---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:15182
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3-retromae
widget:
- source_sentence: Carditis in pediatric patients following foreign serum administration
sentences:
- 'Four cases of carditis occurring in children and associated with the administration
of a foreign serum. '
- 'Understanding Positive Youth Development in Sport Through the Voices of Indigenous
Youth. '
- 'Pericarditis in children. '
- source_sentence: Concept Synthesis
sentences:
- 'Centeredness in Healthcare: A Concept Synthesis of Family-centered Care, Person-centered
Care and Child-centered Care. '
- 'The Power in Concept Mapping! '
- 'Using propensity scores to estimate the cost-effectiveness of medical therapies. '
- source_sentence: Visual Pathway Mapping
sentences:
- 'The visual connection. '
- 'The "tobacco issue". '
- 'Elaboration of the Visual Pathways from the Study of War-Related Cranial Injuries:
The Period from the Russo-Japanese War to World War I. '
- source_sentence: Cerebral Aneurysm Thrombosis
sentences:
- '[A case of spontaneous thrombosis of a cerebral arteriovenous aneurysm]. '
- 'Cerebral Sinus Thrombosis. '
- 'Good clinical practice (GCP) standards: clinical trials in India. An interview
with Dr. Urmila Thatte, Head of Clinical Pharmacology, TN Medical College & BYL
Nair Hospital. Interview by Viveka Roychowdhury. '
- source_sentence: Calcineurin inhibitor-sparing regimen
sentences:
- 'Belatacept-based immunosuppression: A calcineurin inhibitor-sparing regimen in
heart transplant recipients. '
- 'The Outcomes of Cemented Femoral Revisions for Periprosthetic Femoral Fractures
in the Elderly: Comparison with Cementless Stems. '
- 'Neurotoxicity of calcineurin inhibitors: impact and clinical management. '
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on BAAI/bge-m3-retromae
results:
- task:
type: triplet
name: Triplet
dataset:
name: triplet dev
type: triplet-dev
metrics:
- type: cosine_accuracy
value: 0.723
name: Cosine Accuracy
---
# SentenceTransformer based on BAAI/bge-m3-retromae
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) on the json 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:** [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae)
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
### 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': 8192, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction
(1): Pooling({'word_embedding_dimension': 1024, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'Calcineurin inhibitor-sparing regimen',
'Belatacept-based immunosuppression: A calcineurin inhibitor-sparing regimen in heart transplant recipients. ',
'Neurotoxicity of calcineurin inhibitors: impact and clinical management. ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Triplet
