---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
- generated_from_trainer
- dataset_size:183668
- loss:OnlineContrastiveLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: VATICAN [Vatican City] Biblioteca Apostolica Vaticana P. 16939
sentences:
- Canberra [Australia] (National Library Australia), MSS 58843
- PRAGUE [Czech Republic] Národní knihovna Cod. hist. 92003
- Biblioteca Nazionale Centrale (ROME) fr. 69773
- source_sentence: NLMex, ms. Voss. lat. 1773I
sentences:
- Trin. Coll., nouv. acq. 8333
- VATICAN [Vatican City] Biblioteca Vaticana Harley 29185
- Mexico City, BNE-Res, ms. Voss lat 1773I
- source_sentence: Brussels, Royal Library of Belgium, Res 68570
sentences:
- GENEVE [Switzerland] Bibliothèque de Genève A. 77850
- BNCR grec 70511
- KB-BE Res 73290
- source_sentence: BERLIN [Germany] Staatsbibliothek P. 34213
sentences:
- TURIN [Italy] Biblioteca Reale nouv. acq. fr. 37700
- BERLIN [Germany] Staatsbibliothek, Département des manuscrits, P. 34213
- Bibliothèque nationale de France (BnF) (PARIS) P. 42109
- source_sentence: PRAGUE [Czech Republic] Národní knihovna Cod. hist. 17036
sentences:
- Národní knihovna, Cod. hist. 17036 (PRAGUE)
- FLORENCE [Italy] Biblioteca Medicea Laurenziana, Département des manuscrits, Cod.
7698
- Bayerische Staatsbibliothek (Munich, Germany), heb. 26574
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: eval
type: eval
metrics:
- type: cosine_accuracy
value: 0.9935857645354852
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.858028769493103
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9934419293420774
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8504674434661865
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9928118393234673
name: Cosine Precision
- type: cosine_recall
value: 0.9940728196443692
name: Cosine Recall
- type: cosine_ap
value: 0.9994210019936423
name: Cosine Ap
- type: cosine_mcc
value: 0.9871661215441352
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: test
type: test
metrics:
- type: cosine_accuracy
value: 0.991932147290029
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.8689395785331726
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9918631337366992
name: Cosine F1
- type: cosine_f1_threshold
value: 0.8689395785331726
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9945606694560669
name: Cosine Precision
- type: cosine_recall
value: 0.9891801914273824
name: Cosine Recall
- type: cosine_ap
value: 0.999422398762738
name: Cosine Ap
- type: cosine_mcc
value: 0.9838774836901869
name: Cosine Mcc
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). 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
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 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': 256, 'do_lower_case': False, 'architecture': '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})
(2): Normalize()
)
```
## 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("Albertdebeauvais/all-MiniLM-L6-v2_cotes")
# Run inference
sentences = [
'PRAGUE [Czech Republic] Národní knihovna Cod. hist. 17036',
'Národní knihovna, Cod. hist. 17036 (PRAGUE)',
'FLORENCE [Italy] Biblioteca Medicea Laurenziana, Département des manuscrits, Cod. 7698',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0000, 0.9921, 0.2995],
# [0.9921, 1.0000, 0.3039],
# [0.2995, 0.3039, 1.0000]])
```
## Evaluation
### Metrics
#### Binary Classification
* Datasets: `eval` and `test`
* Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | eval | test |
|:--------------------------|:-----------|:-----------|
