---
base_model: sentence-transformers/all-distilroberta-v1
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:4232571
- loss:MaskedCachedMultipleNegativesRankingLoss
widget:
- source_sentence: Mathlib.Data.ENNReal.Operations#190
sentences:
- Finset.Nonempty.cons_induction
- Set.mem_iUnion
- exists_prop
- source_sentence: Mathlib.LinearAlgebra.CliffordAlgebra.Basic#46
sentences:
- Int.sub_nonneg_of_le
- SMulCommClass.symm
- CategoryTheory.Category.id_comp
- source_sentence: Mathlib.AlgebraicGeometry.ProjectiveSpectrum.StructureSheaf#23
sentences:
- hasFDerivAt_pi
- Set.IsPWO.image_of_monotone
- Pi.mul_apply
- source_sentence: Mathlib.Data.Nat.ModEq#84
sentences:
- Nat.gcd_dvd_left
- neg_neg
- map_list_sum
- source_sentence: Mathlib.AlgebraicGeometry.Noetherian#22
sentences:
- AlgebraicGeometry.of_affine_open_cover
- pow_lt_pow_right_of_lt_one₀
- LT.lt.trans_le
---
# SentenceTransformer based on sentence-transformers/all-distilroberta-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1). 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:** [sentence-transformers/all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(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})
(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("hanwenzhu/all-distilroberta-v1-lr2e-4-bs1024-nneg3-ml")
# Run inference
sentences = [
'Mathlib.AlgebraicGeometry.Noetherian#22',
'AlgebraicGeometry.of_affine_open_cover',
'pow_lt_pow_right_of_lt_one₀',
]
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]
```
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 4,232,571 training samples
* Columns: state_name and premise_name
* Approximate statistics based on the first 1000 samples:
| | state_name | premise_name |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 11 tokens
- mean: 16.91 tokens
- max: 28 tokens
| - min: 3 tokens
- mean: 10.27 tokens
- max: 27 tokens
|
* Samples:
| state_name | premise_name |
|:---------------------------------------------------------|:-------------------------------------|
| Mathlib.Algebra.Group.Subgroup.Pointwise#27 | Set.mul_subgroupClosure |
| Mathlib.Algebra.Group.Subgroup.Pointwise#27 | pow_succ |
| Mathlib.Algebra.Group.Subgroup.Pointwise#27 | mul_assoc |
* Loss: loss.MaskedCachedMultipleNegativesRankingLoss with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 1,648 evaluation samples