* Dataset: `triplet-dev`
* Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:--------------------|:----------|
| **cosine_accuracy** | **0.723** |
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 15,182 training samples
* Columns: anchor, positive, and negative
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | - min: 4 tokens
- mean: 10.68 tokens
- max: 49 tokens
| - min: 6 tokens
- mean: 26.34 tokens
- max: 79 tokens
| - min: 4 tokens
- mean: 15.75 tokens
- max: 66 tokens
|
* Samples:
| anchor | positive | negative |
|:--------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------|
| Immunogenetic polymorphism | Immunogenetic polymorphism and disease mechanisms in juvenile chronic arthritis. | Immunogenetic model. |
| Alemtuzumab-induced pancolitis | Pancolitis a novel early complication of Alemtuzumab for MS treatment. | Alemtuzumab in lymphoproliferate disorders. |
| Intermittent infectiousness | Understanding the effects of intermittent shedding on the transmission of infectious diseases: example of salmonellosis in pigs. | Infectious behaviour. |
* 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
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `num_train_epochs`: 1
- `lr_scheduler_type`: cosine_with_restarts
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `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`: 5e-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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: cosine_with_restarts
- `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`: True
- `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}
- `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
- `eval_use_gather_object`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | triplet-dev_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------------------:|
| 0 | 0 | - | 0.543 |
| 0.0032 | 1 | 3.4406 | - |
| 0.0064 | 2 | 3.2403 | - |
| 0.0096 | 3 | 3.3734 | - |
| 0.0128 | 4 | 3.3858 | - |
| 0.0160 | 5 | 3.3195 | - |
| 0.0192 | 6 | 3.2708 | - |
| 0.0224 | 7 | 3.4507 | - |
| 0.0256 | 8 | 3.4782 | - |
| 0.0288 | 9 | 3.2926 | - |
| 0.0319 | 10 | 3.2744 | - |
| 0.0351 | 11 | 3.4455 | - |
| 0.0383 | 12 | 3.3225 | - |
| 0.0415 | 13 | 3.3568 | - |
| 0.0447 | 14 | 3.3349 | - |
| 0.0479 | 15 | 3.2672 | - |
| 0.0511 | 16 | 3.2584 | - |
| 0.0543 | 17 | 3.1607 | - |
| 0.0575 | 18 | 3.1793 | - |
| 0.0607 | 19 | 3.1924 | - |
| 0.0639 | 20 | 3.2913 | - |
| 0.0671 | 21 | 3.2028 | - |
| 0.0703 | 22 | 3.1448 | - |
| 0.0735 | 23 | 3.0991 | - |
| 0.0767 | 24 | 3.1371 | - |
| 0.0799 | 25 | 3.0089 | - |
| 0.0831 | 26 | 3.1232 | - |
| 0.0863 | 27 | 2.8794 | - |
| 0.0895 | 28 | 2.982 | - |
| 0.0927 | 29 | 3.231 | - |
| 0.0958 | 30 | 2.9288 | - |
| 0.0990 | 31 | 3.0117 | - |