| cosine_accuracy | 0.9936 | 0.9919 |
| cosine_accuracy_threshold | 0.858 | 0.8689 |
| cosine_f1 | 0.9934 | 0.9919 |
| cosine_f1_threshold | 0.8505 | 0.8689 |
| cosine_precision | 0.9928 | 0.9946 |
| cosine_recall | 0.9941 | 0.9892 |
| **cosine_ap** | **0.9994** | **0.9994** |
| cosine_mcc | 0.9872 | 0.9839 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 183,668 training samples
* Columns: text1, text2, and label
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 6 tokens
- mean: 16.43 tokens
- max: 34 tokens
| - min: 7 tokens
- mean: 16.67 tokens
- max: 39 tokens
| |
* Samples:
| text1 | text2 | label |
|:-------------------------------------------------------------------------------|:-------------------------------------------------------------------|:---------------|
| Vatican. Biblioteca apostolica vaticana, Vat.lat.1664 | Vatican. Biblioteca apostolica vaticana, Vat.lat.1664 | 1 |
| Royal Library of Belgium (Brussels, Belgium), Voss. lat. 69542-73 | STOCKHOLM [Sweden] Kungliga biblioteket Cod. 69542-73 | 0 |
| KB, ms. Ott. 34088 | Staatsbibliothek zu Berlin, ms. nouv. acq. 34088 | 0 |
* Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 4,833 evaluation samples
* Columns: text1, text2, and label
* Approximate statistics based on the first 1000 samples:
| | text1 | text2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | - min: 7 tokens
- mean: 16.21 tokens
- max: 39 tokens
| - min: 6 tokens
- mean: 16.65 tokens
- max: 41 tokens
| |
* Samples:
| text1 | text2 | label |
|:------------------------------------------------------------|:---------------------------------------------------------------------------|:---------------|
| NYPL, ms. EGERTON 44378 | New York Public Library, Egerton 44378 | 1 |
| BRUSSELS [Belgium] KBR lat. 39761 | BRUSSELS [Belgium] Bibliothèque royale de Belgique lat. 39761 | 1 |
| Stockholm, Royal Library Sweden, lat. 21045-64 | Royal Library of Sweden, lat. 21045-64 | 1 |
* Loss: [OnlineContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 160
- `per_device_eval_batch_size`: 160
- `learning_rate`: 3e-05
- `warmup_ratio`: 0.03
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 160
- `per_device_eval_batch_size`: 160
- `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`: 3e-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`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.03
- `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`: 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_fused
- `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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
Click to expand
| Epoch | Step | Training Loss | Validation Loss | eval_cosine_ap | test_cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:--------------:|:--------------:|
| -1 | -1 | - | - | 0.7380 | - |
| 0.0052 | 6 | 11.0534 | - | - | - |
| 0.0105 | 12 | 11.3792 | - | - | - |
| 0.0157 | 18 | 8.799 | - | - | - |
| 0.0209 | 24 | 7.7743 | - | - | - |
| 0.0261 | 30 | 5.8982 | - | - | - |
| 0.0314 | 36 | 4.9405 | - | - | - |
| 0.0366 | 42 | 3.2519 | - | - | - |
| 0.0418 | 48 | 2.195 | - | - | - |
| 0.0470 | 54 | 2.9317 | - | - | - |
| 0.0523 | 60 | 1.6287 | - | - | - |
| 0.0575 | 66 | 1.39 | - | - | - |
| 0.0627 | 72 | 1.6894 | - | - | - |
| 0.0679 | 78 | 1.7984 | - | - | - |
| 0.0732 | 84 | 1.4432 | - | - | - |
| 0.0784 | 90 | 1.4062 | - | - | - |
| 0.0836 | 96 | 1.5969 | - | - | - |
| 0.0889 | 102 | 1.3597 | - | - | - |
| 0.0941 | 108 | 1.1465 | - | - | - |
| 0.0993 | 114 | 1.1614 | - | - | - |
| 0.1045 | 120 | 1.116 | - | - | - |
| 0.1098 | 126 | 1.1741 | - | - | - |