* Columns: state_name and premise_name
* Approximate statistics based on the first 1000 samples:
| | state_name | premise_name |
|:--------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 12 tokens
- mean: 17.34 tokens
- max: 26 tokens
| - min: 3 tokens
- mean: 10.9 tokens
- max: 34 tokens
|
* Samples:
| state_name | premise_name |
|:-------------------------------------------------------|:--------------------------------------|
| Mathlib.Algebra.BigOperators.Associated#0 | Prime.dvd_or_dvd |
| Mathlib.Algebra.BigOperators.Associated#0 | Multiset.induction_on |
| Mathlib.Algebra.BigOperators.Associated#0 | Multiset.mem_cons_of_mem |
* Loss: loss.MaskedCachedMultipleNegativesRankingLoss with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 1024
- `per_device_eval_batch_size`: 64
- `learning_rate`: 0.0002
- `num_train_epochs`: 1.0
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.03
- `bf16`: True
- `dataloader_num_workers`: 4
- `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`: 1024
- `per_device_eval_batch_size`: 64
- `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`: 0.0002
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1.0
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: 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`: 4
- `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
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | loss |
|:------:|:----:|:-------------:|:------:|
| 0.0024 | 10 | 6.4577 | - |
| 0.0048 | 20 | 6.011 | - |
| 0.0073 | 30 | 5.6038 | - |
| 0.0097 | 40 | 5.3306 | - |
| 0.0102 | 42 | - | 1.8049 |
| 0.0121 | 50 | 5.139 | - |
| 0.0145 | 60 | 5.0408 | - |
| 0.0169 | 70 | 4.9269 | - |
| 0.0194 | 80 | 4.8676 | - |
| 0.0203 | 84 | - | 1.6211 |
| 0.0218 | 90 | 4.7792 | - |
| 0.0242 | 100 | 4.7427 | - |
| 0.0266 | 110 | 4.6929 | - |
| 0.0290 | 120 | 4.6701 | - |
| 0.0305 | 126 | - | 1.4521 |
| 0.0314 | 130 | 4.5866 | - |
| 0.0339 | 140 | 4.5066 | - |
| 0.0363 | 150 | 4.5189 | - |
| 0.0387 | 160 | 4.4494 | - |
| 0.0406 | 168 | - | 1.4517 |
| 0.0411 | 170 | 4.4117 | - |
| 0.0435 | 180 | 4.3827 | - |
| 0.0460 | 190 | 4.2533 | - |
| 0.0484 | 200 | 4.2634 | - |
| 0.0508 | 210 | 4.2472 | 1.3644 |
| 0.0532 | 220 | 4.1949 | - |
| 0.0556 | 230 | 4.1769 | - |
| 0.0581 | 240 | 4.1372 | - |
| 0.0605 | 250 | 4.0943 | - |
| 0.0610 | 252 | - | 1.3161 |
| 0.0629 | 260 | 4.1049 | - |