| 0.1022 | 32 | 2.8717 | - |
| 0.1054 | 33 | 2.7002 | - |
| 0.1086 | 34 | 2.6395 | - |
| 0.1118 | 35 | 2.5087 | - |
| 0.1150 | 36 | 2.7469 | - |
| 0.1182 | 37 | 2.6306 | - |
| 0.1214 | 38 | 2.1149 | - |
| 0.1246 | 39 | 2.5591 | - |
| 0.1278 | 40 | 2.0133 | - |
| 0.1310 | 41 | 2.2863 | - |
| 0.1342 | 42 | 2.2592 | - |
| 0.1374 | 43 | 2.1261 | - |
| 0.1406 | 44 | 2.278 | - |
| 0.1438 | 45 | 1.7339 | - |
| 0.1470 | 46 | 1.8337 | - |
| 0.1502 | 47 | 1.5944 | - |
| 0.1534 | 48 | 2.0899 | - |
| 0.1565 | 49 | 1.509 | - |
| 0.1597 | 50 | 1.8651 | - |
| 0.1629 | 51 | 2.2858 | - |
| 0.1661 | 52 | 2.6881 | - |
| 0.1693 | 53 | 1.7877 | - |
| 0.1725 | 54 | 1.6374 | - |
| 0.1757 | 55 | 2.0763 | - |
| 0.1789 | 56 | 1.7672 | - |
| 0.1821 | 57 | 1.7913 | - |
| 0.1853 | 58 | 1.8524 | - |
| 0.1885 | 59 | 2.2614 | - |
| 0.1917 | 60 | 1.8058 | - |
| 0.1949 | 61 | 2.0403 | - |
| 0.1981 | 62 | 1.2697 | - |
| 0.2013 | 63 | 1.9523 | - |
| 0.2045 | 64 | 1.3965 | - |
| 0.2077 | 65 | 1.5501 | - |
| 0.2109 | 66 | 1.0785 | - |
| 0.2141 | 67 | 1.721 | - |
| 0.2173 | 68 | 1.9049 | - |
| 0.2204 | 69 | 1.4317 | - |
| 0.2236 | 70 | 1.905 | - |
| 0.2268 | 71 | 1.236 | - |
| 0.2300 | 72 | 1.7312 | - |
| 0.2332 | 73 | 0.9951 | - |
| 0.2364 | 74 | 1.5471 | - |
| 0.2396 | 75 | 1.1289 | - |
| 0.2428 | 76 | 1.7902 | - |
| 0.2460 | 77 | 1.2619 | - |
| 0.2492 | 78 | 1.0043 | - |
| 0.2524 | 79 | 1.7546 | - |
| 0.2556 | 80 | 1.8505 | - |
| 0.2588 | 81 | 1.7437 | - |
| 0.2620 | 82 | 1.2788 | - |
| 0.2652 | 83 | 1.438 | - |
| 0.2684 | 84 | 1.5399 | - |
| 0.2716 | 85 | 2.1841 | - |
| 0.2748 | 86 | 1.6834 | - |
| 0.2780 | 87 | 1.3842 | - |
| 0.2812 | 88 | 1.619 | - |
| 0.2843 | 89 | 1.2492 | - |
| 0.2875 | 90 | 1.3613 | - |
| 0.2907 | 91 | 1.2457 | - |
| 0.2939 | 92 | 1.2966 | - |
| 0.2971 | 93 | 1.3718 | - |
| 0.3003 | 94 | 1.3675 | - |
| 0.3035 | 95 | 2.1095 | - |
| 0.3067 | 96 | 1.6177 | - |
| 0.3099 | 97 | 1.3287 | - |
| 0.3131 | 98 | 1.9805 | - |
| 0.3163 | 99 | 1.3861 | - |
| 0.3195 | 100 | 1.8392 | 0.622 |
| 0.3227 | 101 | 1.4698 | - |
| 0.3259 | 102 | 1.4499 | - |
| 0.3291 | 103 | 1.5338 | - |
| 0.3323 | 104 | 1.3867 | - |
| 0.3355 | 105 | 1.7414 | - |
| 0.3387 | 106 | 1.5203 | - |
| 0.3419 | 107 | 1.6059 | - |
| 0.3450 | 108 | 1.3799 | - |
| 0.3482 | 109 | 1.5004 | - |
| 0.3514 | 110 | 1.0175 | - |
| 0.3546 | 111 | 1.0399 | - |
| 0.3578 | 112 | 1.6369 | - |
| 0.3610 | 113 | 1.5692 | - |
| 0.3642 | 114 | 1.6808 | - |
| 0.3674 | 115 | 1.4315 | - |
| 0.3706 | 116 | 0.9854 | - |
| 0.3738 | 117 | 1.3637 | - |
| 0.3770 | 118 | 1.3986 | - |
| 0.3802 | 119 | 1.3848 | - |
| 0.3834 | 120 | 1.4436 | - |
| 0.3866 | 121 | 1.0704 | - |
| 0.3898 | 122 | 1.3788 | - |
| 0.3930 | 123 | 1.7131 | - |
| 0.3962 | 124 | 1.5013 | - |
| 0.3994 | 125 | 1.7377 | - |
| 0.4026 | 126 | 2.0296 | - |