| 0.1150 | 132 | 0.9491 | - | - | - |
| 0.1202 | 138 | 0.7978 | - | - | - |
| 0.1254 | 144 | 0.9691 | - | - | - |
| 0.1307 | 150 | 0.8838 | - | - | - |
| 0.1359 | 156 | 0.9894 | - | - | - |
| 0.1411 | 162 | 1.0388 | - | - | - |
| 0.1463 | 168 | 0.9774 | - | - | - |
| 0.1516 | 174 | 0.8315 | - | - | - |
| 0.1568 | 180 | 0.747 | - | - | - |
| 0.1620 | 186 | 0.7621 | - | - | - |
| 0.1672 | 192 | 0.8285 | - | - | - |
| 0.1725 | 198 | 0.8893 | - | - | - |
| 0.1777 | 204 | 0.8845 | - | - | - |
| 0.1829 | 210 | 0.7866 | - | - | - |
| 0.1882 | 216 | 0.8586 | - | - | - |
| 0.1934 | 222 | 0.8521 | - | - | - |
| 0.1986 | 228 | 0.9021 | - | - | - |
| 0.2038 | 234 | 0.5791 | - | - | - |
| 0.2091 | 240 | 0.5312 | - | - | - |
| 0.2143 | 246 | 0.6911 | - | - | - |
| 0.2195 | 252 | 0.543 | - | - | - |
| 0.2247 | 258 | 0.7933 | - | - | - |
| 0.2300 | 264 | 0.6489 | - | - | - |
| 0.2352 | 270 | 0.6369 | - | - | - |
| 0.2404 | 276 | 0.6113 | - | - | - |
| 0.2456 | 282 | 0.647 | - | - | - |
| 0.2509 | 288 | 0.6253 | - | - | - |
| 0.2561 | 294 | 0.7232 | - | - | - |
| 0.2613 | 300 | 0.5919 | - | - | - |
| 0.2666 | 306 | 0.5326 | - | - | - |
| 0.2718 | 312 | 0.7215 | - | - | - |
| 0.2770 | 318 | 0.7516 | - | - | - |
| 0.2822 | 324 | 0.5459 | - | - | - |
| 0.2875 | 330 | 0.3956 | - | - | - |
| 0.2927 | 336 | 0.6712 | - | - | - |
| 0.2979 | 342 | 1.1014 | - | - | - |
| 0.3031 | 348 | 0.7373 | - | - | - |
| 0.3084 | 354 | 0.6435 | - | - | - |
| 0.3136 | 360 | 0.726 | - | - | - |
| 0.3188 | 366 | 0.6514 | - | - | - |
| 0.3240 | 372 | 0.9203 | - | - | - |
| 0.3293 | 378 | 0.4015 | - | - | - |
| 0.3345 | 384 | 0.4945 | - | - | - |
| 0.3397 | 390 | 0.5672 | - | - | - |
| 0.3449 | 396 | 0.5229 | - | - | - |
| 0.3502 | 402 | 0.6377 | - | - | - |
| 0.3554 | 408 | 0.7667 | - | - | - |
| 0.3606 | 414 | 0.8402 | - | - | - |
| 0.3659 | 420 | 0.5398 | - | - | - |
| 0.3711 | 426 | 1.017 | - | - | - |
| 0.3763 | 432 | 0.6365 | - | - | - |
| 0.3815 | 438 | 0.2821 | - | - | - |
| 0.3868 | 444 | 0.7511 | - | - | - |
| 0.3920 | 450 | 0.5463 | - | - | - |
| 0.3972 | 456 | 0.4772 | - | - | - |
| 0.4024 | 462 | 0.6965 | - | - | - |
| 0.4077 | 468 | 0.646 | - | - | - |
| 0.4129 | 474 | 0.4739 | - | - | - |
| 0.4181 | 480 | 0.3673 | - | - | - |
| 0.4233 | 486 | 0.5785 | - | - | - |
| 0.4286 | 492 | 0.227 | - | - | - |
| 0.4338 | 498 | 0.7576 | - | - | - |
| 0.4390 | 504 | 0.8942 | - | - | - |
| 0.4443 | 510 | 0.4486 | - | - | - |
| 0.4495 | 516 | 0.3542 | - | - | - |
| 0.4547 | 522 | 0.4259 | - | - | - |
| 0.4599 | 528 | 0.5376 | - | - | - |
| 0.4652 | 534 | 0.7009 | - | - | - |
| 0.4704 | 540 | 0.5248 | - | - | - |
| 0.4756 | 546 | 0.577 | - | - | - |
| 0.4808 | 552 | 0.3948 | - | - | - |
| 0.4861 | 558 | 0.4447 | - | - | - |
| 0.4913 | 564 | 0.7539 | - | - | - |
| 0.4965 | 570 | 0.2763 | - | - | - |
| 0.5017 | 576 | 0.3015 | - | - | - |
| 0.5070 | 582 | 0.4449 | - | - | - |
| 0.5122 | 588 | 0.6351 | - | - | - |
| 0.5174 | 594 | 0.5846 | - | - | - |
| 0.5226 | 600 | 0.576 | - | - | - |
| 0.5279 | 606 | 0.3576 | - | - | - |
| 0.5331 | 612 | 0.2414 | - | - | - |
| 0.5383 | 618 | 0.5904 | - | - | - |
| 0.5436 | 624 | 0.5306 | - | - | - |
| 0.5488 | 630 | 0.474 | - | - | - |
| 0.5540 | 636 | 0.6083 | - | - | - |
| 0.5592 | 642 | 0.6796 | - | - | - |
| 0.5645 | 648 | 0.5498 | - | - | - |
| 0.5697 | 654 | 0.6603 | - | - | - |
| 0.5749 | 660 | 0.3717 | - | - | - |
| 0.5801 | 666 | 0.8338 | - | - | - |
| 0.5854 | 672 | 0.4483 | - | - | - |
| 0.5906 | 678 | 0.3904 | - | - | - |
| 0.5958 | 684 | 0.3456 | - | - | - |
| 0.6010 | 690 | 0.3724 | - | - | - |
