''A4550 surgical trays (Charge)'''
- '[''99214 office o/p est mod 30-39 min (Charge)'']'
- source_sentence: 'COMMAND: Have lab draw blood today per ordered tests.
CONTEXT: get a little blood work today they''re gonna get you to x-ray and lab
before you leave'
sentences:
- Thin Prep Pap w/ High Risk HPV, Over 30 Years old (Request)* (Orchard)
- Rocephin*
- 36415 blood draw, venipuncture (Charge)
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on NeuML/pubmedbert-base-embeddings
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings). 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:** [NeuML/pubmedbert-base-embeddings](https://huggingface.co/NeuML/pubmedbert-base-embeddings)
- **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, 'architecture': 'BertModel'})
(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})
)
```
## 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("praphul555/jeda-stage-1")
# Run inference
sentences = [
"COMMAND: Have lab draw blood today per ordered tests.\nCONTEXT: get a little blood work today they're gonna get you to x-ray and lab before you leave",
'blood draw, venipuncture (Charge)',
'Rocephin*',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[ 1.0000, 0.8369, -0.0363],
# [ 0.8369, 1.0000, -0.0708],
# [-0.0363, -0.0708, 1.0000]])
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 329,355 training samples
* Columns: text1 and text2
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 8 tokens
- mean: 37.3 tokens
- max: 106 tokens
| - min: 3 tokens
- mean: 12.95 tokens
- max: 24 tokens
|
* Samples:
| text1 | text2 |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------|
| COMMAND: Please arrange transport to radiology now and let them know we're sending him for a right foot/toe x-ray with weight-bearing views.
CONTEXT: wheel him over to x-ray x-ray right foot complete with weight-bearing views go tell the x-ray lady | Radiology Transfer Communication |
| COMMAND: Please arrange transport to radiology now and let them know we're sending him for a right foot/toe x-ray with weight-bearing views. | Radiology Transfer Communication |
| CONTEXT: wheel him over to x-ray x-ray right foot complete with weight-bearing views go tell the x-ray lady
REASONING: Doctor instructs staff to transport patient to x-ray and communicate exam details. | Radiology Transfer Communication |
* Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `seed`: 13
