---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11312
- loss:MultipleNegativesRankingLoss
base_model: malteos/PubMedNCL
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 malteos/PubMedNCL
results:
- task:
type: triplet
name: Triplet
dataset:
name: triplet dev
type: triplet-dev
metrics:
- type: cosine_accuracy
value: 0.65
name: Cosine Accuracy
---
# SentenceTransformer based on malteos/PubMedNCL
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [malteos/PubMedNCL](https://huggingface.co/malteos/PubMedNCL) on the json dataset. 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:** [malteos/PubMedNCL](https://huggingface.co/malteos/PubMedNCL)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 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': 512, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction
(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("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, 768]
# 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** | **0.65** |
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 11,312 training 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: 7.33 tokens
- max: 37 tokens
| - min: 4 tokens
- mean: 19.54 tokens
- max: 67 tokens
| - min: 4 tokens
- mean: 11.81 tokens
- max: 45 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 | - | 0.541 |
| 0.0032 | 1 | 1.8857 | - |
| 0.0064 | 2 | 1.3201 | - |
| 0.0096 | 3 | 1.6458 | - |
| 0.0128 | 4 | 1.783 | - |
| 0.0160 | 5 | 1.6226 | - |
| 0.0192 | 6 | 1.7636 | - |
| 0.0224 | 7 | 1.6457 | - |
| 0.0256 | 8 | 1.7128 | - |
| 0.0288 | 9 | 1.6293 | - |
| 0.0319 | 10 | 1.8555 | - |
| 0.0351 | 11 | 1.9232 | - |
| 0.0383 | 12 | 1.5314 | - |
| 0.0415 | 13 | 1.6542 | - |
| 0.0447 | 14 | 1.5947 | - |
| 0.0479 | 15 | 1.849 | - |
| 0.0511 | 16 | 1.5738 | - |
| 0.0543 | 17 | 1.556 | - |
| 0.0575 | 18 | 1.5806 | - |
| 0.0607 | 19 | 1.5298 | - |
| 0.0639 | 20 | 1.7878 | - |
| 0.0671 | 21 | 1.7792 | - |
| 0.0703 | 22 | 1.7247 | - |
| 0.0735 | 23 | 1.4215 | - |
| 0.0767 | 24 | 1.7255 | - |
| 0.0799 | 25 | 1.3724 | - |
| 0.0831 | 26 | 1.7002 | - |
| 0.0863 | 27 | 1.4362 | - |
| 0.0895 | 28 | 1.2914 | - |
| 0.0927 | 29 | 1.8951 | - |
| 0.0958 | 30 | 1.7748 | - |
| 0.0990 | 31 | 1.5147 | - |
| 0.1022 | 32 | 1.4271 | - |
| 0.1054 | 33 | 1.401 | - |
| 0.1086 | 34 | 1.5386 | - |
| 0.1118 | 35 | 1.1083 | - |
| 0.1150 | 36 | 1.6985 | - |
| 0.1182 | 37 | 1.3883 | - |
| 0.1214 | 38 | 1.2327 | - |
| 0.1246 | 39 | 1.1182 | - |
| 0.1278 | 40 | 1.1503 | - |
| 0.1310 | 41 | 1.0549 | - |
| 0.1342 | 42 | 1.1005 | - |
| 0.1374 | 43 | 1.2327 | - |
| 0.1406 | 44 | 1.4466 | - |
| 0.1438 | 45 | 1.053 | - |