| 0.6063 | 696 | 0.8648 | - | - | - |
| 0.6115 | 702 | 0.6776 | - | - | - |
| 0.6167 | 708 | 0.3602 | - | - | - |
| 0.6220 | 714 | 0.405 | - | - | - |
| 0.6272 | 720 | 0.5042 | - | - | - |
| 0.6324 | 726 | 0.458 | - | - | - |
| 0.6376 | 732 | 0.3791 | - | - | - |
| 0.6429 | 738 | 0.4746 | - | - | - |
| 0.6481 | 744 | 0.6014 | - | - | - |
| 0.6533 | 750 | 0.4847 | - | - | - |
| 0.6585 | 756 | 0.3132 | - | - | - |
| 0.6638 | 762 | 0.4689 | - | - | - |
| 0.6690 | 768 | 0.3886 | - | - | - |
| 0.6742 | 774 | 0.6002 | - | - | - |
| 0.6794 | 780 | 0.2 | - | - | - |
| 0.6847 | 786 | 0.4584 | - | - | - |
| 0.6899 | 792 | 0.5236 | - | - | - |
| 0.6951 | 798 | 0.2428 | - | - | - |
| 0.7003 | 804 | 0.7477 | - | - | - |
| 0.7056 | 810 | 0.5688 | - | - | - |
| 0.7108 | 816 | 0.2678 | - | - | - |
| 0.7160 | 822 | 0.2852 | - | - | - |
| 0.7213 | 828 | 0.3194 | - | - | - |
| 0.7265 | 834 | 0.6157 | - | - | - |
| 0.7317 | 840 | 0.2916 | - | - | - |
| 0.7369 | 846 | 0.5354 | - | - | - |
| 0.7422 | 852 | 0.5441 | - | - | - |
| 0.7474 | 858 | 0.3386 | - | - | - |
| 0.7526 | 864 | 0.2868 | - | - | - |
| 0.7578 | 870 | 0.8884 | - | - | - |
| 0.7631 | 876 | 0.1933 | - | - | - |
| 0.7683 | 882 | 0.5702 | - | - | - |
| 0.7735 | 888 | 0.4724 | - | - | - |
| 0.7787 | 894 | 0.4239 | - | - | - |
| 0.7840 | 900 | 0.5211 | - | - | - |
| 0.7892 | 906 | 0.5454 | - | - | - |
| 0.7944 | 912 | 0.3982 | - | - | - |
| 0.7997 | 918 | 0.6393 | - | - | - |
| 0.8049 | 924 | 0.2553 | - | - | - |
| 0.8101 | 930 | 0.2186 | - | - | - |
| 0.8153 | 936 | 0.3652 | - | - | - |
| 0.8206 | 942 | 0.4922 | - | - | - |
| 0.8258 | 948 | 0.4043 | - | - | - |
| 0.8310 | 954 | 0.5297 | - | - | - |
| 0.8362 | 960 | 0.4649 | - | - | - |
| 0.8415 | 966 | 0.4515 | - | - | - |
| 0.8467 | 972 | 0.3466 | - | - | - |
| 0.8519 | 978 | 0.4999 | - | - | - |
| 0.8571 | 984 | 0.4356 | - | - | - |
| 0.8624 | 990 | 0.4066 | - | - | - |
| 0.8676 | 996 | 0.1665 | - | - | - |
| 0.8728 | 1002 | 0.4078 | - | - | - |
| 0.8780 | 1008 | 0.2811 | - | - | - |
| 0.8833 | 1014 | 0.351 | - | - | - |
| 0.8885 | 1020 | 0.2498 | - | - | - |
| 0.8937 | 1026 | 0.3684 | - | - | - |
| 0.8990 | 1032 | 0.3429 | - | - | - |
| 0.9042 | 1038 | 0.3797 | - | - | - |
| 0.9094 | 1044 | 0.3756 | - | - | - |
| 0.9146 | 1050 | 0.1628 | - | - | - |
| 0.9199 | 1056 | 0.6396 | - | - | - |
| 0.9251 | 1062 | 0.4486 | - | - | - |
| 0.9303 | 1068 | 0.4347 | - | - | - |
| 0.9355 | 1074 | 0.3849 | - | - | - |
| 0.9408 | 1080 | 0.6004 | - | - | - |
| 0.9460 | 1086 | 0.5233 | - | - | - |
| 0.9512 | 1092 | 0.3776 | - | - | - |
| 0.9564 | 1098 | 0.5516 | - | - | - |
| 0.9617 | 1104 | 0.3355 | - | - | - |
| 0.9669 | 1110 | 0.3031 | - | - | - |
| 0.9721 | 1116 | 0.409 | - | - | - |
| 0.9774 | 1122 | 0.5007 | - | - | - |
| 0.9826 | 1128 | 0.4215 | - | - | - |
| 0.9878 | 1134 | 0.4394 | - | - | - |
| 0.9930 | 1140 | 0.273 | - | - | - |
| 0.9983 | 1146 | 0.365 | - | - | - |
| 1.0 | 1148 | - | 0.2877 | 0.9987 | - |
| 1.0035 | 1152 | 0.3812 | - | - | - |
| 1.0087 | 1158 | 0.2444 | - | - | - |
| 1.0139 | 1164 | 0.3097 | - | - | - |
| 1.0192 | 1170 | 0.4659 | - | - | - |
| 1.0244 | 1176 | 0.1669 | - | - | - |
| 1.0296 | 1182 | 0.1825 | - | - | - |
| 1.0348 | 1188 | 0.4532 | - | - | - |
| 1.0401 | 1194 | 0.5368 | - | - | - |
| 1.0453 | 1200 | 0.2084 | - | - | - |
| 1.0505 | 1206 | 0.1953 | - | - | - |
| 1.0557 | 1212 | 0.2187 | - | - | - |
| 1.0610 | 1218 | 0.3887 | - | - | - |
| 1.0662 | 1224 | 0.366 | - | - | - |
| 1.0714 | 1230 | 0.1054 | - | - | - |
| 1.0767 | 1236 | 0.5161 | - | - | - |
| 1.0819 | 1242 | 0.2891 | - | - | - |