- `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`: 64
- `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`: 5
- `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`: 13
- `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`: 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
- `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
- `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`: False
- `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`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
Click to expand
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0097 | 50 | 2.3103 |
| 0.0194 | 100 | 1.9798 |
| 0.0291 | 150 | 1.6487 |
| 0.0389 | 200 | 1.3829 |
| 0.0486 | 250 | 1.25 |
| 0.0583 | 300 | 1.1482 |
| 0.0680 | 350 | 1.0997 |
| 0.0777 | 400 | 1.0484 |
| 0.0874 | 450 | 0.9522 |
| 0.0971 | 500 | 0.9385 |
| 0.1069 | 550 | 0.8914 |
| 0.1166 | 600 | 0.86 |
| 0.1263 | 650 | 0.8825 |
| 0.1360 | 700 | 0.8217 |
| 0.1457 | 750 | 0.8102 |
| 0.1554 | 800 | 0.7831 |
| 0.1651 | 850 | 0.796 |
| 0.1749 | 900 | 0.7542 |
| 0.1846 | 950 | 0.775 |
| 0.1943 | 1000 | 0.7437 |
| 0.2040 | 1050 | 0.7237 |
| 0.2137 | 1100 | 0.6945 |
| 0.2234 | 1150 | 0.6979 |
| 0.2331 | 1200 | 0.6834 |
| 0.2429 | 1250 | 0.7149 |
| 0.2526 | 1300 | 0.6582 |
| 0.2623 | 1350 | 0.6437 |
| 0.2720 | 1400 | 0.6213 |
| 0.2817 | 1450 | 0.6087 |
| 0.2914 | 1500 | 0.6225 |
| 0.3011 | 1550 | 0.5579 |
| 0.3109 | 1600 | 0.6206 |
| 0.3206 | 1650 | 0.5787 |
| 0.3303 | 1700 | 0.5721 |
| 0.3400 | 1750 | 0.5695 |
| 0.3497 | 1800 | 0.5395 |
| 0.3594 | 1850 | 0.5476 |
| 0.3691 | 1900 | 0.5556 |
| 0.3789 | 1950 | 0.5628 |
| 0.3886 | 2000 | 0.5241 |
| 0.3983 | 2050 | 0.5457 |
| 0.4080 | 2100 | 0.5339 |
| 0.4177 | 2150 | 0.5429 |
| 0.4274 | 2200 | 0.5421 |
| 0.4371 | 2250 | 0.5149 |
| 0.4469 | 2300 | 0.5015 |
| 0.4566 | 2350 | 0.5005 |
| 0.4663 | 2400 | 0.5149 |
| 0.4760 | 2450 | 0.5004 |
| 0.4857 | 2500 | 0.4852 |
| 0.4954 | 2550 | 0.5316 |
| 0.5051 | 2600 | 0.5227 |
| 0.5149 | 2650 | 0.5138 |
| 0.5246 | 2700 | 0.4744 |
| 0.5343 | 2750 | 0.4885 |
| 0.5440 | 2800 | 0.5036 |
| 0.5537 | 2850 | 0.5077 |
| 0.5634 | 2900 | 0.4669 |
| 0.5731 | 2950 | 0.4682 |
| 0.5829 | 3000 | 0.4588 |
| 0.5926 | 3050 | 0.4567 |
| 0.6023 | 3100 | 0.4671 |
| 0.6120 | 3150 | 0.5114 |
| 0.6217 | 3200 | 0.4715 |
| 0.6314 | 3250 | 0.4353 |
| 0.6412 | 3300 | 0.46 |
| 0.6509 | 3350 | 0.4525 |
| 0.6606 | 3400 | 0.4633 |
| 0.6703 | 3450 | 0.4344 |
| 0.6800 | 3500 | 0.4566 |
