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---
language:
- en
license: apache-2.0
tags:
- cross-encoder
- sentence-transformers
- text-classification
- sentence-pair-classification
- semantic-similarity
- semantic-search
- retrieval
- reranking
- generated_from_trainer
- dataset_size:40015318
- loss:BinaryCrossEntropyLoss
base_model: cross-encoder/ms-marco-TinyBERT-L2-v2
datasets:
- adbaral/langcache-sentencepairs-v3
pipeline_tag: text-ranking
library_name: sentence-transformers
metrics:
- accuracy
- accuracy_threshold
- f1
- f1_threshold
- precision
- recall
- average_precision
- map
- mrr@1
- ndcg@1
model-index:
- name: Redis fine-tuned CrossEncoder model for semantic caching on LangCache
results:
- task:
type: cross-encoder-classification
name: Cross Encoder Classification
dataset:
name: test cls
type: test_cls
metrics:
- type: accuracy
value: 0.5703628896519222
name: Accuracy
- type: accuracy_threshold
value: -2.546875
name: Accuracy Threshold
- type: f1
value: 0.6399568354457164
name: F1
- type: f1_threshold
value: -3.0625
name: F1 Threshold
- type: precision
value: 0.4934011974094194
name: Precision
- type: recall
value: 0.9103633025313789
name: Recall
- type: average_precision
value: 0.5199179546549424
name: Average Precision
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoQuoraRetrieval R25
type: NanoQuoraRetrieval_R25
metrics:
- type: map
value: 0.2815
name: Map
- type: mrr@1
value: 0.12
name: Mrr@1
- type: ndcg@1
value: 0.12
name: Ndcg@1
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoMSMARCO R25
type: NanoMSMARCO_R25
metrics:
- type: map
value: 0.1786
name: Map
- type: mrr@1
value: 0.04
name: Mrr@1
- type: ndcg@1
value: 0.04
name: Ndcg@1
- task:
type: cross-encoder-reranking
name: Cross Encoder Reranking
dataset:
name: NanoNQ R25
type: NanoNQ_R25
metrics:
- type: map
value: 0.1402
name: Map
- type: mrr@1
value: 0.04
name: Mrr@1
- type: ndcg@1
value: 0.04
name: Ndcg@1
- task:
type: cross-encoder-nano-beir
name: Cross Encoder Nano BEIR
dataset:
name: NanoBEIR R25 mean
type: NanoBEIR_R25_mean
metrics:
- type: map
value: 0.2001
name: Map
- type: mrr@1
value: 0.0667
name: Mrr@1
- type: ndcg@1
value: 0.0667
name: Ndcg@1
---
# Redis fine-tuned CrossEncoder model for semantic caching on LangCache
This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-TinyBERT-L2-v2](https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L2-v2) on the [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v3) dataset using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for sentence pair classification.
## Model Details
### Model Description
- **Model Type:** Cross Encoder
- **Base model:** [cross-encoder/ms-marco-TinyBERT-L2-v2](https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L2-v2) <!-- at revision 81d1926f67cb8eee2c2be17ca9f793c7c3bd20cc -->
- **Maximum Sequence Length:** 512 tokens
- **Number of Output Labels:** 1 label
- **Training Dataset:**
- [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v3)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
## 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 CrossEncoder
# Download from the 🤗 Hub
model = CrossEncoder("redis/langcache-reranker-v2-tiny-bce-label-smoothing-0.5")
# Get scores for pairs of texts
pairs = [
['The newer Punts are still very much in existence today and race in the same fleets as the older boats .', 'The newer punts are still very much in existence today and run in the same fleets as the older boats .'],
['Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .', 'Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .'],
['After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .', 'Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .'],