| 0.4058 | 127 | 1.2643 | - |
| 0.4089 | 128 | 1.3647 | - |
| 0.4121 | 129 | 1.175 | - |
| 0.4153 | 130 | 1.0797 | - |
| 0.4185 | 131 | 1.5746 | - |
| 0.4217 | 132 | 1.0914 | - |
| 0.4249 | 133 | 1.6672 | - |
| 0.4281 | 134 | 1.2959 | - |
| 0.4313 | 135 | 1.5387 | - |
| 0.4345 | 136 | 1.2571 | - |
| 0.4377 | 137 | 1.42 | - |
| 0.4409 | 138 | 1.3452 | - |
| 0.4441 | 139 | 1.2238 | - |
| 0.4473 | 140 | 0.9963 | - |
| 0.4505 | 141 | 1.0326 | - |
| 0.4537 | 142 | 0.8793 | - |
| 0.4569 | 143 | 1.2197 | - |
| 0.4601 | 144 | 1.2992 | - |
| 0.4633 | 145 | 1.1456 | - |
| 0.4665 | 146 | 1.6002 | - |
| 0.4696 | 147 | 1.54 | - |
| 0.4728 | 148 | 1.2323 | - |
| 0.4760 | 149 | 1.0184 | - |
| 0.4792 | 150 | 1.2416 | - |
| 0.4824 | 151 | 1.1777 | - |
| 0.4856 | 152 | 1.0964 | - |
| 0.4888 | 153 | 1.0828 | - |
| 0.4920 | 154 | 1.3446 | - |
| 0.4952 | 155 | 0.9454 | - |
| 0.4984 | 156 | 0.7719 | - |
| 0.5016 | 157 | 1.003 | - |
| 0.5048 | 158 | 0.9863 | - |
| 0.5080 | 159 | 0.9672 | - |
| 0.5112 | 160 | 1.1432 | - |
| 0.5144 | 161 | 1.0377 | - |
| 0.5176 | 162 | 1.102 | - |
| 0.5208 | 163 | 0.9345 | - |
| 0.5240 | 164 | 0.9486 | - |
| 0.5272 | 165 | 1.5389 | - |
| 0.5304 | 166 | 1.8956 | - |
| 0.5335 | 167 | 1.0425 | - |
| 0.5367 | 168 | 1.5296 | - |
| 0.5399 | 169 | 0.9602 | - |
| 0.5431 | 170 | 0.9832 | - |
| 0.5463 | 171 | 1.0982 | - |
| 0.5495 | 172 | 1.6295 | - |
| 0.5527 | 173 | 1.3986 | - |
| 0.5559 | 174 | 1.1721 | - |
| 0.5591 | 175 | 0.7994 | - |
| 0.5623 | 176 | 1.5655 | - |
| 0.5655 | 177 | 1.2068 | - |
| 0.5687 | 178 | 1.2747 | - |
| 0.5719 | 179 | 1.0729 | - |
| 0.5751 | 180 | 0.9977 | - |
| 0.5783 | 181 | 1.3537 | - |
| 0.5815 | 182 | 1.0964 | - |
| 0.5847 | 183 | 0.8029 | - |
| 0.5879 | 184 | 0.765 | - |
| 0.5911 | 185 | 1.0457 | - |
| 0.5942 | 186 | 1.2928 | - |
| 0.5974 | 187 | 1.2354 | - |
| 0.6006 | 188 | 1.031 | - |
| 0.6038 | 189 | 1.2561 | - |
| 0.6070 | 190 | 1.1676 | - |
| 0.6102 | 191 | 1.2186 | - |
| 0.6134 | 192 | 1.1786 | - |
| 0.6166 | 193 | 1.283 | - |
| 0.6198 | 194 | 0.8316 | - |
| 0.6230 | 195 | 1.2239 | - |
| 0.6262 | 196 | 1.08 | - |
| 0.6294 | 197 | 1.7637 | - |
| 0.6326 | 198 | 1.2315 | - |
| 0.6358 | 199 | 1.5375 | - |
| 0.6390 | 200 | 1.4388 | 0.73 |
| 0.6422 | 201 | 1.3918 | - |
| 0.6454 | 202 | 1.37 | - |
| 0.6486 | 203 | 1.3753 | - |
| 0.6518 | 204 | 1.137 | - |
| 0.6550 | 205 | 1.4457 | - |
| 0.6581 | 206 | 1.3072 | - |
| 0.6613 | 207 | 2.0953 | - |
| 0.6645 | 208 | 1.6811 | - |
| 0.6677 | 209 | 0.9206 | - |
| 0.6709 | 210 | 0.9801 | - |
| 0.6741 | 211 | 0.961 | - |
| 0.6773 | 212 | 1.386 | - |
| 0.6805 | 213 | 1.5354 | - |
| 0.6837 | 214 | 0.6571 | - |
| 0.6869 | 215 | 1.2631 | - |
| 0.6901 | 216 | 1.2122 | - |
| 0.6933 | 217 | 1.6253 | - |
| 0.6965 | 218 | 1.266 | - |
| 0.6997 | 219 | 1.7445 | - |
| 0.7029 | 220 | 1.1527 | - |