| 0.0653 | 270 | 4.1018 | - |
| 0.0677 | 280 | 4.078 | - |
| 0.0701 | 290 | 4.0355 | - |
| 0.0711 | 294 | - | 1.2026 |
| 0.0726 | 300 | 4.0104 | - |
| 0.0750 | 310 | 3.9392 | - |
| 0.0774 | 320 | 3.9519 | - |
| 0.0798 | 330 | 3.9671 | - |
| 0.0813 | 336 | - | 1.1869 |
| 0.0822 | 340 | 3.9297 | - |
| 0.0847 | 350 | 3.9435 | - |
| 0.0871 | 360 | 3.9317 | - |
| 0.0895 | 370 | 3.8544 | - |
| 0.0914 | 378 | - | 1.1943 |
| 0.0919 | 380 | 3.9131 | - |
| 0.0943 | 390 | 3.8758 | - |
| 0.0968 | 400 | 3.7628 | - |
| 0.0992 | 410 | 3.8589 | - |
| 0.1016 | 420 | 3.8057 | 1.1280 |
| 0.1040 | 430 | 3.7792 | - |
| 0.1064 | 440 | 3.8011 | - |
| 0.1089 | 450 | 3.7708 | - |
| 0.1113 | 460 | 3.7248 | - |
| 0.1118 | 462 | - | 1.1578 |
| 0.1137 | 470 | 3.6717 | - |
| 0.1161 | 480 | 3.643 | - |
| 0.1185 | 490 | 3.6564 | - |
| 0.1209 | 500 | 3.6266 | - |
| 0.1219 | 504 | - | 1.1440 |
| 0.1234 | 510 | 3.6275 | - |
| 0.1258 | 520 | 3.6675 | - |
| 0.1282 | 530 | 3.6608 | - |
| 0.1306 | 540 | 3.6002 | - |
| 0.1321 | 546 | - | 1.1416 |
| 0.1330 | 550 | 3.6128 | - |
| 0.1355 | 560 | 3.6028 | - |
| 0.1379 | 570 | 3.5061 | - |
| 0.1403 | 580 | 3.5551 | - |
| 0.1422 | 588 | - | 1.0684 |
| 0.1427 | 590 | 3.5213 | - |
| 0.1451 | 600 | 3.495 | - |
| 0.1476 | 610 | 3.5169 | - |
| 0.1500 | 620 | 3.4666 | - |
| 0.1524 | 630 | 3.4942 | 1.0657 |
| 0.1548 | 640 | 3.4864 | - |
| 0.1572 | 650 | 3.4139 | - |
| 0.1597 | 660 | 3.3886 | - |
| 0.1621 | 670 | 3.3498 | - |
| 0.1626 | 672 | - | 1.0647 |
| 0.1645 | 680 | 3.3646 | - |
| 0.1669 | 690 | 3.3792 | - |
| 0.1693 | 700 | 3.3803 | - |
| 0.1717 | 710 | 3.3244 | - |
| 0.1727 | 714 | - | 1.0366 |
| 0.1742 | 720 | 3.3935 | - |
| 0.1766 | 730 | 3.4148 | - |
| 0.1790 | 740 | 3.3258 | - |
| 0.1814 | 750 | 3.3057 | - |
| 0.1829 | 756 | - | 0.9969 |
| 0.1838 | 760 | 3.3044 | - |
| 0.1863 | 770 | 3.3046 | - |
| 0.1887 | 780 | 3.2663 | - |
| 0.1911 | 790 | 3.2622 | - |
| 0.1930 | 798 | - | 0.9886 |
| 0.1935 | 800 | 3.3027 | - |
| 0.1959 | 810 | 3.3228 | - |
| 0.1984 | 820 | 3.2329 | - |
| 0.2008 | 830 | 3.2792 | - |
| 0.2032 | 840 | 3.2124 | 0.9268 |
| 0.2056 | 850 | 3.1746 | - |
| 0.2080 | 860 | 3.1745 | - |
| 0.2104 | 870 | 3.1741 | - |
| 0.2129 | 880 | 3.242 | - |
| 0.2134 | 882 | - | 0.9676 |
| 0.2153 | 890 | 3.2074 | - |
| 0.2177 | 900 | 3.0812 | - |
| 0.2201 | 910 | 3.1686 | - |
| 0.2225 | 920 | 3.1844 | - |
| 0.2235 | 924 | - | 0.9905 |