| 0.6897 | 3550 | 0.4643 |
| 0.6994 | 3600 | 0.4615 |
| 0.7092 | 3650 | 0.4387 |
| 0.7189 | 3700 | 0.4145 |
| 0.7286 | 3750 | 0.4646 |
| 0.7383 | 3800 | 0.4831 |
| 0.7480 | 3850 | 0.444 |
| 0.7577 | 3900 | 0.4412 |
| 0.7674 | 3950 | 0.4407 |
| 0.7772 | 4000 | 0.4383 |
| 0.7869 | 4050 | 0.4403 |
| 0.7966 | 4100 | 0.4674 |
| 0.8063 | 4150 | 0.4477 |
| 0.8160 | 4200 | 0.4619 |
| 0.8257 | 4250 | 0.4368 |
| 0.8354 | 4300 | 0.4531 |
| 0.8452 | 4350 | 0.4409 |
| 0.8549 | 4400 | 0.4456 |
| 0.8646 | 4450 | 0.4312 |
| 0.8743 | 4500 | 0.4233 |
| 0.8840 | 4550 | 0.4134 |
| 0.8937 | 4600 | 0.3193 |
| 0.9034 | 4650 | 0.2839 |
| 0.9132 | 4700 | 0.2286 |
| 0.9229 | 4750 | 0.2572 |
| 0.9326 | 4800 | 0.2896 |
| 0.9423 | 4850 | 0.1615 |
| 0.9520 | 4900 | 0.2984 |
| 0.9617 | 4950 | 0.1891 |
| 0.9714 | 5000 | 0.2552 |
| 0.9812 | 5050 | 0.2165 |
| 0.9909 | 5100 | 0.2774 |
| 1.0006 | 5150 | 0.2737 |
| 1.0103 | 5200 | 0.447 |
| 1.0200 | 5250 | 0.4317 |
| 1.0297 | 5300 | 0.3798 |
| 1.0394 | 5350 | 0.4063 |
| 1.0492 | 5400 | 0.4231 |
| 1.0589 | 5450 | 0.4202 |
| 1.0686 | 5500 | 0.3911 |
| 1.0783 | 5550 | 0.3807 |
| 1.0880 | 5600 | 0.3979 |
| 1.0977 | 5650 | 0.3908 |
| 1.1074 | 5700 | 0.4167 |
| 1.1172 | 5750 | 0.3885 |
| 1.1269 | 5800 | 0.3992 |
| 1.1366 | 5850 | 0.4102 |
| 1.1463 | 5900 | 0.3949 |
| 1.1560 | 5950 | 0.4066 |
| 1.1657 | 6000 | 0.3871 |
| 1.1754 | 6050 | 0.3925 |
| 1.1852 | 6100 | 0.3785 |
| 1.1949 | 6150 | 0.4529 |
| 1.2046 | 6200 | 0.4188 |
| 1.2143 | 6250 | 0.4844 |
| 1.2240 | 6300 | 0.4171 |
| 1.2337 | 6350 | 0.4001 |
| 1.2434 | 6400 | 0.3992 |
| 1.2532 | 6450 | 0.4167 |
| 1.2629 | 6500 | 0.4395 |
| 1.2726 | 6550 | 0.4 |
| 1.2823 | 6600 | 0.3905 |
| 1.2920 | 6650 | 0.3769 |
| 1.3017 | 6700 | 0.3846 |
| 1.3114 | 6750 | 0.4 |
| 1.3212 | 6800 | 0.4062 |
| 1.3309 | 6850 | 0.3972 |
| 1.3406 | 6900 | 0.3875 |
| 1.3503 | 6950 | 0.3958 |
| 1.3600 | 7000 | 0.3843 |
| 1.3697 | 7050 | 0.4004 |
| 1.3794 | 7100 | 0.4435 |
| 1.3892 | 7150 | 0.3856 |
| 1.3989 | 7200 | 0.3843 |
| 1.4086 | 7250 | 0.3777 |
| 1.4183 | 7300 | 0.4103 |
| 1.4280 | 7350 | 0.3795 |
| 1.4377 | 7400 | 0.3719 |
| 1.4474 | 7450 | 0.3938 |
| 1.4572 | 7500 | 0.4058 |
| 1.4669 | 7550 | 0.3913 |
| 1.4766 | 7600 | 0.3992 |
| 1.4863 | 7650 | 0.3743 |
| 1.4960 | 7700 | 0.4072 |
| 1.5057 | 7750 | 0.3788 |
| 1.5154 | 7800 | 0.3987 |
| 1.5252 | 7850 | 0.3774 |
| 1.5349 | 7900 | 0.3803 |
| 1.5446 | 7950 | 0.3582 |
| 1.5543 | 8000 | 0.4222 |
| 1.5640 | 8050 | 0.4001 |