| 0.1470 | 46 | 1.2527 | - |
| 0.1502 | 47 | 1.2469 | - |
| 0.1534 | 48 | 1.4009 | - |
| 0.1565 | 49 | 0.9248 | - |
| 0.1597 | 50 | 1.5937 | - |
| 0.1629 | 51 | 1.4656 | - |
| 0.1661 | 52 | 1.4136 | - |
| 0.1693 | 53 | 1.178 | - |
| 0.1725 | 54 | 1.3482 | - |
| 0.1757 | 55 | 1.2768 | - |
| 0.1789 | 56 | 1.2803 | - |
| 0.1821 | 57 | 1.3748 | - |
| 0.1853 | 58 | 1.3586 | - |
| 0.1885 | 59 | 1.2199 | - |
| 0.1917 | 60 | 1.3183 | - |
| 0.1949 | 61 | 1.4524 | - |
| 0.1981 | 62 | 0.8348 | - |
| 0.2013 | 63 | 1.123 | - |
| 0.2045 | 64 | 1.076 | - |
| 0.2077 | 65 | 0.8969 | - |
| 0.2109 | 66 | 0.7729 | - |
| 0.2141 | 67 | 1.1902 | - |
| 0.2173 | 68 | 1.4572 | - |
| 0.2204 | 69 | 1.2323 | - |
| 0.2236 | 70 | 1.1836 | - |
| 0.2268 | 71 | 0.9406 | - |
| 0.2300 | 72 | 1.1957 | - |
| 0.2332 | 73 | 0.7556 | - |
| 0.2364 | 74 | 1.1107 | - |
| 0.2396 | 75 | 0.776 | - |
| 0.2428 | 76 | 0.9051 | - |
| 0.2460 | 77 | 1.2314 | - |
| 0.2492 | 78 | 1.2717 | - |
| 0.2524 | 79 | 1.1385 | - |
| 0.2556 | 80 | 0.9591 | - |
| 0.2588 | 81 | 1.2769 | - |
| 0.2620 | 82 | 0.9365 | - |
| 0.2652 | 83 | 1.0268 | - |
| 0.2684 | 84 | 1.2769 | - |
| 0.2716 | 85 | 1.4569 | - |
| 0.2748 | 86 | 1.2353 | - |
| 0.2780 | 87 | 1.1564 | - |
| 0.2812 | 88 | 1.128 | - |
| 0.2843 | 89 | 1.2359 | - |
| 0.2875 | 90 | 1.0234 | - |
| 0.2907 | 91 | 0.9329 | - |
| 0.2939 | 92 | 0.9122 | - |
| 0.2971 | 93 | 1.046 | - |
| 0.3003 | 94 | 1.1084 | - |
| 0.3035 | 95 | 1.5154 | - |
| 0.3067 | 96 | 1.394 | - |
| 0.3099 | 97 | 0.9329 | - |
| 0.3131 | 98 | 1.1751 | - |
| 0.3163 | 99 | 1.4136 | - |
| 0.3195 | 100 | 1.0859 | 0.6 |
| 0.3227 | 101 | 1.3132 | - |
| 0.3259 | 102 | 1.1107 | - |
| 0.3291 | 103 | 1.1071 | - |
| 0.3323 | 104 | 1.3991 | - |
| 0.3355 | 105 | 1.1542 | - |
| 0.3387 | 106 | 1.5527 | - |
| 0.3419 | 107 | 1.3701 | - |
| 0.3450 | 108 | 1.1583 | - |
| 0.3482 | 109 | 1.1743 | - |
| 0.3514 | 110 | 0.9375 | - |
| 0.3546 | 111 | 1.0193 | - |
| 0.3578 | 112 | 0.9705 | - |
| 0.3610 | 113 | 1.2329 | - |
| 0.3642 | 114 | 1.0263 | - |
| 0.3674 | 115 | 1.1292 | - |
| 0.3706 | 116 | 0.9325 | - |
| 0.3738 | 117 | 1.0293 | - |
| 0.3770 | 118 | 1.0638 | - |
| 0.3802 | 119 | 1.0024 | - |
| 0.3834 | 120 | 1.1966 | - |
| 0.3866 | 121 | 0.874 | - |
| 0.3898 | 122 | 1.1094 | - |
| 0.3930 | 123 | 1.1334 | - |
| 0.3962 | 124 | 1.5534 | - |
| 0.3994 | 125 | 0.8601 | - |
| 0.4026 | 126 | 1.172 | - |
| 0.4058 | 127 | 0.9888 | - |
| 0.4089 | 128 | 1.1072 | - |
| 0.4121 | 129 | 0.9179 | - |
| 0.4153 | 130 | 0.8901 | - |
| 0.4185 | 131 | 1.2932 | - |
| 0.4217 | 132 | 0.8809 | - |
| 0.4249 | 133 | 1.407 | - |
| 0.4281 | 134 | 1.1723 | - |
| 0.4313 | 135 | 0.7617 | - |
| 0.4345 | 136 | 0.8623 | - |