| 1.0871 | 1248 | 0.2111 | - | - | - |
| 1.0923 | 1254 | 0.2921 | - | - | - |
| 1.0976 | 1260 | 0.2873 | - | - | - |
| 1.1028 | 1266 | 0.3253 | - | - | - |
| 1.1080 | 1272 | 0.3026 | - | - | - |
| 1.1132 | 1278 | 0.0585 | - | - | - |
| 1.1185 | 1284 | 0.2984 | - | - | - |
| 1.1237 | 1290 | 0.1654 | - | - | - |
| 1.1289 | 1296 | 0.6225 | - | - | - |
| 1.1341 | 1302 | 0.3749 | - | - | - |
| 1.1394 | 1308 | 0.3727 | - | - | - |
| 1.1446 | 1314 | 0.2266 | - | - | - |
| 1.1498 | 1320 | 0.2619 | - | - | - |
| 1.1551 | 1326 | 0.2534 | - | - | - |
| 1.1603 | 1332 | 0.3271 | - | - | - |
| 1.1655 | 1338 | 0.1328 | - | - | - |
| 1.1707 | 1344 | 0.4029 | - | - | - |
| 1.1760 | 1350 | 0.0869 | - | - | - |
| 1.1812 | 1356 | 0.6434 | - | - | - |
| 1.1864 | 1362 | 0.2033 | - | - | - |
| 1.1916 | 1368 | 0.2266 | - | - | - |
| 1.1969 | 1374 | 0.2547 | - | - | - |
| 1.2021 | 1380 | 0.2199 | - | - | - |
| 1.2073 | 1386 | 0.3716 | - | - | - |
| 1.2125 | 1392 | 0.4646 | - | - | - |
| 1.2178 | 1398 | 0.2163 | - | - | - |
| 1.2230 | 1404 | 0.1998 | - | - | - |
| 1.2282 | 1410 | 0.41 | - | - | - |
| 1.2334 | 1416 | 0.2859 | - | - | - |
| 1.2387 | 1422 | 0.2039 | - | - | - |
| 1.2439 | 1428 | 0.4095 | - | - | - |
| 1.2491 | 1434 | 0.0924 | - | - | - |
| 1.2544 | 1440 | 0.3192 | - | - | - |
| 1.2596 | 1446 | 0.4833 | - | - | - |
| 1.2648 | 1452 | 0.4927 | - | - | - |
| 1.2700 | 1458 | 0.2107 | - | - | - |
| 1.2753 | 1464 | 0.1869 | - | - | - |
| 1.2805 | 1470 | 0.188 | - | - | - |
| 1.2857 | 1476 | 0.0841 | - | - | - |
| 1.2909 | 1482 | 0.4332 | - | - | - |
| 1.2962 | 1488 | 0.3164 | - | - | - |
| 1.3014 | 1494 | 0.1526 | - | - | - |
| 1.3066 | 1500 | 0.0848 | - | - | - |
| 1.3118 | 1506 | 0.1896 | - | - | - |
| 1.3171 | 1512 | 0.2525 | - | - | - |
| 1.3223 | 1518 | 0.1776 | - | - | - |
| 1.3275 | 1524 | 0.2098 | - | - | - |
| 1.3328 | 1530 | 0.2494 | - | - | - |
| 1.3380 | 1536 | 0.1643 | - | - | - |
| 1.3432 | 1542 | 0.2588 | - | - | - |
| 1.3484 | 1548 | 0.2429 | - | - | - |
| 1.3537 | 1554 | 0.2195 | - | - | - |
| 1.3589 | 1560 | 0.138 | - | - | - |
| 1.3641 | 1566 | 0.285 | - | - | - |
| 1.3693 | 1572 | 0.7177 | - | - | - |
| 1.3746 | 1578 | 0.5872 | - | - | - |
| 1.3798 | 1584 | 0.1981 | - | - | - |
| 1.3850 | 1590 | 0.34 | - | - | - |
| 1.3902 | 1596 | 0.3694 | - | - | - |
| 1.3955 | 1602 | 0.0795 | - | - | - |
| 1.4007 | 1608 | 0.4017 | - | - | - |
| 1.4059 | 1614 | 0.364 | - | - | - |
| 1.4111 | 1620 | 0.2462 | - | - | - |
| 1.4164 | 1626 | 0.0681 | - | - | - |
| 1.4216 | 1632 | 0.2719 | - | - | - |
| 1.4268 | 1638 | 0.1616 | - | - | - |
| 1.4321 | 1644 | 0.3847 | - | - | - |
| 1.4373 | 1650 | 0.3032 | - | - | - |
| 1.4425 | 1656 | 0.2087 | - | - | - |
| 1.4477 | 1662 | 0.2143 | - | - | - |
| 1.4530 | 1668 | 0.2841 | - | - | - |
| 1.4582 | 1674 | 0.0644 | - | - | - |
| 1.4634 | 1680 | 0.1469 | - | - | - |
| 1.4686 | 1686 | 0.1478 | - | - | - |
| 1.4739 | 1692 | 0.1094 | - | - | - |
| 1.4791 | 1698 | 0.1843 | - | - | - |
| 1.4843 | 1704 | 0.4574 | - | - | - |
| 1.4895 | 1710 | 0.4318 | - | - | - |
| 1.4948 | 1716 | 0.1449 | - | - | - |
| 1.5 | 1722 | 0.2713 | - | - | - |
| 1.5052 | 1728 | 0.3274 | - | - | - |
| 1.5105 | 1734 | 0.2755 | - | - | - |
| 1.5157 | 1740 | 0.3226 | - | - | - |
| 1.5209 | 1746 | 0.2007 | - | - | - |
| 1.5261 | 1752 | 0.4237 | - | - | - |
| 1.5314 | 1758 | 0.2821 | - | - | - |
| 1.5366 | 1764 | 0.4661 | - | - | - |
| 1.5418 | 1770 | 0.2417 | - | - | - |
| 1.5470 | 1776 | 0.1035 | - | - | - |
| 1.5523 | 1782 | 0.238 | - | - | - |
| 1.5575 | 1788 | 0.1845 | - | - | - |
| 1.5627 | 1794 | 0.1721 | - | - | - |
| 1.5679 | 1800 | 0.04 | - | - | - |