| 1.5737 | 8100 | 0.3857 |
| 1.5834 | 8150 | 0.3819 |
| 1.5932 | 8200 | 0.3643 |
| 1.6029 | 8250 | 0.3884 |
| 1.6126 | 8300 | 0.3761 |
| 1.6223 | 8350 | 0.4295 |
| 1.6320 | 8400 | 0.4073 |
| 1.6417 | 8450 | 0.3963 |
| 1.6514 | 8500 | 0.389 |
| 1.6612 | 8550 | 0.3677 |
| 1.6709 | 8600 | 0.4012 |
| 1.6806 | 8650 | 0.3732 |
| 1.6903 | 8700 | 0.3793 |
| 1.7000 | 8750 | 0.3712 |
| 1.7097 | 8800 | 0.3734 |
| 1.7194 | 8850 | 0.3895 |
| 1.7292 | 8900 | 0.3667 |
| 1.7389 | 8950 | 0.3832 |
| 1.7486 | 9000 | 0.3842 |
| 1.7583 | 9050 | 0.3822 |
| 1.7680 | 9100 | 0.3706 |
| 1.7777 | 9150 | 0.3699 |
| 1.7874 | 9200 | 0.3738 |
| 1.7972 | 9250 | 0.3748 |
| 1.8069 | 9300 | 0.3911 |
| 1.8166 | 9350 | 0.366 |
| 1.8263 | 9400 | 0.3626 |
| 1.8360 | 9450 | 0.3762 |
| 1.8457 | 9500 | 0.3711 |
| 1.8554 | 9550 | 0.3568 |
| 1.8652 | 9600 | 0.3877 |
| 1.8749 | 9650 | 0.3744 |
| 1.8846 | 9700 | 0.3858 |
| 1.8943 | 9750 | 0.2191 |
| 1.9040 | 9800 | 0.1622 |
| 1.9137 | 9850 | 0.13 |
| 1.9235 | 9900 | 0.359 |
| 1.9332 | 9950 | 0.1739 |
| 1.9429 | 10000 | 0.2212 |
| 1.9526 | 10050 | 0.2445 |
| 1.9623 | 10100 | 0.2059 |
| 1.9720 | 10150 | 0.2288 |
| 1.9817 | 10200 | 0.1985 |
| 1.9915 | 10250 | 0.182 |
| 2.0012 | 10300 | 0.2609 |
| 2.0109 | 10350 | 0.3533 |
| 2.0206 | 10400 | 0.3322 |
| 2.0303 | 10450 | 0.3565 |
| 2.0400 | 10500 | 0.3454 |
| 2.0497 | 10550 | 0.3623 |
| 2.0595 | 10600 | 0.3685 |
| 2.0692 | 10650 | 0.3468 |
| 2.0789 | 10700 | 0.3448 |
| 2.0886 | 10750 | 0.3524 |
| 2.0983 | 10800 | 0.3691 |
| 2.1080 | 10850 | 0.3505 |
| 2.1177 | 10900 | 0.3253 |
| 2.1275 | 10950 | 0.3422 |
| 2.1372 | 11000 | 0.3321 |
| 2.1469 | 11050 | 0.3392 |
| 2.1566 | 11100 | 0.3292 |
| 2.1663 | 11150 | 0.3572 |
| 2.1760 | 11200 | 0.3483 |
| 2.1857 | 11250 | 0.3535 |
| 2.1955 | 11300 | 0.3559 |
| 2.2052 | 11350 | 0.3331 |
| 2.2149 | 11400 | 0.3367 |
| 2.2246 | 11450 | 0.3538 |
| 2.2343 | 11500 | 0.3458 |
| 2.2440 | 11550 | 0.3197 |
| 2.2537 | 11600 | 0.3587 |
| 2.2635 | 11650 | 0.3565 |
| 2.2732 | 11700 | 0.3533 |
| 2.2829 | 11750 | 0.3191 |
| 2.2926 | 11800 | 0.3591 |
| 2.3023 | 11850 | 0.3598 |
| 2.3120 | 11900 | 0.3495 |
| 2.3217 | 11950 | 0.353 |
| 2.3315 | 12000 | 0.3329 |
| 2.3412 | 12050 | 0.3365 |
| 2.3509 | 12100 | 0.3246 |
| 2.3606 | 12150 | 0.3377 |
| 2.3703 | 12200 | 0.3392 |
| 2.3800 | 12250 | 0.3546 |
| 2.3897 | 12300 | 0.3452 |
| 2.3995 | 12350 | 0.3403 |
| 2.4092 | 12400 | 0.3473 |
| 2.4189 | 12450 | 0.336 |
| 2.4286 | 12500 | 0.3591 |