| 0.4377 | 137 | 1.1092 | - |
| 0.4409 | 138 | 0.9422 | - |
| 0.4441 | 139 | 0.8478 | - |
| 0.4473 | 140 | 1.0439 | - |
| 0.4505 | 141 | 0.9857 | - |
| 0.4537 | 142 | 0.8718 | - |
| 0.4569 | 143 | 1.0178 | - |
| 0.4601 | 144 | 1.4263 | - |
| 0.4633 | 145 | 0.9818 | - |
| 0.4665 | 146 | 1.1999 | - |
| 0.4696 | 147 | 1.0042 | - |
| 0.4728 | 148 | 0.7386 | - |
| 0.4760 | 149 | 0.8121 | - |
| 0.4792 | 150 | 0.982 | - |
| 0.4824 | 151 | 0.9998 | - |
| 0.4856 | 152 | 1.2617 | - |
| 0.4888 | 153 | 1.124 | - |
| 0.4920 | 154 | 0.948 | - |
| 0.4952 | 155 | 1.1027 | - |
| 0.4984 | 156 | 0.8592 | - |
| 0.5016 | 157 | 0.7257 | - |
| 0.5048 | 158 | 1.1329 | - |
| 0.5080 | 159 | 0.7886 | - |
| 0.5112 | 160 | 1.1468 | - |
| 0.5144 | 161 | 0.8234 | - |
| 0.5176 | 162 | 1.0084 | - |
| 0.5208 | 163 | 1.3117 | - |
| 0.5240 | 164 | 0.6839 | - |
| 0.5272 | 165 | 1.0097 | - |
| 0.5304 | 166 | 1.3979 | - |
| 0.5335 | 167 | 0.9312 | - |
| 0.5367 | 168 | 1.1595 | - |
| 0.5399 | 169 | 0.9771 | - |
| 0.5431 | 170 | 0.8747 | - |
| 0.5463 | 171 | 0.9973 | - |
| 0.5495 | 172 | 1.1271 | - |
| 0.5527 | 173 | 1.5213 | - |
| 0.5559 | 174 | 0.7934 | - |
| 0.5591 | 175 | 0.9291 | - |
| 0.5623 | 176 | 1.1036 | - |
| 0.5655 | 177 | 1.0352 | - |
| 0.5687 | 178 | 1.0123 | - |
| 0.5719 | 179 | 0.8707 | - |
| 0.5751 | 180 | 0.8158 | - |
| 0.5783 | 181 | 1.0186 | - |
| 0.5815 | 182 | 0.9716 | - |
| 0.5847 | 183 | 0.6801 | - |
| 0.5879 | 184 | 0.9617 | - |
| 0.5911 | 185 | 0.7656 | - |
| 0.5942 | 186 | 1.1093 | - |
| 0.5974 | 187 | 0.8643 | - |
| 0.6006 | 188 | 0.7412 | - |
| 0.6038 | 189 | 1.097 | - |
| 0.6070 | 190 | 0.6598 | - |
| 0.6102 | 191 | 0.8787 | - |
| 0.6134 | 192 | 0.8798 | - |
| 0.6166 | 193 | 1.1196 | - |
| 0.6198 | 194 | 0.7264 | - |
| 0.6230 | 195 | 0.9405 | - |
| 0.6262 | 196 | 0.9194 | - |
| 0.6294 | 197 | 1.4257 | - |
| 0.6326 | 198 | 0.8355 | - |
| 0.6358 | 199 | 0.9674 | - |
| 0.6390 | 200 | 0.6853 | 0.638 |
| 0.6422 | 201 | 1.2965 | - |
| 0.6454 | 202 | 1.1806 | - |
| 0.6486 | 203 | 1.1466 | - |
| 0.6518 | 204 | 0.8743 | - |
| 0.6550 | 205 | 1.1603 | - |
| 0.6581 | 206 | 1.333 | - |
| 0.6613 | 207 | 1.211 | - |
| 0.6645 | 208 | 1.3726 | - |
| 0.6677 | 209 | 0.6753 | - |
| 0.6709 | 210 | 0.8125 | - |
| 0.6741 | 211 | 0.9256 | - |
| 0.6773 | 212 | 1.0996 | - |
| 0.6805 | 213 | 0.9329 | - |
| 0.6837 | 214 | 0.9108 | - |
| 0.6869 | 215 | 1.1639 | - |
| 0.6901 | 216 | 0.9787 | - |
| 0.6933 | 217 | 1.0471 | - |
| 0.6965 | 218 | 1.3486 | - |
| 0.6997 | 219 | 1.1849 | - |
| 0.7029 | 220 | 1.023 | - |
| 0.7061 | 221 | 1.1853 | - |
| 0.7093 | 222 | 1.0969 | - |
| 0.7125 | 223 | 0.9121 | - |
| 0.7157 | 224 | 1.1646 | - |