| 0.2250 | 930 | 3.1659 | - |
| 0.2274 | 940 | 3.0974 | - |
| 0.2298 | 950 | 3.1673 | - |
| 0.2322 | 960 | 3.1398 | - |
| 0.2337 | 966 | - | 0.9434 |
| 0.2346 | 970 | 3.1269 | - |
| 0.2371 | 980 | 3.0904 | - |
| 0.2395 | 990 | 3.0663 | - |
| 0.2419 | 1000 | 3.0815 | - |
| 0.2438 | 1008 | - | 0.9529 |
| 0.2443 | 1010 | 2.9928 | - |
| 0.2467 | 1020 | 3.0058 | - |
| 0.2492 | 1030 | 3.0084 | - |
| 0.2516 | 1040 | 3.0597 | - |
| 0.2540 | 1050 | 3.0111 | 0.9823 |
| 0.2564 | 1060 | 2.9955 | - |
| 0.2588 | 1070 | 2.9575 | - |
| 0.2612 | 1080 | 2.9818 | - |
| 0.2637 | 1090 | 3.0291 | - |
| 0.2642 | 1092 | - | 0.9308 |
| 0.2661 | 1100 | 3.0057 | - |
| 0.2685 | 1110 | 2.9912 | - |
| 0.2709 | 1120 | 2.9504 | - |
| 0.2733 | 1130 | 2.971 | - |
| 0.2743 | 1134 | - | 0.9150 |
| 0.2758 | 1140 | 2.9252 | - |
| 0.2782 | 1150 | 2.9444 | - |
| 0.2806 | 1160 | 2.9667 | - |
| 0.2830 | 1170 | 2.9109 | - |
| 0.2845 | 1176 | - | 0.9648 |
| 0.2854 | 1180 | 2.8874 | - |
| 0.2879 | 1190 | 2.9271 | - |
| 0.2903 | 1200 | 2.8456 | - |
| 0.2927 | 1210 | 2.8096 | - |
| 0.2946 | 1218 | - | 0.9288 |
| 0.2951 | 1220 | 2.8143 | - |
| 0.2975 | 1230 | 2.8275 | - |
| 0.3000 | 1240 | 2.7645 | - |
| 0.3024 | 1250 | 2.8012 | - |
| 0.3048 | 1260 | 2.8237 | 0.9021 |
| 0.3072 | 1270 | 2.8388 | - |
| 0.3096 | 1280 | 2.8354 | - |
| 0.3120 | 1290 | 2.8441 | - |
| 0.3145 | 1300 | 2.7928 | - |
| 0.3149 | 1302 | - | 0.8679 |
| 0.3169 | 1310 | 2.7765 | - |
| 0.3193 | 1320 | 2.7912 | - |
| 0.3217 | 1330 | 2.8062 | - |
| 0.3241 | 1340 | 2.8296 | - |
| 0.3251 | 1344 | - | 0.8739 |
| 0.3266 | 1350 | 2.7594 | - |
| 0.3290 | 1360 | 2.7772 | - |
| 0.3314 | 1370 | 2.7557 | - |
| 0.3338 | 1380 | 2.7978 | - |
| 0.3353 | 1386 | - | 0.8085 |
| 0.3362 | 1390 | 2.7711 | - |
| 0.3387 | 1400 | 2.7239 | - |
| 0.3411 | 1410 | 2.7382 | - |
| 0.3435 | 1420 | 2.7235 | - |
| 0.3454 | 1428 | - | 0.8075 |
| 0.3459 | 1430 | 2.7126 | - |
| 0.3483 | 1440 | 2.7319 | - |
| 0.3507 | 1450 | 2.7015 | - |
| 0.3532 | 1460 | 2.7161 | - |
| 0.3556 | 1470 | 2.6951 | 0.7942 |
| 0.3580 | 1480 | 2.6832 | - |
| 0.3604 | 1490 | 2.7305 | - |
| 0.3628 | 1500 | 2.6417 | - |
| 0.3653 | 1510 | 2.6772 | - |
| 0.3657 | 1512 | - | 0.8244 |
| 0.3677 | 1520 | 2.6933 | - |
| 0.3701 | 1530 | 2.6397 | - |
| 0.3725 | 1540 | 2.6323 | - |
| 0.3749 | 1550 | 2.6216 | - |
| 0.3759 | 1554 | - | 0.8660 |
| 0.3774 | 1560 | 2.6384 | - |
| 0.3798 | 1570 | 2.669 | - |