| 2.4383 | 12550 | 0.3425 |
| 2.4480 | 12600 | 0.3293 |
| 2.4577 | 12650 | 0.3339 |
| 2.4675 | 12700 | 0.3386 |
| 2.4772 | 12750 | 0.3335 |
| 2.4869 | 12800 | 0.3249 |
| 2.4966 | 12850 | 0.3123 |
| 2.5063 | 12900 | 0.3182 |
| 2.5160 | 12950 | 0.3282 |
| 2.5257 | 13000 | 0.317 |
| 2.5355 | 13050 | 0.3177 |
| 2.5452 | 13100 | 0.3075 |
| 2.5549 | 13150 | 0.3349 |
| 2.5646 | 13200 | 0.3543 |
| 2.5743 | 13250 | 0.3228 |
| 2.5840 | 13300 | 0.3334 |
| 2.5937 | 13350 | 0.3364 |
| 2.6035 | 13400 | 0.333 |
| 2.6132 | 13450 | 0.3633 |
| 2.6229 | 13500 | 0.3547 |
| 2.6326 | 13550 | 0.3431 |
| 2.6423 | 13600 | 0.3265 |
| 2.6520 | 13650 | 0.3197 |
| 2.6617 | 13700 | 0.3233 |
| 2.6715 | 13750 | 0.3293 |
| 2.6812 | 13800 | 0.3249 |
| 2.6909 | 13850 | 0.3041 |
| 2.7006 | 13900 | 0.3612 |
| 2.7103 | 13950 | 0.3391 |
| 2.7200 | 14000 | 0.324 |
| 2.7297 | 14050 | 0.3114 |
| 2.7395 | 14100 | 0.3365 |
| 2.7492 | 14150 | 0.2987 |
| 2.7589 | 14200 | 0.3233 |
| 2.7686 | 14250 | 0.3221 |
| 2.7783 | 14300 | 0.3348 |
| 2.7880 | 14350 | 0.3231 |
| 2.7977 | 14400 | 0.3407 |
| 2.8075 | 14450 | 0.3017 |
| 2.8172 | 14500 | 0.3264 |
| 2.8269 | 14550 | 0.3349 |
| 2.8366 | 14600 | 0.3217 |
| 2.8463 | 14650 | 0.2965 |
| 2.8560 | 14700 | 0.322 |
| 2.8657 | 14750 | 0.3195 |
| 2.8755 | 14800 | 0.3021 |
| 2.8852 | 14850 | 0.299 |
| 2.8949 | 14900 | 0.1857 |
| 2.9046 | 14950 | 0.1839 |
| 2.9143 | 15000 | 0.1171 |
| 2.9240 | 15050 | 0.1275 |
| 2.9337 | 15100 | 0.1814 |
| 2.9435 | 15150 | 0.1778 |
| 2.9532 | 15200 | 0.142 |
| 2.9629 | 15250 | 0.2545 |
| 2.9726 | 15300 | 0.1202 |
| 2.9823 | 15350 | 0.132 |
| 2.9920 | 15400 | 0.154 |
| 3.0017 | 15450 | 0.2622 |
| 3.0115 | 15500 | 0.3185 |
| 3.0212 | 15550 | 0.293 |
| 3.0309 | 15600 | 0.3164 |
| 3.0406 | 15650 | 0.2934 |
| 3.0503 | 15700 | 0.3005 |
| 3.0600 | 15750 | 0.3017 |
| 3.0697 | 15800 | 0.2965 |
| 3.0795 | 15850 | 0.309 |
| 3.0892 | 15900 | 0.3056 |
| 3.0989 | 15950 | 0.3318 |
| 3.1086 | 16000 | 0.3094 |
| 3.1183 | 16050 | 0.3041 |
| 3.1280 | 16100 | 0.2981 |
| 3.1378 | 16150 | 0.316 |
| 3.1475 | 16200 | 0.3086 |
| 3.1572 | 16250 | 0.3062 |
| 3.1669 | 16300 | 0.3069 |
| 3.1766 | 16350 | 0.312 |
| 3.1863 | 16400 | 0.3161 |
| 3.1960 | 16450 | 0.3059 |
| 3.2058 | 16500 | 0.2899 |
| 3.2155 | 16550 | 0.312 |
| 3.2252 | 16600 | 0.3189 |
| 3.2349 | 16650 | 0.3152 |
| 3.2446 | 16700 | 0.2998 |
| 3.2543 | 16750 | 0.301 |
| 3.2640 | 16800 | 0.3129 |
| 3.2738 | 16850 | 0.2955 |
| 3.2835 | 16900 | 0.2923 |
