---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:11172
- loss:MultipleNegativesRankingLoss
base_model: jxm/cde-small-v1
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 jxm/cde-small-v1
results:
- task:
type: triplet
name: Triplet
dataset:
name: triplet dev
type: triplet-dev
metrics:
- type: cosine_accuracy
value: 0.71
name: Cosine Accuracy
---
# SentenceTransformer based on jxm/cde-small-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jxm/cde-small-v1](https://huggingface.co/jxm/cde-small-v1) on the json dataset. It maps sentences & paragraphs to a None-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:** [jxm/cde-small-v1](https://huggingface.co/jxm/cde-small-v1)
- **Maximum Sequence Length:** None tokens
- **Output Dimensionality:** None 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({}) with Transformer model: PeftModelForFeatureExtraction
)
```
## 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** | **0.71** |
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 11,172 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: 9.5 tokens
- max: 42 tokens
| - min: 4 tokens
- mean: 23.37 tokens
- max: 82 tokens
| - min: 4 tokens
- mean: 14.13 tokens
- max: 59 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 | - | 0.566 |
| 0.0032 | 1 | 3.7786 | - |
| 0.0064 | 2 | 3.0207 | - |
| 0.0096 | 3 | 3.4699 | - |
| 0.0128 | 4 | 3.9642 | - |
| 0.0160 | 5 | 2.9183 | - |
| 0.0192 | 6 | 3.1035 | - |
| 0.0224 | 7 | 3.2608 | - |
| 0.0256 | 8 | 3.7062 | - |
| 0.0288 | 9 | 3.2258 | - |
| 0.0319 | 10 | 2.9817 | - |
| 0.0351 | 11 | 3.9166 | - |
| 0.0383 | 12 | 3.4075 | - |
| 0.0415 | 13 | 3.097 | - |
| 0.0447 | 14 | 2.6437 | - |
| 0.0479 | 15 | 2.751 | - |
| 0.0511 | 16 | 3.0026 | - |
| 0.0543 | 17 | 3.2352 | - |
| 0.0575 | 18 | 2.9763 | - |
| 0.0607 | 19 | 3.5151 | - |
| 0.0639 | 20 | 2.3222 | - |
| 0.0671 | 21 | 3.347 | - |
| 0.0703 | 22 | 2.7674 | - |
| 0.0735 | 23 | 3.2104 | - |
| 0.0767 | 24 | 2.7494 | - |
| 0.0799 | 25 | 3.209 | - |
| 0.0831 | 26 | 2.8368 | - |
| 0.0863 | 27 | 2.5643 | - |
| 0.0895 | 28 | 2.6125 | - |
| 0.0927 | 29 | 3.5675 | - |
| 0.0958 | 30 | 3.6836 | - |
| 0.0990 | 31 | 2.6156 | - |
| 0.1022 | 32 | 2.1644 | - |
| 0.1054 | 33 | 2.3822 | - |
| 0.1086 | 34 | 2.9865 | - |
| 0.1118 | 35 | 3.2025 | - |
| 0.1150 | 36 | 2.407 | - |
| 0.1182 | 37 | 1.8443 | - |
| 0.1214 | 38 | 1.8141 | - |
| 0.1246 | 39 | 1.9261 | - |
| 0.1278 | 40 | 2.3911 | - |
| 0.1310 | 41 | 2.5934 | - |
| 0.1342 | 42 | 2.6681 | - |
| 0.1374 | 43 | 2.0246 | - |
| 0.1406 | 44 | 2.027 | - |
| 0.1438 | 45 | 2.3277 | - |
| 0.1470 | 46 | 3.252 | - |
| 0.1502 | 47 | 2.1263 | - |