| 0.3822 | 1580 | 2.6828 | - |
| 0.3846 | 1590 | 2.6789 | - |
| 0.3861 | 1596 | - | 0.8344 |
| 0.3870 | 1600 | 2.6774 | - |
| 0.3895 | 1610 | 2.6501 | - |
| 0.3919 | 1620 | 2.63 | - |
| 0.3943 | 1630 | 2.6474 | - |
| 0.3962 | 1638 | - | 0.7953 |
| 0.3967 | 1640 | 2.6595 | - |
| 0.3991 | 1650 | 2.7007 | - |
| 0.4015 | 1660 | 2.639 | - |
| 0.4040 | 1670 | 2.6418 | - |
| 0.4064 | 1680 | 2.6044 | 0.7789 |
| 0.4088 | 1690 | 2.6058 | - |
| 0.4112 | 1700 | 2.564 | - |
| 0.4136 | 1710 | 2.5331 | - |
| 0.4161 | 1720 | 2.5746 | - |
| 0.4165 | 1722 | - | 0.8096 |
| 0.4185 | 1730 | 2.5725 | - |
| 0.4209 | 1740 | 2.5796 | - |
| 0.4233 | 1750 | 2.5675 | - |
| 0.4257 | 1760 | 2.558 | - |
| 0.4267 | 1764 | - | 0.7845 |
| 0.4282 | 1770 | 2.5968 | - |
| 0.4306 | 1780 | 2.5798 | - |
| 0.4330 | 1790 | 2.4829 | - |
| 0.4354 | 1800 | 2.4951 | - |
| 0.4369 | 1806 | - | 0.7755 |
| 0.4378 | 1810 | 2.519 | - |
| 0.4403 | 1820 | 2.4864 | - |
| 0.4427 | 1830 | 2.5012 | - |
| 0.4451 | 1840 | 2.5165 | - |
| 0.4470 | 1848 | - | 0.7455 |
| 0.4475 | 1850 | 2.5074 | - |
| 0.4499 | 1860 | 2.4461 | - |
| 0.4523 | 1870 | 2.452 | - |
| 0.4548 | 1880 | 2.5045 | - |
| 0.4572 | 1890 | 2.4821 | 0.7466 |
| 0.4596 | 1900 | 2.5006 | - |
| 0.4620 | 1910 | 2.4616 | - |
| 0.4644 | 1920 | 2.4638 | - |
| 0.4669 | 1930 | 2.4698 | - |
| 0.4673 | 1932 | - | 0.7377 |
| 0.4693 | 1940 | 2.5035 | - |
| 0.4717 | 1950 | 2.4711 | - |
| 0.4741 | 1960 | 2.5317 | - |
| 0.4765 | 1970 | 2.472 | - |
| 0.4775 | 1974 | - | 0.7255 |
| 0.4790 | 1980 | 2.438 | - |
| 0.4814 | 1990 | 2.432 | - |
| 0.4838 | 2000 | 2.3946 | - |
| 0.4862 | 2010 | 2.3805 | - |
| 0.4877 | 2016 | - | 0.7449 |
| 0.4886 | 2020 | 2.4001 | - |
| 0.4910 | 2030 | 2.418 | - |
| 0.4935 | 2040 | 2.3911 | - |
| 0.4959 | 2050 | 2.4212 | - |
| 0.4978 | 2058 | - | 0.7663 |
| 0.4983 | 2060 | 2.3855 | - |
| 0.5007 | 2070 | 2.3713 | - |
| 0.5031 | 2080 | 2.4021 | - |
| 0.5056 | 2090 | 2.3537 | - |
| 0.5080 | 2100 | 2.4182 | 0.7588 |
| 0.5104 | 2110 | 2.413 | - |
| 0.5128 | 2120 | 2.3741 | - |
| 0.5152 | 2130 | 2.4061 | - |
| 0.5177 | 2140 | 2.4137 | - |
| 0.5181 | 2142 | - | 0.7185 |
| 0.5201 | 2150 | 2.3823 | - |
| 0.5225 | 2160 | 2.3781 | - |
| 0.5249 | 2170 | 2.3621 | - |
| 0.5273 | 2180 | 2.3601 | - |
| 0.5283 | 2184 | - | 0.7088 |
| 0.5298 | 2190 | 2.4113 | - |
| 0.5322 | 2200 | 2.2813 | - |
| 0.5346 | 2210 | 2.3359 | - |