| 0.7188 | 225 | 0.6575 | - |
| 0.7220 | 226 | 0.9888 | - |
| 0.7252 | 227 | 0.8568 | - |
| 0.7284 | 228 | 1.0076 | - |
| 0.7316 | 229 | 0.9794 | - |
| 0.7348 | 230 | 1.1174 | - |
| 0.7380 | 231 | 1.078 | - |
| 0.7412 | 232 | 0.6901 | - |
| 0.7444 | 233 | 1.0532 | - |
| 0.7476 | 234 | 1.0519 | - |
| 0.7508 | 235 | 1.1772 | - |
| 0.7540 | 236 | 0.89 | - |
| 0.7572 | 237 | 0.9911 | - |
| 0.7604 | 238 | 1.0053 | - |
| 0.7636 | 239 | 1.0855 | - |
| 0.7668 | 240 | 1.1801 | - |
| 0.7700 | 241 | 0.9228 | - |
| 0.7732 | 242 | 0.5901 | - |
| 0.7764 | 243 | 1.0322 | - |
| 0.7796 | 244 | 1.1607 | - |
| 0.7827 | 245 | 0.937 | - |
| 0.7859 | 246 | 1.0137 | - |
| 0.7891 | 247 | 1.2338 | - |
| 0.7923 | 248 | 0.672 | - |
| 0.7955 | 249 | 0.8709 | - |
| 0.7987 | 250 | 0.9364 | - |
| 0.8019 | 251 | 1.4397 | - |
| 0.8051 | 252 | 0.9922 | - |
| 0.8083 | 253 | 0.8738 | - |
| 0.8115 | 254 | 1.2506 | - |
| 0.8147 | 255 | 1.0251 | - |
| 0.8179 | 256 | 0.7608 | - |
| 0.8211 | 257 | 0.7537 | - |
| 0.8243 | 258 | 1.0931 | - |
| 0.8275 | 259 | 0.7419 | - |
| 0.8307 | 260 | 1.0598 | - |
| 0.8339 | 261 | 1.2947 | - |
| 0.8371 | 262 | 0.9113 | - |
| 0.8403 | 263 | 1.1814 | - |
| 0.8435 | 264 | 1.008 | - |
| 0.8466 | 265 | 0.8872 | - |
| 0.8498 | 266 | 1.0446 | - |
| 0.8530 | 267 | 1.0517 | - |
| 0.8562 | 268 | 1.6135 | - |
| 0.8594 | 269 | 0.6549 | - |
| 0.8626 | 270 | 1.1515 | - |
| 0.8658 | 271 | 0.9095 | - |
| 0.8690 | 272 | 0.9574 | - |
| 0.8722 | 273 | 1.4922 | - |
| 0.8754 | 274 | 1.0787 | - |
| 0.8786 | 275 | 0.9104 | - |
| 0.8818 | 276 | 1.009 | - |
| 0.8850 | 277 | 1.0063 | - |
| 0.8882 | 278 | 0.842 | - |
| 0.8914 | 279 | 0.9313 | - |
| 0.8946 | 280 | 0.9677 | - |
| 0.8978 | 281 | 0.83 | - |
| 0.9010 | 282 | 1.1904 | - |
| 0.9042 | 283 | 1.3531 | - |
| 0.9073 | 284 | 0.7808 | - |
| 0.9105 | 285 | 0.6189 | - |
| 0.9137 | 286 | 1.1642 | - |
| 0.9169 | 287 | 0.7282 | - |
| 0.9201 | 288 | 1.0109 | - |
| 0.9233 | 289 | 0.7644 | - |
| 0.9265 | 290 | 1.3702 | - |
| 0.9297 | 291 | 0.9911 | - |
| 0.9329 | 292 | 1.0527 | - |
| 0.9361 | 293 | 1.1148 | - |
| 0.9393 | 294 | 0.995 | - |
| 0.9425 | 295 | 0.7739 | - |
| 0.9457 | 296 | 1.1728 | - |
| 0.9489 | 297 | 1.3264 | - |
| 0.9521 | 298 | 1.0306 | - |
| 0.9553 | 299 | 1.0521 | - |
| 0.9585 | 300 | 0.7472 | 0.649 |
| 0.9617 | 301 | 0.9635 | - |
| 0.9649 | 302 | 1.1699 | - |
| 0.9681 | 303 | 1.143 | - |
| 0.9712 | 304 | 0.939 | - |
| 0.9744 | 305 | 1.3473 | - |
| 0.9776 | 306 | 1.2086 | - |
| 0.9808 | 307 | 1.0876 | - |
| 0.9840 | 308 | 0.866 | - |
| 0.9872 | 309 | 0.9147 | - |
| 0.9904 | 310 | 1.1839 | - |
| 0.9936 | 311 | 1.0603 | - |
| 0.9968 | 312 | 1.0036 | - |
| 1.0 | 313 | 1.0408 | 0.65 |