['She married Peter Haygarth on 29 May 1964 in Durban . Her second marriage , to Robin Osborne , took place in 1977 .', 'She married Robin Osborne on May 29 , 1964 in Durban , and her second marriage with Peter Haygarth took place in 1977 .'],
['In 2005 she moved to Norway , settled in Geilo and worked as a rafting guide , in 2006 she started mountain biking - races .', 'In 2005 , she moved to Geilo , settling in Norway and worked as a rafting guide . She started mountain bike races in 2006 .'],
]
scores = model.predict(pairs)
print(scores.shape)
# (5,)
# Or rank different texts based on similarity to a single text
ranks = model.rank(
'The newer Punts are still very much in existence today and race in the same fleets as the older boats .',
[
'The newer punts are still very much in existence today and run in the same fleets as the older boats .',
'Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .',
'Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .',
'She married Robin Osborne on May 29 , 1964 in Durban , and her second marriage with Peter Haygarth took place in 1977 .',
'In 2005 , she moved to Geilo , settling in Norway and worked as a rafting guide . She started mountain bike races in 2006 .',
]
)
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Cross Encoder Classification
* Dataset: `test_cls`
* Evaluated with [<code>CrossEncoderClassificationEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderClassificationEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| accuracy | 0.5704 |
| accuracy_threshold | -2.5469 |
| f1 | 0.64 |
| f1_threshold | -3.0625 |
| precision | 0.4934 |
| recall | 0.9104 |
| **average_precision** | **0.5199** |
#### Cross Encoder Reranking
* Datasets: `NanoQuoraRetrieval_R25`, `NanoMSMARCO_R25` and `NanoNQ_R25`
* Evaluated with [<code>CrossEncoderRerankingEvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderRerankingEvaluator) with these parameters:
```json
{
"at_k": 1,
"always_rerank_positives": true
}
```
| Metric | NanoQuoraRetrieval_R25 | NanoMSMARCO_R25 | NanoNQ_R25 |
|:-----------|:-----------------------|:---------------------|:---------------------|
| map | 0.2815 (-0.5489) | 0.1786 (-0.3092) | 0.1402 (-0.2798) |
| mrr@1 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) |
| **ndcg@1** | **0.1200 (-0.6800)** | **0.0400 (-0.3000)** | **0.0400 (-0.2000)** |
#### Cross Encoder Nano BEIR
* Dataset: `NanoBEIR_R25_mean`
* Evaluated with [<code>CrossEncoderNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/cross_encoder/evaluation.html#sentence_transformers.cross_encoder.evaluation.CrossEncoderNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"QuoraRetrieval",
"MSMARCO",
"NQ"
],
"rerank_k": 25,
"at_k": 1,
"always_rerank_positives": true
}
```
| Metric | Value |
|:-----------|:---------------------|
| map | 0.2001 (-0.3793) |
| mrr@1 | 0.0667 (-0.3933) |
| **ndcg@1** | **0.0667 (-0.3933)** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### LangCache Sentence Pairs (subsets=['all'], train+val=True)
* Dataset: [LangCache Sentence Pairs (subsets=['all'], train+val=True)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v3)
* Size: 40,015,318 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 27 characters</li><li>mean: 110.99 characters</li><li>max: 199 characters</li></ul> | <ul><li>min: 29 characters</li><li>mean: 110.74 characters</li><li>max: 191 characters</li></ul> | <ul><li>min: 0.25</li><li>mean: 0.49</li><li>max: 0.75</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| <code>The film was remade in Telugu with the same name in 1981 by Chandra Mohan , Jayasudha , Chakravarthy and S. P. Balasubrahmanyam starring K. Vasu .</code> | <code>The film was written in Telugu with the same name in 1981 by Chandra Mohan , Jayasudha , Chakravarthy and S. P. Balasubrahmanyam with K. Vasu .</code> | <code>0.75</code> |