| 0.5370 | 2220 | 2.3571 | - |
| 0.5385 | 2226 | - | 0.7379 |
| 0.5394 | 2230 | 2.3492 | - |
| 0.5418 | 2240 | 2.366 | - |
| 0.5443 | 2250 | 2.3369 | - |
| 0.5467 | 2260 | 2.2976 | - |
| 0.5486 | 2268 | - | 0.7122 |
| 0.5491 | 2270 | 2.322 | - |
| 0.5515 | 2280 | 2.3378 | - |
| 0.5539 | 2290 | 2.3309 | - |
| 0.5564 | 2300 | 2.3335 | - |
| 0.5588 | 2310 | 2.3072 | 0.7062 |
| 0.5612 | 2320 | 2.3204 | - |
| 0.5636 | 2330 | 2.3422 | - |
| 0.5660 | 2340 | 2.3745 | - |
| 0.5685 | 2350 | 2.357 | - |
| 0.5689 | 2352 | - | 0.6977 |
| 0.5709 | 2360 | 2.3391 | - |
| 0.5733 | 2370 | 2.2945 | - |
| 0.5757 | 2380 | 2.2974 | - |
| 0.5781 | 2390 | 2.2967 | - |
| 0.5791 | 2394 | - | 0.6999 |
| 0.5806 | 2400 | 2.3177 | - |
| 0.5830 | 2410 | 2.3384 | - |
| 0.5854 | 2420 | 2.2601 | - |
| 0.5878 | 2430 | 2.2544 | - |
| 0.5893 | 2436 | - | 0.6774 |
| 0.5902 | 2440 | 2.2491 | - |
| 0.5926 | 2450 | 2.2732 | - |
| 0.5951 | 2460 | 2.2231 | - |
| 0.5975 | 2470 | 2.2812 | - |
| 0.5994 | 2478 | - | 0.6634 |
| 0.5999 | 2480 | 2.2717 | - |
| 0.6023 | 2490 | 2.2238 | - |
| 0.6047 | 2500 | 2.2699 | - |
| 0.6072 | 2510 | 2.2256 | - |
| 0.6096 | 2520 | 2.2547 | 0.6635 |
| 0.6120 | 2530 | 2.224 | - |
| 0.6144 | 2540 | 2.2645 | - |
| 0.6168 | 2550 | 2.2098 | - |
| 0.6193 | 2560 | 2.1807 | - |
| 0.6197 | 2562 | - | 0.6813 |
| 0.6217 | 2570 | 2.2292 | - |
| 0.6241 | 2580 | 2.1626 | - |
| 0.6265 | 2590 | 2.17 | - |
| 0.6289 | 2600 | 2.1772 | - |
| 0.6299 | 2604 | - | 0.6646 |
| 0.6313 | 2610 | 2.2138 | - |
| 0.6338 | 2620 | 2.2005 | - |
| 0.6362 | 2630 | 2.1698 | - |
| 0.6386 | 2640 | 2.1521 | - |
| 0.6401 | 2646 | - | 0.6704 |
| 0.6410 | 2650 | 2.2262 | - |
| 0.6434 | 2660 | 2.2312 | - |
| 0.6459 | 2670 | 2.187 | - |
| 0.6483 | 2680 | 2.1775 | - |
| 0.6502 | 2688 | - | 0.6599 |
| 0.6507 | 2690 | 2.1486 | - |
| 0.6531 | 2700 | 2.175 | - |
| 0.6555 | 2710 | 2.187 | - |
| 0.6580 | 2720 | 2.1859 | - |
| 0.6604 | 2730 | 2.1693 | 0.6518 |
| 0.6628 | 2740 | 2.1661 | - |
| 0.6652 | 2750 | 2.1916 | - |
| 0.6676 | 2760 | 2.1953 | - |
| 0.6701 | 2770 | 2.1674 | - |
| 0.6705 | 2772 | - | 0.6670 |
| 0.6725 | 2780 | 2.1716 | - |
| 0.6749 | 2790 | 2.189 | - |
| 0.6773 | 2800 | 2.1499 | - |
| 0.6797 | 2810 | 2.198 | - |
| 0.6807 | 2814 | - | 0.6443 |
| 0.6821 | 2820 | 2.1888 | - |
| 0.6846 | 2830 | 2.182 | - |
| 0.6870 | 2840 | 2.1553 | - |
| 0.6894 | 2850 | 2.1383 | - |