| 0.1534 | 48 | 3.0712 | - |
| 0.1565 | 49 | 2.077 | - |
| 0.1597 | 50 | 3.0536 | - |
| 0.1629 | 51 | 3.1603 | - |
| 0.1661 | 52 | 3.3711 | - |
| 0.1693 | 53 | 2.4095 | - |
| 0.1725 | 54 | 2.0411 | - |
| 0.1757 | 55 | 2.4364 | - |
| 0.1789 | 56 | 3.1587 | - |
| 0.1821 | 57 | 2.8662 | - |
| 0.1853 | 58 | 2.8759 | - |
| 0.1885 | 59 | 2.8717 | - |
| 0.1917 | 60 | 3.515 | - |
| 0.1949 | 61 | 3.192 | - |
| 0.1981 | 62 | 2.253 | - |
| 0.2013 | 63 | 2.8449 | - |
| 0.2045 | 64 | 2.0755 | - |
| 0.2077 | 65 | 1.9475 | - |
| 0.2109 | 66 | 1.8015 | - |
| 0.2141 | 67 | 2.4801 | - |
| 0.2173 | 68 | 3.0986 | - |
| 0.2204 | 69 | 2.8571 | - |
| 0.2236 | 70 | 2.8611 | - |
| 0.2268 | 71 | 2.0581 | - |
| 0.2300 | 72 | 2.7042 | - |
| 0.2332 | 73 | 2.2055 | - |
| 0.2364 | 74 | 2.3948 | - |
| 0.2396 | 75 | 2.1092 | - |
| 0.2428 | 76 | 3.2277 | - |
| 0.2460 | 77 | 2.0378 | - |
| 0.2492 | 78 | 2.1426 | - |
| 0.2524 | 79 | 2.6016 | - |
| 0.2556 | 80 | 2.8198 | - |
| 0.2588 | 81 | 2.3303 | - |
| 0.2620 | 82 | 2.4117 | - |
| 0.2652 | 83 | 2.8172 | - |
| 0.2684 | 84 | 2.2824 | - |
| 0.2716 | 85 | 2.3764 | - |
| 0.2748 | 86 | 1.7689 | - |
| 0.2780 | 87 | 1.8861 | - |
| 0.2812 | 88 | 2.0835 | - |
| 0.2843 | 89 | 2.3946 | - |
| 0.2875 | 90 | 2.4478 | - |
| 0.2907 | 91 | 2.0612 | - |
| 0.2939 | 92 | 1.6599 | - |
| 0.2971 | 93 | 2.6267 | - |
| 0.3003 | 94 | 1.9966 | - |
| 0.3035 | 95 | 2.3953 | - |
| 0.3067 | 96 | 2.4832 | - |
| 0.3099 | 97 | 1.7252 | - |
| 0.3131 | 98 | 2.1252 | - |
| 0.3163 | 99 | 2.4232 | - |
| 0.3195 | 100 | 1.8645 | 0.663 |
| 0.3227 | 101 | 2.3749 | - |
| 0.3259 | 102 | 2.1641 | - |
| 0.3291 | 103 | 1.6162 | - |
| 0.3323 | 104 | 2.118 | - |
| 0.3355 | 105 | 2.2934 | - |
| 0.3387 | 106 | 2.6288 | - |
| 0.3419 | 107 | 2.6996 | - |
| 0.3450 | 108 | 1.6489 | - |
| 0.3482 | 109 | 2.4605 | - |
| 0.3514 | 110 | 1.9531 | - |
| 0.3546 | 111 | 1.8193 | - |
| 0.3578 | 112 | 1.9936 | - |
| 0.3610 | 113 | 2.26 | - |
| 0.3642 | 114 | 1.9986 | - |
| 0.3674 | 115 | 2.4304 | - |
| 0.3706 | 116 | 2.1585 | - |
| 0.3738 | 117 | 1.8003 | - |
| 0.3770 | 118 | 2.4486 | - |
| 0.3802 | 119 | 2.637 | - |
| 0.3834 | 120 | 2.1322 | - |
| 0.3866 | 121 | 2.0404 | - |
| 0.3898 | 122 | 2.0502 | - |
| 0.3930 | 123 | 2.0422 | - |
| 0.3962 | 124 | 2.236 | - |
| 0.3994 | 125 | 2.3226 | - |
| 0.4026 | 126 | 2.469 | - |
| 0.4058 | 127 | 1.8761 | - |
| 0.4089 | 128 | 2.3535 | - |
| 0.4121 | 129 | 1.5602 | - |
| 0.4153 | 130 | 1.266 | - |
| 0.4185 | 131 | 2.3524 | - |
| 0.4217 | 132 | 1.7668 | - |
| 0.4249 | 133 | 1.8161 | - |
| 0.4281 | 134 | 2.1238 | - |
| 0.4313 | 135 | 2.0247 | - |
| 0.4345 | 136 | 2.2131 | - |
| 0.4377 | 137 | 1.9424 | - |