| <code>The Cauchy stress tensor has therefore the following form</code> | <code>Therefore , the cauchy tensor has the following form</code> | <code>0.75</code> |
| <code>John Jairo Culma , sometimes spelled as Jhon Jairo Culma ( born 17 March 1981 ) is a Colombian footballer .</code> | <code>John Jairo Culma , sometimes known as Jhon Jairo Culma ( born March 17 , 1981 ) , is a Colombian footballer .</code> | <code>0.75</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": 0.1028856709599495
}
```
### Evaluation Dataset
#### LangCache Sentence Pairs (split=test)
* Dataset: [LangCache Sentence Pairs (split=test)](https://huggingface.co/datasets/aditeyabaral-redis/langcache-sentencepairs-v3)
* Size: 74,265 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:-------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 27 characters</li><li>mean: 112.72 characters</li><li>max: 197 characters</li></ul> | <ul><li>min: 27 characters</li><li>mean: 112.54 characters</li><li>max: 198 characters</li></ul> | <ul><li>0: ~50.30%</li><li>1: ~49.70%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|:---------------|
| <code>The newer Punts are still very much in existence today and race in the same fleets as the older boats .</code> | <code>The newer punts are still very much in existence today and run in the same fleets as the older boats .</code> | <code>1</code> |
| <code>Turner Valley , was at the Turner Valley Bar N Ranch Airport , southwest of the Turner Valley Bar N Ranch , Alberta , Canada .</code> | <code>Turner Valley Bar N Ranch Airport , , was located at Turner Valley Bar N Ranch , southwest of Turner Valley , Alberta , Canada .</code> | <code>0</code> |
| <code>After losing his second election , he resigned as opposition leader and was replaced by Geoff Pearsall .</code> | <code>Max Bingham resigned as opposition leader after losing his second election , and was replaced by Geoff Pearsall .</code> | <code>1</code> |
* Loss: [<code>BinaryCrossEntropyLoss</code>](https://sbert.net/docs/package_reference/cross_encoder/losses.html#binarycrossentropyloss) with these parameters:
```json
{
"activation_fn": "torch.nn.modules.linear.Identity",
"pos_weight": 0.1028856709599495
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `learning_rate`: 0.001
- `weight_decay`: 0.001
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
- `optim`: adamw_torch
- `ddp_find_unused_parameters`: False
- `push_to_hub`: True
- `hub_model_id`: redis/langcache-reranker-v2-tiny-bce-label-smoothing-0.5
- `eval_on_start`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 48
- `per_device_eval_batch_size`: 48
- `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`: 0.001
- `weight_decay`: 0.001
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `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`: 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`: True
- `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`: True
- `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
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: False
- `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`: True
- `resume_from_checkpoint`: None
- `hub_model_id`: redis/langcache-reranker-v2-tiny-bce-label-smoothing-0.5
- `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`: True
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: True
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | test_cls_average_precision | NanoQuoraRetrieval_R25_ndcg@1 | NanoMSMARCO_R25_ndcg@1 | NanoNQ_R25_ndcg@1 | NanoBEIR_R25_mean_ndcg@1 |