| 3.2932 | 16950 | 0.3111 |
| 3.3029 | 17000 | 0.3097 |
| 3.3126 | 17050 | 0.3045 |
| 3.3223 | 17100 | 0.296 |
| 3.3320 | 17150 | 0.3086 |
| 3.3418 | 17200 | 0.2902 |
| 3.3515 | 17250 | 0.322 |
| 3.3612 | 17300 | 0.3105 |
| 3.3709 | 17350 | 0.3048 |
| 3.3806 | 17400 | 0.2853 |
| 3.3903 | 17450 | 0.2795 |
| 3.4000 | 17500 | 0.2933 |
| 3.4098 | 17550 | 0.2834 |
| 3.4195 | 17600 | 0.3 |
| 3.4292 | 17650 | 0.2998 |
| 3.4389 | 17700 | 0.2972 |
| 3.4486 | 17750 | 0.285 |
| 3.4583 | 17800 | 0.2888 |
| 3.4680 | 17850 | 0.293 |
| 3.4778 | 17900 | 0.2941 |
| 3.4875 | 17950 | 0.3 |
| 3.4972 | 18000 | 0.3022 |
| 3.5069 | 18050 | 0.3049 |
| 3.5166 | 18100 | 0.3067 |
| 3.5263 | 18150 | 0.2934 |
| 3.5360 | 18200 | 0.312 |
| 3.5458 | 18250 | 0.2823 |
| 3.5555 | 18300 | 0.2746 |
| 3.5652 | 18350 | 0.2971 |
| 3.5749 | 18400 | 0.2827 |
| 3.5846 | 18450 | 0.2718 |
| 3.5943 | 18500 | 0.2908 |
| 3.6040 | 18550 | 0.2911 |
| 3.6138 | 18600 | 0.3008 |
| 3.6235 | 18650 | 0.3058 |
| 3.6332 | 18700 | 0.304 |
| 3.6429 | 18750 | 0.284 |
| 3.6526 | 18800 | 0.3037 |
| 3.6623 | 18850 | 0.2768 |
| 3.6720 | 18900 | 0.3287 |
| 3.6818 | 18950 | 0.2768 |
| 3.6915 | 19000 | 0.316 |
| 3.7012 | 19050 | 0.2786 |
| 3.7109 | 19100 | 0.2746 |
| 3.7206 | 19150 | 0.2794 |
| 3.7303 | 19200 | 0.2869 |
| 3.7400 | 19250 | 0.2836 |
| 3.7498 | 19300 | 0.2982 |
| 3.7595 | 19350 | 0.3143 |
| 3.7692 | 19400 | 0.2942 |
| 3.7789 | 19450 | 0.2693 |
| 3.7886 | 19500 | 0.2894 |
| 3.7983 | 19550 | 0.3009 |
| 3.8080 | 19600 | 0.2893 |
| 3.8178 | 19650 | 0.2915 |
| 3.8275 | 19700 | 0.2991 |
| 3.8372 | 19750 | 0.2857 |
| 3.8469 | 19800 | 0.3028 |
| 3.8566 | 19850 | 0.3068 |
| 3.8663 | 19900 | 0.2955 |
| 3.8760 | 19950 | 0.3119 |
| 3.8858 | 20000 | 0.3364 |
| 3.8955 | 20050 | 0.0993 |
| 3.9052 | 20100 | 0.1208 |
| 3.9149 | 20150 | 0.1015 |
| 3.9246 | 20200 | 0.1422 |
| 3.9343 | 20250 | 0.1879 |
| 3.9440 | 20300 | 0.1437 |
| 3.9538 | 20350 | 0.1556 |
| 3.9635 | 20400 | 0.1279 |
| 3.9732 | 20450 | 0.1384 |
| 3.9829 | 20500 | 0.1556 |
| 3.9926 | 20550 | 0.1508 |
| 4.0023 | 20600 | 0.1812 |
| 4.0120 | 20650 | 0.2858 |
| 4.0218 | 20700 | 0.2807 |
| 4.0315 | 20750 | 0.3016 |
| 4.0412 | 20800 | 0.2611 |
| 4.0509 | 20850 | 0.3031 |
| 4.0606 | 20900 | 0.2772 |
| 4.0703 | 20950 | 0.2776 |
| 4.0800 | 21000 | 0.2556 |
| 4.0898 | 21050 | 0.2744 |
| 4.0995 | 21100 | 0.2825 |
| 4.1092 | 21150 | 0.2664 |
| 4.1189 | 21200 | 0.2772 |
| 4.1286 | 21250 | 0.2767 |
| 4.1383 | 21300 | 0.2562 |