| 1.5732 | 1806 | 0.0548 | - | - | - |
| 1.5784 | 1812 | 0.427 | - | - | - |
| 1.5836 | 1818 | 0.0614 | - | - | - |
| 1.5889 | 1824 | 0.1521 | - | - | - |
| 1.5941 | 1830 | 0.3391 | - | - | - |
| 1.5993 | 1836 | 0.2717 | - | - | - |
| 1.6045 | 1842 | 0.2131 | - | - | - |
| 1.6098 | 1848 | 0.3416 | - | - | - |
| 1.6150 | 1854 | 0.218 | - | - | - |
| 1.6202 | 1860 | 0.148 | - | - | - |
| 1.6254 | 1866 | 0.3309 | - | - | - |
| 1.6307 | 1872 | 0.0677 | - | - | - |
| 1.6359 | 1878 | 0.1093 | - | - | - |
| 1.6411 | 1884 | 0.4192 | - | - | - |
| 1.6463 | 1890 | 0.165 | - | - | - |
| 1.6516 | 1896 | 0.1975 | - | - | - |
| 1.6568 | 1902 | 0.2514 | - | - | - |
| 1.6620 | 1908 | 0.3038 | - | - | - |
| 1.6672 | 1914 | 0.4222 | - | - | - |
| 1.6725 | 1920 | 0.2399 | - | - | - |
| 1.6777 | 1926 | 0.1679 | - | - | - |
| 1.6829 | 1932 | 0.3724 | - | - | - |
| 1.6882 | 1938 | 0.1685 | - | - | - |
| 1.6934 | 1944 | 0.4037 | - | - | - |
| 1.6986 | 1950 | 0.3649 | - | - | - |
| 1.7038 | 1956 | 0.3041 | - | - | - |
| 1.7091 | 1962 | 0.29 | - | - | - |
| 1.7143 | 1968 | 0.2204 | - | - | - |
| 1.7195 | 1974 | 0.3762 | - | - | - |
| 1.7247 | 1980 | 0.3857 | - | - | - |
| 1.7300 | 1986 | 0.2591 | - | - | - |
| 1.7352 | 1992 | 0.1436 | - | - | - |
| 1.7404 | 1998 | 0.3725 | - | - | - |
| 1.7456 | 2004 | 0.272 | - | - | - |
| 1.7509 | 2010 | 0.2497 | - | - | - |
| 1.7561 | 2016 | 0.211 | - | - | - |
| 1.7613 | 2022 | 0.339 | - | - | - |
| 1.7666 | 2028 | 0.3218 | - | - | - |
| 1.7718 | 2034 | 0.28 | - | - | - |
| 1.7770 | 2040 | 0.2353 | - | - | - |
| 1.7822 | 2046 | 0.1672 | - | - | - |
| 1.7875 | 2052 | 0.1992 | - | - | - |
| 1.7927 | 2058 | 0.1121 | - | - | - |
| 1.7979 | 2064 | 0.2625 | - | - | - |
| 1.8031 | 2070 | 0.3866 | - | - | - |
| 1.8084 | 2076 | 0.35 | - | - | - |
| 1.8136 | 2082 | 0.1784 | - | - | - |
| 1.8188 | 2088 | 0.2353 | - | - | - |
| 1.8240 | 2094 | 0.2156 | - | - | - |
| 1.8293 | 2100 | 0.1825 | - | - | - |
| 1.8345 | 2106 | 0.2695 | - | - | - |
| 1.8397 | 2112 | 0.2211 | - | - | - |
| 1.8449 | 2118 | 0.3734 | - | - | - |
| 1.8502 | 2124 | 0.4629 | - | - | - |
| 1.8554 | 2130 | 0.1376 | - | - | - |
| 1.8606 | 2136 | 0.2899 | - | - | - |
| 1.8659 | 2142 | 0.2706 | - | - | - |
| 1.8711 | 2148 | 0.3565 | - | - | - |
| 1.8763 | 2154 | 0.1231 | - | - | - |
| 1.8815 | 2160 | 0.3058 | - | - | - |
| 1.8868 | 2166 | 0.1174 | - | - | - |
| 1.8920 | 2172 | 0.2687 | - | - | - |
| 1.8972 | 2178 | 0.1954 | - | - | - |
| 1.9024 | 2184 | 0.1452 | - | - | - |
| 1.9077 | 2190 | 0.2603 | - | - | - |
| 1.9129 | 2196 | 0.2607 | - | - | - |
| 1.9181 | 2202 | 0.2368 | - | - | - |
| 1.9233 | 2208 | 0.3415 | - | - | - |
| 1.9286 | 2214 | 0.1312 | - | - | - |
| 1.9338 | 2220 | 0.1627 | - | - | - |
| 1.9390 | 2226 | 0.1815 | - | - | - |
| 1.9443 | 2232 | 0.089 | - | - | - |
| 1.9495 | 2238 | 0.1868 | - | - | - |
| 1.9547 | 2244 | 0.1073 | - | - | - |
| 1.9599 | 2250 | 0.341 | - | - | - |
| 1.9652 | 2256 | 0.2377 | - | - | - |
| 1.9704 | 2262 | 0.2618 | - | - | - |
| 1.9756 | 2268 | 0.24 | - | - | - |
| 1.9808 | 2274 | 0.2164 | - | - | - |
| 1.9861 | 2280 | 0.1535 | - | - | - |
| 1.9913 | 2286 | 0.3638 | - | - | - |
| 1.9965 | 2292 | 0.2644 | - | - | - |
| 2.0 | 2296 | - | 0.1788 | 0.9993 | - |
| 2.0017 | 2298 | 0.1214 | - | - | - |
| 2.0070 | 2304 | 0.1739 | - | - | - |
| 2.0122 | 2310 | 0.143 | - | - | - |
| 2.0174 | 2316 | 0.2787 | - | - | - |
| 2.0226 | 2322 | 0.0949 | - | - | - |
| 2.0279 | 2328 | 0.1275 | - | - | - |
| 2.0331 | 2334 | 0.1298 | - | - | - |
| 2.0383 | 2340 | 0.1309 | - | - | - |
| 2.0436 | 2346 | 0.2705 | - | - | - |