| 0.6909 | 2856 | - | 0.6478 |
| 0.6918 | 2860 | 2.1612 | - |
| 0.6942 | 2870 | 2.1143 | - |
| 0.6967 | 2880 | 2.1486 | - |
| 0.6991 | 2890 | 2.1399 | - |
| 0.7010 | 2898 | - | 0.6526 |
| 0.7015 | 2900 | 2.1102 | - |
| 0.7039 | 2910 | 2.1406 | - |
| 0.7063 | 2920 | 2.1497 | - |
| 0.7088 | 2930 | 2.1516 | - |
| 0.7112 | 2940 | 2.157 | 0.6488 |
| 0.7136 | 2950 | 2.1253 | - |
| 0.7160 | 2960 | 2.1263 | - |
| 0.7184 | 2970 | 2.1494 | - |
| 0.7209 | 2980 | 2.1852 | - |
| 0.7213 | 2982 | - | 0.6403 |
| 0.7233 | 2990 | 2.1337 | - |
| 0.7257 | 3000 | 2.0886 | - |
| 0.7281 | 3010 | 2.1446 | - |
| 0.7305 | 3020 | 2.1968 | - |
| 0.7315 | 3024 | - | 0.6295 |
| 0.7329 | 3030 | 2.1591 | - |
| 0.7354 | 3040 | 2.2047 | - |
| 0.7378 | 3050 | 2.1976 | - |
| 0.7402 | 3060 | 2.1879 | - |
| 0.7417 | 3066 | - | 0.6194 |
| 0.7426 | 3070 | 2.1718 | - |
| 0.7450 | 3080 | 2.1308 | - |
| 0.7475 | 3090 | 2.1689 | - |
| 0.7499 | 3100 | 2.1403 | - |
| 0.7518 | 3108 | - | 0.6232 |
| 0.7523 | 3110 | 2.1289 | - |
| 0.7547 | 3120 | 2.1357 | - |
| 0.7571 | 3130 | 2.0794 | - |
| 0.7596 | 3140 | 2.0682 | - |
| 0.7620 | 3150 | 2.0474 | 0.6240 |
| 0.7644 | 3160 | 2.0671 | - |
| 0.7668 | 3170 | 2.102 | - |
| 0.7692 | 3180 | 2.1298 | - |
| 0.7716 | 3190 | 2.1423 | - |
| 0.7721 | 3192 | - | 0.6201 |
| 0.7741 | 3200 | 2.1402 | - |
| 0.7765 | 3210 | 2.0642 | - |
| 0.7789 | 3220 | 2.1015 | - |
| 0.7813 | 3230 | 2.0943 | - |
| 0.7823 | 3234 | - | 0.6179 |
| 0.7837 | 3240 | 2.0712 | - |
| 0.7862 | 3250 | 2.0815 | - |
| 0.7886 | 3260 | 2.1121 | - |
| 0.7910 | 3270 | 2.0644 | - |
| 0.7925 | 3276 | - | 0.6156 |
| 0.7934 | 3280 | 2.0557 | - |
| 0.7958 | 3290 | 2.1012 | - |
| 0.7983 | 3300 | 2.052 | - |
| 0.8007 | 3310 | 2.0757 | - |
| 0.8026 | 3318 | - | 0.6016 |
| 0.8031 | 3320 | 2.0778 | - |
| 0.8055 | 3330 | 2.0894 | - |
| 0.8079 | 3340 | 2.0869 | - |
| 0.8104 | 3350 | 2.02 | - |
| 0.8128 | 3360 | 2.0559 | 0.6053 |
| 0.8152 | 3370 | 2.0366 | - |
| 0.8176 | 3380 | 2.04 | - |
| 0.8200 | 3390 | 2.1044 | - |
| 0.8224 | 3400 | 2.0686 | - |
| 0.8229 | 3402 | - | 0.6000 |
| 0.8249 | 3410 | 2.0828 | - |
| 0.8273 | 3420 | 2.0871 | - |
| 0.8297 | 3430 | 2.0887 | - |
| 0.8321 | 3440 | 2.1046 | - |
| 0.8331 | 3444 | - | 0.6045 |
| 0.8345 | 3450 | 2.0854 | - |
| 0.8370 | 3460 | 2.0727 | - |
| 0.8394 | 3470 | 2.0631 | - |
| 0.8418 | 3480 | 1.9793 | - |
| 0.8433 | 3486 | - | 0.5937 |
| 0.8442 | 3490 | 2.0554 | - |