| 4.1480 | 21350 | 0.256 |
| 4.1578 | 21400 | 0.2824 |
| 4.1675 | 21450 | 0.2762 |
| 4.1772 | 21500 | 0.2766 |
| 4.1869 | 21550 | 0.291 |
| 4.1966 | 21600 | 0.2636 |
| 4.2063 | 21650 | 0.2751 |
| 4.2160 | 21700 | 0.2739 |
| 4.2258 | 21750 | 0.2982 |
| 4.2355 | 21800 | 0.2881 |
| 4.2452 | 21850 | 0.2687 |
| 4.2549 | 21900 | 0.2644 |
| 4.2646 | 21950 | 0.2827 |
| 4.2743 | 22000 | 0.2591 |
| 4.2840 | 22050 | 0.2645 |
| 4.2938 | 22100 | 0.2786 |
| 4.3035 | 22150 | 0.2693 |
| 4.3132 | 22200 | 0.2909 |
| 4.3229 | 22250 | 0.2838 |
| 4.3326 | 22300 | 0.2901 |
| 4.3423 | 22350 | 0.2629 |
| 4.3520 | 22400 | 0.2672 |
| 4.3618 | 22450 | 0.2962 |
| 4.3715 | 22500 | 0.2742 |
| 4.3812 | 22550 | 0.2811 |
| 4.3909 | 22600 | 0.2639 |
| 4.4006 | 22650 | 0.244 |
| 4.4103 | 22700 | 0.2866 |
| 4.4201 | 22750 | 0.2968 |
| 4.4298 | 22800 | 0.2828 |
| 4.4395 | 22850 | 0.2515 |
| 4.4492 | 22900 | 0.282 |
| 4.4589 | 22950 | 0.282 |
| 4.4686 | 23000 | 0.2776 |
| 4.4783 | 23050 | 0.2795 |
| 4.4881 | 23100 | 0.2701 |
| 4.4978 | 23150 | 0.2808 |
| 4.5075 | 23200 | 0.2651 |
| 4.5172 | 23250 | 0.2631 |
| 4.5269 | 23300 | 0.2911 |
| 4.5366 | 23350 | 0.2615 |
| 4.5463 | 23400 | 0.2772 |
| 4.5561 | 23450 | 0.2826 |
| 4.5658 | 23500 | 0.2797 |
| 4.5755 | 23550 | 0.2954 |
| 4.5852 | 23600 | 0.2816 |
| 4.5949 | 23650 | 0.2889 |
| 4.6046 | 23700 | 0.2647 |
| 4.6143 | 23750 | 0.2882 |
| 4.6241 | 23800 | 0.2709 |
| 4.6338 | 23850 | 0.2794 |
| 4.6435 | 23900 | 0.2702 |
| 4.6532 | 23950 | 0.2527 |
| 4.6629 | 24000 | 0.2642 |
| 4.6726 | 24050 | 0.2808 |
| 4.6823 | 24100 | 0.2764 |
| 4.6921 | 24150 | 0.2583 |
| 4.7018 | 24200 | 0.2286 |
| 4.7115 | 24250 | 0.2707 |
| 4.7212 | 24300 | 0.2793 |
| 4.7309 | 24350 | 0.2593 |
| 4.7406 | 24400 | 0.2779 |
| 4.7503 | 24450 | 0.3168 |
| 4.7601 | 24500 | 0.2943 |
| 4.7698 | 24550 | 0.3078 |
| 4.7795 | 24600 | 0.2735 |
| 4.7892 | 24650 | 0.2846 |
| 4.7989 | 24700 | 0.2571 |
| 4.8086 | 24750 | 0.2785 |
| 4.8183 | 24800 | 0.2753 |
| 4.8281 | 24850 | 0.2943 |
| 4.8378 | 24900 | 0.264 |
| 4.8475 | 24950 | 0.2962 |
| 4.8572 | 25000 | 0.2743 |
| 4.8669 | 25050 | 0.2748 |
| 4.8766 | 25100 | 0.3039 |
| 4.8863 | 25150 | 0.2817 |
| 4.8961 | 25200 | 0.1467 |
| 4.9058 | 25250 | 0.1224 |
| 4.9155 | 25300 | 0.0547 |
| 4.9252 | 25350 | 0.1329 |
| 4.9349 | 25400 | 0.086 |
| 4.9446 | 25450 | 0.1423 |
| 4.9543 | 25500 | 0.0783 |
| 4.9641 | 25550 | 0.1377 |
| 4.9738 | 25600 | 0.0743 |
| 4.9835 | 25650 | 0.0879 |
| 4.9932 | 25700 | 0.1108 |