| 2.0488 | 2352 | 0.1701 | - | - | - |
| 2.0540 | 2358 | 0.0701 | - | - | - |
| 2.0592 | 2364 | 0.1964 | - | - | - |
| 2.0645 | 2370 | 0.3012 | - | - | - |
| 2.0697 | 2376 | 0.0734 | - | - | - |
| 2.0749 | 2382 | 0.1742 | - | - | - |
| 2.0801 | 2388 | 0.0627 | - | - | - |
| 2.0854 | 2394 | 0.1086 | - | - | - |
| 2.0906 | 2400 | 0.0505 | - | - | - |
| 2.0958 | 2406 | 0.1698 | - | - | - |
| 2.1010 | 2412 | 0.2281 | - | - | - |
| 2.1063 | 2418 | 0.2046 | - | - | - |
| 2.1115 | 2424 | 0.2343 | - | - | - |
| 2.1167 | 2430 | 0.1266 | - | - | - |
| 2.1220 | 2436 | 0.0939 | - | - | - |
| 2.1272 | 2442 | 0.2014 | - | - | - |
| 2.1324 | 2448 | 0.0345 | - | - | - |
| 2.1376 | 2454 | 0.164 | - | - | - |
| 2.1429 | 2460 | 0.1983 | - | - | - |
| 2.1481 | 2466 | 0.3191 | - | - | - |
| 2.1533 | 2472 | 0.2991 | - | - | - |
| 2.1585 | 2478 | 0.089 | - | - | - |
| 2.1638 | 2484 | 0.136 | - | - | - |
| 2.1690 | 2490 | 0.0 | - | - | - |
| 2.1742 | 2496 | 0.1122 | - | - | - |
| 2.1794 | 2502 | 0.1697 | - | - | - |
| 2.1847 | 2508 | 0.0866 | - | - | - |
| 2.1899 | 2514 | 0.1509 | - | - | - |
| 2.1951 | 2520 | 0.3506 | - | - | - |
| 2.2003 | 2526 | 0.0 | - | - | - |
| 2.2056 | 2532 | 0.1797 | - | - | - |
| 2.2108 | 2538 | 0.0762 | - | - | - |
| 2.2160 | 2544 | 0.1793 | - | - | - |
| 2.2213 | 2550 | 0.0303 | - | - | - |
| 2.2265 | 2556 | 0.3666 | - | - | - |
| 2.2317 | 2562 | 0.0903 | - | - | - |
| 2.2369 | 2568 | 0.2618 | - | - | - |
| 2.2422 | 2574 | 0.0372 | - | - | - |
| 2.2474 | 2580 | 0.1638 | - | - | - |
| 2.2526 | 2586 | 0.1492 | - | - | - |
| 2.2578 | 2592 | 0.128 | - | - | - |
| 2.2631 | 2598 | 0.2282 | - | - | - |
| 2.2683 | 2604 | 0.14 | - | - | - |
| 2.2735 | 2610 | 0.2016 | - | - | - |
| 2.2787 | 2616 | 0.2329 | - | - | - |
| 2.2840 | 2622 | 0.0231 | - | - | - |
| 2.2892 | 2628 | 0.1221 | - | - | - |
| 2.2944 | 2634 | 0.2853 | - | - | - |
| 2.2997 | 2640 | 0.1054 | - | - | - |
| 2.3049 | 2646 | 0.1585 | - | - | - |
| 2.3101 | 2652 | 0.1773 | - | - | - |
| 2.3153 | 2658 | 0.0978 | - | - | - |
| 2.3206 | 2664 | 0.1096 | - | - | - |
| 2.3258 | 2670 | 0.0669 | - | - | - |
| 2.3310 | 2676 | 0.0727 | - | - | - |
| 2.3362 | 2682 | 0.329 | - | - | - |
| 2.3415 | 2688 | 0.2567 | - | - | - |
| 2.3467 | 2694 | 0.0864 | - | - | - |
| 2.3519 | 2700 | 0.1642 | - | - | - |
| 2.3571 | 2706 | 0.1138 | - | - | - |
| 2.3624 | 2712 | 0.3575 | - | - | - |
| 2.3676 | 2718 | 0.1272 | - | - | - |
| 2.3728 | 2724 | 0.0902 | - | - | - |
| 2.3780 | 2730 | 0.211 | - | - | - |
| 2.3833 | 2736 | 0.1225 | - | - | - |
| 2.3885 | 2742 | 0.1512 | - | - | - |
| 2.3937 | 2748 | 0.2895 | - | - | - |
| 2.3990 | 2754 | 0.1159 | - | - | - |
| 2.4042 | 2760 | 0.0993 | - | - | - |
| 2.4094 | 2766 | 0.109 | - | - | - |
| 2.4146 | 2772 | 0.1609 | - | - | - |
| 2.4199 | 2778 | 0.2196 | - | - | - |
| 2.4251 | 2784 | 0.0618 | - | - | - |
| 2.4303 | 2790 | 0.1255 | - | - | - |
| 2.4355 | 2796 | 0.2316 | - | - | - |
| 2.4408 | 2802 | 0.1175 | - | - | - |
| 2.4460 | 2808 | 0.2023 | - | - | - |
| 2.4512 | 2814 | 0.2239 | - | - | - |
| 2.4564 | 2820 | 0.2202 | - | - | - |
| 2.4617 | 2826 | 0.2029 | - | - | - |
| 2.4669 | 2832 | 0.2325 | - | - | - |
| 2.4721 | 2838 | 0.1697 | - | - | - |
| 2.4774 | 2844 | 0.3761 | - | - | - |
| 2.4826 | 2850 | 0.1319 | - | - | - |
| 2.4878 | 2856 | 0.0234 | - | - | - |
| 2.4930 | 2862 | 0.3063 | - | - | - |
| 2.4983 | 2868 | 0.0783 | - | - | - |
| 2.5035 | 2874 | 0.1579 | - | - | - |
| 2.5087 | 2880 | 0.3283 | - | - | - |
| 2.5139 | 2886 | 0.217 | - | - | - |
| 2.5192 | 2892 | 0.0555 | - | - | - |
| 2.5244 | 2898 | 0.1873 | - | - | - |