| 0.8466 | 3500 | 2.0813 | - |
| 0.8491 | 3510 | 2.0382 | - |
| 0.8515 | 3520 | 2.0452 | - |
| 0.8534 | 3528 | - | 0.5968 |
| 0.8539 | 3530 | 2.0577 | - |
| 0.8563 | 3540 | 2.036 | - |
| 0.8587 | 3550 | 2.0794 | - |
| 0.8612 | 3560 | 2.0635 | - |
| 0.8636 | 3570 | 2.0277 | 0.5926 |
| 0.8660 | 3580 | 2.0952 | - |
| 0.8684 | 3590 | 2.0965 | - |
| 0.8708 | 3600 | 2.029 | - |
| 0.8732 | 3610 | 2.061 | - |
| 0.8737 | 3612 | - | 0.5937 |
| 0.8757 | 3620 | 1.9961 | - |
| 0.8781 | 3630 | 1.6592 | - |
| 0.8805 | 3640 | 1.506 | - |
| 0.8829 | 3650 | 1.6058 | - |
| 0.8839 | 3654 | - | 0.5780 |
| 0.8853 | 3660 | 1.7033 | - |
| 0.8878 | 3670 | 1.8416 | - |
| 0.8902 | 3680 | 1.9193 | - |
| 0.8926 | 3690 | 2.0024 | - |
| 0.8940 | 3696 | - | 0.6375 |
| 0.8950 | 3700 | 1.9548 | - |
| 0.8974 | 3710 | 1.9862 | - |
| 0.8999 | 3720 | 2.0547 | - |
| 0.9023 | 3730 | 2.0142 | - |
| 0.9042 | 3738 | - | 0.6825 |
| 0.9047 | 3740 | 1.992 | - |
| 0.9071 | 3750 | 1.9453 | - |
| 0.9095 | 3760 | 1.9988 | - |
| 0.9119 | 3770 | 1.9175 | - |
| 0.9144 | 3780 | 1.964 | 0.7054 |
| 0.9168 | 3790 | 2.0087 | - |
| 0.9192 | 3800 | 2.0223 | - |
| 0.9216 | 3810 | 1.9337 | - |
| 0.9240 | 3820 | 1.9478 | - |
| 0.9245 | 3822 | - | 0.7357 |
| 0.9265 | 3830 | 1.9026 | - |
| 0.9289 | 3840 | 2.0058 | - |
| 0.9313 | 3850 | 1.9698 | - |
| 0.9337 | 3860 | 1.9783 | - |
| 0.9347 | 3864 | - | 0.7518 |
| 0.9361 | 3870 | 2.0335 | - |
| 0.9386 | 3880 | 1.9112 | - |
| 0.9410 | 3890 | 1.9733 | - |
| 0.9434 | 3900 | 1.9693 | - |
| 0.9448 | 3906 | - | 0.7665 |
| 0.9458 | 3910 | 1.9911 | - |
| 0.9482 | 3920 | 1.8972 | - |
| 0.9507 | 3930 | 1.9521 | - |
| 0.9531 | 3940 | 1.9827 | - |
| 0.9550 | 3948 | - | 0.7700 |
| 0.9555 | 3950 | 2.0008 | - |
| 0.9579 | 3960 | 1.9525 | - |
| 0.9603 | 3970 | 2.0095 | - |
| 0.9627 | 3980 | 2.018 | - |
| 0.9652 | 3990 | 1.9514 | 0.7782 |
| 0.9676 | 4000 | 1.878 | - |
| 0.9700 | 4010 | 1.9244 | - |
| 0.9724 | 4020 | 1.9141 | - |
| 0.9748 | 4030 | 1.8425 | - |
| 0.9753 | 4032 | - | 0.7829 |
| 0.9773 | 4040 | 1.899 | - |
| 0.9797 | 4050 | 2.0281 | - |
| 0.9821 | 4060 | 1.9944 | - |
| 0.9845 | 4070 | 2.0086 | - |
| 0.9855 | 4074 | - | 0.7848 |
| 0.9869 | 4080 | 1.8952 | - |
| 0.9894 | 4090 | 1.9491 | - |
| 0.9918 | 4100 | 1.9953 | - |
| 0.9942 | 4110 | 1.9592 | - |
| 0.9956 | 4116 | - | 0.7852 |
| 0.9966 | 4120 | 1.8991 | - |
| 0.9990 | 4130 | 1.9578 | - |