| 0.7061 | 221 | 1.7681 | - |
| 0.7093 | 222 | 1.4941 | - |
| 0.7125 | 223 | 1.8236 | - |
| 0.7157 | 224 | 1.4117 | - |
| 0.7188 | 225 | 0.7363 | - |
| 0.7220 | 226 | 1.4519 | - |
| 0.7252 | 227 | 1.4138 | - |
| 0.7284 | 228 | 1.0758 | - |
| 0.7316 | 229 | 1.6275 | - |
| 0.7348 | 230 | 1.6303 | - |
| 0.7380 | 231 | 1.4706 | - |
| 0.7412 | 232 | 0.5958 | - |
| 0.7444 | 233 | 1.2442 | - |
| 0.7476 | 234 | 1.3782 | - |
| 0.7508 | 235 | 1.3971 | - |
| 0.7540 | 236 | 1.3412 | - |
| 0.7572 | 237 | 0.9017 | - |
| 0.7604 | 238 | 1.6336 | - |
| 0.7636 | 239 | 1.2652 | - |
| 0.7668 | 240 | 1.0598 | - |
| 0.7700 | 241 | 1.3082 | - |
| 0.7732 | 242 | 0.9677 | - |
| 0.7764 | 243 | 1.2684 | - |
| 0.7796 | 244 | 1.3539 | - |
| 0.7827 | 245 | 1.7301 | - |
| 0.7859 | 246 | 1.2539 | - |
| 0.7891 | 247 | 1.1073 | - |
| 0.7923 | 248 | 1.079 | - |
| 0.7955 | 249 | 1.3488 | - |
| 0.7987 | 250 | 1.0672 | - |
| 0.8019 | 251 | 1.4308 | - |
| 0.8051 | 252 | 1.126 | - |
| 0.8083 | 253 | 1.131 | - |
| 0.8115 | 254 | 0.9585 | - |
| 0.8147 | 255 | 0.9348 | - |
| 0.8179 | 256 | 1.1288 | - |
| 0.8211 | 257 | 1.2577 | - |
| 0.8243 | 258 | 1.286 | - |
| 0.8275 | 259 | 1.1985 | - |
| 0.8307 | 260 | 1.2386 | - |
| 0.8339 | 261 | 1.6239 | - |
| 0.8371 | 262 | 0.8122 | - |
| 0.8403 | 263 | 1.42 | - |
| 0.8435 | 264 | 0.9854 | - |
| 0.8466 | 265 | 0.9861 | - |
| 0.8498 | 266 | 1.2226 | - |
| 0.8530 | 267 | 1.1535 | - |
| 0.8562 | 268 | 1.634 | - |
| 0.8594 | 269 | 1.0699 | - |
| 0.8626 | 270 | 1.2927 | - |
| 0.8658 | 271 | 1.2269 | - |
| 0.8690 | 272 | 0.8528 | - |
| 0.8722 | 273 | 1.6345 | - |
| 0.8754 | 274 | 1.4596 | - |
| 0.8786 | 275 | 0.9795 | - |
| 0.8818 | 276 | 1.1772 | - |
| 0.8850 | 277 | 1.135 | - |
| 0.8882 | 278 | 0.994 | - |
| 0.8914 | 279 | 0.8705 | - |
| 0.8946 | 280 | 0.976 | - |
| 0.8978 | 281 | 1.2215 | - |
| 0.9010 | 282 | 1.4685 | - |
| 0.9042 | 283 | 1.6724 | - |
| 0.9073 | 284 | 1.3882 | - |
| 0.9105 | 285 | 1.2283 | - |
| 0.9137 | 286 | 1.0334 | - |
| 0.9169 | 287 | 1.2039 | - |
| 0.9201 | 288 | 1.0914 | - |
| 0.9233 | 289 | 1.7033 | - |
| 0.9265 | 290 | 1.7687 | - |
| 0.9297 | 291 | 1.2867 | - |
| 0.9329 | 292 | 1.196 | - |
| 0.9361 | 293 | 0.9771 | - |
| 0.9393 | 294 | 1.1878 | - |
| 0.9425 | 295 | 1.235 | - |
| 0.9457 | 296 | 1.4398 | - |
| 0.9489 | 297 | 1.475 | - |
| 0.9521 | 298 | 1.2632 | - |
| 0.9553 | 299 | 1.5732 | - |
| 0.9585 | 300 | 1.0147 | 0.725 |
| 0.9617 | 301 | 1.0345 | - |
| 0.9649 | 302 | 1.2582 | - |
| 0.9681 | 303 | 1.0398 | - |
| 0.9712 | 304 | 1.3973 | - |
| 0.9744 | 305 | 1.6701 | - |
| 0.9776 | 306 | 1.2617 | - |
| 0.9808 | 307 | 1.5779 | - |
| 0.9840 | 308 | 1.0839 | - |
| 0.9872 | 309 | 1.3117 | - |
| 0.9904 | 310 | 1.6139 | - |
| 0.9936 | 311 | 1.0128 | - |
| 0.9968 | 312 | 0.837 | - |
| 1.0 | 313 | 1.3687 | 0.723 |