| 2.5296 | 2904 | 0.1958 | - | - | - |
| 2.5348 | 2910 | 0.2545 | - | - | - |
| 2.5401 | 2916 | 0.1208 | - | - | - |
| 2.5453 | 2922 | 0.212 | - | - | - |
| 2.5505 | 2928 | 0.2414 | - | - | - |
| 2.5557 | 2934 | 0.1004 | - | - | - |
| 2.5610 | 2940 | 0.0302 | - | - | - |
| 2.5662 | 2946 | 0.1554 | - | - | - |
| 2.5714 | 2952 | 0.1421 | - | - | - |
| 2.5767 | 2958 | 0.1493 | - | - | - |
| 2.5819 | 2964 | 0.2166 | - | - | - |
| 2.5871 | 2970 | 0.4662 | - | - | - |
| 2.5923 | 2976 | 0.0 | - | - | - |
| 2.5976 | 2982 | 0.3535 | - | - | - |
| 2.6028 | 2988 | 0.1567 | - | - | - |
| 2.6080 | 2994 | 0.3008 | - | - | - |
| 2.6132 | 3000 | 0.1711 | - | - | - |
| 2.6185 | 3006 | 0.1507 | - | - | - |
| 2.6237 | 3012 | 0.2314 | - | - | - |
| 2.6289 | 3018 | 0.1814 | - | - | - |
| 2.6341 | 3024 | 0.1327 | - | - | - |
| 2.6394 | 3030 | 0.1694 | - | - | - |
| 2.6446 | 3036 | 0.1623 | - | - | - |
| 2.6498 | 3042 | 0.1089 | - | - | - |
| 2.6551 | 3048 | 0.1668 | - | - | - |
| 2.6603 | 3054 | 0.0577 | - | - | - |
| 2.6655 | 3060 | 0.1246 | - | - | - |
| 2.6707 | 3066 | 0.0771 | - | - | - |
| 2.6760 | 3072 | 0.2558 | - | - | - |
| 2.6812 | 3078 | 0.1282 | - | - | - |
| 2.6864 | 3084 | 0.2405 | - | - | - |
| 2.6916 | 3090 | 0.2521 | - | - | - |
| 2.6969 | 3096 | 0.2159 | - | - | - |
| 2.7021 | 3102 | 0.3155 | - | - | - |
| 2.7073 | 3108 | 0.0728 | - | - | - |
| 2.7125 | 3114 | 0.1084 | - | - | - |
| 2.7178 | 3120 | 0.108 | - | - | - |
| 2.7230 | 3126 | 0.2431 | - | - | - |
| 2.7282 | 3132 | 0.075 | - | - | - |
| 2.7334 | 3138 | 0.2153 | - | - | - |
| 2.7387 | 3144 | 0.2256 | - | - | - |
| 2.7439 | 3150 | 0.153 | - | - | - |
| 2.7491 | 3156 | 0.1715 | - | - | - |
| 2.7544 | 3162 | 0.2082 | - | - | - |
| 2.7596 | 3168 | 0.1098 | - | - | - |
| 2.7648 | 3174 | 0.1658 | - | - | - |
| 2.7700 | 3180 | 0.0779 | - | - | - |
| 2.7753 | 3186 | 0.2188 | - | - | - |
| 2.7805 | 3192 | 0.1308 | - | - | - |
| 2.7857 | 3198 | 0.1662 | - | - | - |
| 2.7909 | 3204 | 0.1753 | - | - | - |
| 2.7962 | 3210 | 0.1767 | - | - | - |
| 2.8014 | 3216 | 0.1818 | - | - | - |
| 2.8066 | 3222 | 0.207 | - | - | - |
| 2.8118 | 3228 | 0.3599 | - | - | - |
| 2.8171 | 3234 | 0.1318 | - | - | - |
| 2.8223 | 3240 | 0.1923 | - | - | - |
| 2.8275 | 3246 | 0.1841 | - | - | - |
| 2.8328 | 3252 | 0.1179 | - | - | - |
| 2.8380 | 3258 | 0.2105 | - | - | - |
| 2.8432 | 3264 | 0.1522 | - | - | - |
| 2.8484 | 3270 | 0.1161 | - | - | - |
| 2.8537 | 3276 | 0.134 | - | - | - |
| 2.8589 | 3282 | 0.0355 | - | - | - |
| 2.8641 | 3288 | 0.1747 | - | - | - |
| 2.8693 | 3294 | 0.101 | - | - | - |
| 2.8746 | 3300 | 0.1603 | - | - | - |
| 2.8798 | 3306 | 0.1461 | - | - | - |
| 2.8850 | 3312 | 0.0955 | - | - | - |
| 2.8902 | 3318 | 0.1072 | - | - | - |
| 2.8955 | 3324 | 0.0749 | - | - | - |
| 2.9007 | 3330 | 0.3698 | - | - | - |
| 2.9059 | 3336 | 0.1146 | - | - | - |
| 2.9111 | 3342 | 0.0699 | - | - | - |
| 2.9164 | 3348 | 0.2239 | - | - | - |
| 2.9216 | 3354 | 0.2916 | - | - | - |
| 2.9268 | 3360 | 0.1078 | - | - | - |
| 2.9321 | 3366 | 0.2395 | - | - | - |
| 2.9373 | 3372 | 0.3056 | - | - | - |
| 2.9425 | 3378 | 0.0643 | - | - | - |
| 2.9477 | 3384 | 0.1579 | - | - | - |
| 2.9530 | 3390 | 0.2721 | - | - | - |
| 2.9582 | 3396 | 0.1975 | - | - | - |
| 2.9634 | 3402 | 0.1886 | - | - | - |
| 2.9686 | 3408 | 0.1968 | - | - | - |
| 2.9739 | 3414 | 0.0632 | - | - | - |
| 2.9791 | 3420 | 0.1413 | - | - | - |
| 2.9843 | 3426 | 0.1126 | - | - | - |
| 2.9895 | 3432 | 0.1712 | - | - | - |
| 2.9948 | 3438 | 0.042 | - | - | - |
| 3.0 | 3444 | 0.236 | 0.1238 | 0.9994 | - |
| -1 | -1 | - | - | - | 0.9994 |