| 0.4409 | 138 | 1.8134 | - |
| 0.4441 | 139 | 2.2077 | - |
| 0.4473 | 140 | 1.17 | - |
| 0.4505 | 141 | 2.2172 | - |
| 0.4537 | 142 | 1.903 | - |
| 0.4569 | 143 | 1.9001 | - |
| 0.4601 | 144 | 1.7742 | - |
| 0.4633 | 145 | 1.7324 | - |
| 0.4665 | 146 | 2.2174 | - |
| 0.4696 | 147 | 2.1008 | - |
| 0.4728 | 148 | 1.6292 | - |
| 0.4760 | 149 | 1.4405 | - |
| 0.4792 | 150 | 1.7845 | - |
| 0.4824 | 151 | 1.8363 | - |
| 0.4856 | 152 | 1.8181 | - |
| 0.4888 | 153 | 1.6015 | - |
| 0.4920 | 154 | 2.0204 | - |
| 0.4952 | 155 | 1.4804 | - |
| 0.4984 | 156 | 1.4607 | - |
| 0.5016 | 157 | 1.8526 | - |
| 0.5048 | 158 | 1.731 | - |
| 0.5080 | 159 | 1.1399 | - |
| 0.5112 | 160 | 1.8764 | - |
| 0.5144 | 161 | 1.7151 | - |
| 0.5176 | 162 | 2.4042 | - |
| 0.5208 | 163 | 2.1513 | - |
| 0.5240 | 164 | 1.31 | - |
| 0.5272 | 165 | 1.8768 | - |
| 0.5304 | 166 | 1.8048 | - |
| 0.5335 | 167 | 1.6037 | - |
| 0.5367 | 168 | 2.3568 | - |
| 0.5399 | 169 | 1.8979 | - |
| 0.5431 | 170 | 1.4007 | - |
| 0.5463 | 171 | 1.466 | - |
| 0.5495 | 172 | 1.8892 | - |
| 0.5527 | 173 | 2.1865 | - |
| 0.5559 | 174 | 1.6588 | - |
| 0.5591 | 175 | 1.3176 | - |
| 0.5623 | 176 | 1.9557 | - |
| 0.5655 | 177 | 1.6885 | - |
| 0.5687 | 178 | 2.0255 | - |
| 0.5719 | 179 | 1.7787 | - |
| 0.5751 | 180 | 1.9642 | - |
| 0.5783 | 181 | 1.8975 | - |
| 0.5815 | 182 | 1.7 | - |
| 0.5847 | 183 | 1.3562 | - |
| 0.5879 | 184 | 1.78 | - |
| 0.5911 | 185 | 1.6219 | - |
| 0.5942 | 186 | 2.3187 | - |
| 0.5974 | 187 | 1.4364 | - |
| 0.6006 | 188 | 1.4302 | - |
| 0.6038 | 189 | 1.9611 | - |
| 0.6070 | 190 | 1.299 | - |
| 0.6102 | 191 | 1.5023 | - |
| 0.6134 | 192 | 1.6221 | - |
| 0.6166 | 193 | 1.8834 | - |
| 0.6198 | 194 | 1.9183 | - |
| 0.6230 | 195 | 1.666 | - |
| 0.6262 | 196 | 1.2618 | - |
| 0.6294 | 197 | 2.1579 | - |
| 0.6326 | 198 | 2.0658 | - |
| 0.6358 | 199 | 1.7967 | - |
| 0.6390 | 200 | 2.0332 | 0.706 |
| 0.6422 | 201 | 1.8525 | - |
| 0.6454 | 202 | 1.914 | - |
| 0.6486 | 203 | 2.1121 | - |
| 0.6518 | 204 | 1.6235 | - |
| 0.6550 | 205 | 2.165 | - |
| 0.6581 | 206 | 2.1271 | - |
| 0.6613 | 207 | 2.6429 | - |
| 0.6645 | 208 | 2.1433 | - |
| 0.6677 | 209 | 1.6523 | - |
| 0.6709 | 210 | 1.3249 | - |
| 0.6741 | 211 | 1.6668 | - |
| 0.6773 | 212 | 1.7824 | - |
| 0.6805 | 213 | 2.276 | - |
| 0.6837 | 214 | 1.0015 | - |
| 0.6869 | 215 | 1.549 | - |
| 0.6901 | 216 | 1.9478 | - |
| 0.6933 | 217 | 2.0875 | - |
| 0.6965 | 218 | 1.6177 | - |
| 0.6997 | 219 | 1.9426 | - |
| 0.7029 | 220 | 1.5141 | - |
| 0.7061 | 221 | 2.3293 | - |
| 0.7093 | 222 | 1.7937 | - |
| 0.7125 | 223 | 1.7204 | - |
| 0.7157 | 224 | 1.9365 | - |
| 0.7188 | 225 | 1.0698 | - |
| 0.7220 | 226 | 1.7878 | - |
| 0.7252 | 227 | 1.5305 | - |
| 0.7284 | 228 | 1.7989 | - |
| 0.7316 | 229 | 1.7433 | - |
| 0.7348 | 230 | 2.2788 | - |
| 0.7380 | 231 | 1.6643 | - |
| 0.7412 | 232 | 1.2865 | - |
| 0.7444 | 233 | 1.6712 | - |
| 0.7476 | 234 | 2.0329 | - |
| 0.7508 | 235 | 2.3507 | - |
| 0.7540 | 236 | 2.0751 | - |
| 0.7572 | 237 | 1.3945 | - |
| 0.7604 | 238 | 1.7766 | - |
| 0.7636 | 239 | 2.0564 | - |
| 0.7668 | 240 | 1.8347 | - |
| 0.7700 | 241 | 1.676 | - |
| 0.7732 | 242 | 1.5643 | - |
| 0.7764 | 243 | 1.9716 | - |
| 0.7796 | 244 | 1.8792 | - |
| 0.7827 | 245 | 1.8918 | - |
| 0.7859 | 246 | 1.8682 | - |
| 0.7891 | 247 | 1.7447 | - |
| 0.7923 | 248 | 1.4158 | - |
| 0.7955 | 249 | 1.6805 | - |
| 0.7987 | 250 | 1.4564 | - |
| 0.8019 | 251 | 2.2649 | - |
| 0.8051 | 252 | 1.6834 | - |
| 0.8083 | 253 | 1.4704 | - |
| 0.8115 | 254 | 1.9097 | - |
| 0.8147 | 255 | 1.6388 | - |
| 0.8179 | 256 | 1.4111 | - |
| 0.8211 | 257 | 1.3129 | - |
| 0.8243 | 258 | 2.0162 | - |
| 0.8275 | 259 | 1.9092 | - |
| 0.8307 | 260 | 1.5773 | - |
| 0.8339 | 261 | 1.791 | - |
| 0.8371 | 262 | 2.0667 | - |
| 0.8403 | 263 | 2.1272 | - |
| 0.8435 | 264 | 1.6405 | - |
| 0.8466 | 265 | 1.5684 | - |
| 0.8498 | 266 | 1.6187 | - |
| 0.8530 | 267 | 1.6393 | - |
| 0.8562 | 268 | 1.6839 | - |
| 0.8594 | 269 | 1.3771 | - |
| 0.8626 | 270 | 1.888 | - |
| 0.8658 | 271 | 1.9585 | - |
| 0.8690 | 272 | 1.7324 | - |
| 0.8722 | 273 | 2.2049 | - |
| 0.8754 | 274 | 1.8384 | - |
| 0.8786 | 275 | 1.2521 | - |
| 0.8818 | 276 | 1.6959 | - |
| 0.8850 | 277 | 1.949 | - |
| 0.8882 | 278 | 1.5847 | - |
| 0.8914 | 279 | 1.3838 | - |
| 0.8946 | 280 | 1.467 | - |
| 0.8978 | 281 | 1.6706 | - |
| 0.9010 | 282 | 1.7329 | - |
| 0.9042 | 283 | 1.8004 | - |
| 0.9073 | 284 | 1.518 | - |
| 0.9105 | 285 | 1.659 | - |
| 0.9137 | 286 | 1.5457 | - |
| 0.9169 | 287 | 1.5093 | - |
| 0.9201 | 288 | 1.6979 | - |
| 0.9233 | 289 | 2.0024 | - |
| 0.9265 | 290 | 2.3541 | - |
| 0.9297 | 291 | 1.6111 | - |
| 0.9329 | 292 | 1.6866 | - |
| 0.9361 | 293 | 1.5074 | - |
| 0.9393 | 294 | 1.9874 | - |
| 0.9425 | 295 | 1.9216 | - |
| 0.9457 | 296 | 1.6023 | - |
| 0.9489 | 297 | 2.344 | - |
| 0.9521 | 298 | 2.3418 | - |
| 0.9553 | 299 | 1.4993 | - |
| 0.9585 | 300 | 1.4566 | 0.708 |
| 0.9617 | 301 | 1.5179 | - |
| 0.9649 | 302 | 1.5219 | - |
| 0.9681 | 303 | 1.8588 | - |
| 0.9712 | 304 | 1.5196 | - |
| 0.9744 | 305 | 2.124 | - |
| 0.9776 | 306 | 1.6914 | - |
| 0.9808 | 307 | 1.8972 | - |
| 0.9840 | 308 | 1.5841 | - |
| 0.9872 | 309 | 1.9003 | - |
| 0.9904 | 310 | 1.8052 | - |
| 0.9936 | 311 | 1.7956 | - |
| 0.9968 | 312 | 1.7592 | - |
| 1.0 | 313 | 2.0598 | 0.71 |