|:----------:|:-----------:|:-------------:|:---------------:|:--------------------------:|:-----------------------------:|:----------------------:|:--------------------:|:------------------------:|
| 0 | 0 | - | 2.4926 | 0.5444 | 0.9000 (+0.1000) | 0.4200 (+0.0800) | 0.5067 (+0.2667) | 0.6089 (+0.1489) |
| 0.0048 | 1000 | 0.9793 | - | - | - | - | - | - |
| 0.0096 | 2000 | 0.6215 | - | - | - | - | - | - |
| 0.0144 | 3000 | 0.3612 | - | - | - | - | - | - |
| 0.0192 | 4000 | 0.2163 | - | - | - | - | - | - |
| 0.0240 | 5000 | 0.2023 | - | - | - | - | - | - |
| 0.0288 | 6000 | 0.1985 | - | - | - | - | - | - |
| 0.0336 | 7000 | 0.1959 | - | - | - | - | - | - |
| 0.0384 | 8000 | 0.1939 | - | - | - | - | - | - |
| 0.0432 | 9000 | 0.1924 | - | - | - | - | - | - |
| 0.0480 | 10000 | 0.1911 | - | - | - | - | - | - |
| 0.0528 | 11000 | 0.1903 | - | - | - | - | - | - |
| 0.0576 | 12000 | 0.1896 | - | - | - | - | - | - |
| 0.0624 | 13000 | 0.1891 | - | - | - | - | - | - |
| 0.0672 | 14000 | 0.1886 | - | - | - | - | - | - |
| 0.0720 | 15000 | 0.1879 | - | - | - | - | - | - |
| 0.0768 | 16000 | 0.1876 | - | - | - | - | - | - |
| 0.0816 | 17000 | 0.1873 | - | - | - | - | - | - |
| 0.0864 | 18000 | 0.187 | - | - | - | - | - | - |
| 0.0912 | 19000 | 0.1867 | - | - | - | - | - | - |
| 0.0960 | 20000 | 0.1865 | 0.1930 | 0.5160 | 0.6400 (-0.1600) | 0.2800 (-0.0600) | 0.2200 (-0.0200) | 0.3800 (-0.0800) |
| 0.1008 | 21000 | 0.1864 | - | - | - | - | - | - |
| 0.1056 | 22000 | 0.1861 | - | - | - | - | - | - |
| 0.1104 | 23000 | 0.1859 | - | - | - | - | - | - |
| 0.1152 | 24000 | 0.1858 | - | - | - | - | - | - |
| 0.1200 | 25000 | 0.1857 | - | - | - | - | - | - |
| 0.1248 | 26000 | 0.1856 | - | - | - | - | - | - |
| 0.1296 | 27000 | 0.1856 | - | - | - | - | - | - |
| 0.1343 | 28000 | 0.1854 | - | - | - | - | - | - |
| 0.1391 | 29000 | 0.1853 | - | - | - | - | - | - |
| 0.1439 | 30000 | 0.1853 | - | - | - | - | - | - |
| 0.1487 | 31000 | 0.1852 | - | - | - | - | - | - |
| 0.1535 | 32000 | 0.1851 | - | - | - | - | - | - |
| 0.1583 | 33000 | 0.185 | - | - | - | - | - | - |
| 0.1631 | 34000 | 0.185 | - | - | - | - | - | - |
| 0.1679 | 35000 | 0.1849 | - | - | - | - | - | - |
| 0.1727 | 36000 | 0.1847 | - | - | - | - | - | - |
| 0.1775 | 37000 | 0.1847 | - | - | - | - | - | - |
| 0.1823 | 38000 | 0.1847 | - | - | - | - | - | - |
| 0.1871 | 39000 | 0.1845 | - | - | - | - | - | - |
| 0.1919 | 40000 | 0.1845 | 0.1884 | 0.4755 | 0.1500 (-0.6500) | 0.0967 (-0.2433) | 0.0800 (-0.1600) | 0.1089 (-0.3511) |
| 0.1967 | 41000 | 0.1844 | - | - | - | - | - | - |
| 0.2015 | 42000 | 0.1843 | - | - | - | - | - | - |
| 0.2063 | 43000 | 0.1841 | - | - | - | - | - | - |
| 0.2111 | 44000 | 0.1841 | - | - | - | - | - | - |
| 0.2159 | 45000 | 0.1841 | - | - | - | - | - | - |
| 0.2207 | 46000 | 0.184 | - | - | - | - | - | - |
| 0.2255 | 47000 | 0.1838 | - | - | - | - | - | - |
| 0.2303 | 48000 | 0.1838 | - | - | - | - | - | - |
| 0.2351 | 49000 | 0.1836 | - | - | - | - | - | - |
| 0.2399 | 50000 | 0.1835 | - | - | - | - | - | - |
| 0.2447 | 51000 | 0.1833 | - | - | - | - | - | - |
| 0.2495 | 52000 | 0.1833 | - | - | - | - | - | - |
| 0.2543 | 53000 | 0.1832 | - | - | - | - | - | - |
| 0.2591 | 54000 | 0.1831 | - | - | - | - | - | - |
| 0.2639 | 55000 | 0.1828 | - | - | - | - | - | - |
| 0.2687 | 56000 | 0.1826 | - | - | - | - | - | - |
| 0.2735 | 57000 | 0.1825 | - | - | - | - | - | - |
| 0.2783 | 58000 | 0.1822 | - | - | - | - | - | - |
| 0.2831 | 59000 | 0.182 | - | - | - | - | - | - |
| 0.2879 | 60000 | 0.1817 | 0.1884 | 0.4378 | 0.0200 (-0.7800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0333 (-0.4267) |
| 0.2927 | 61000 | 0.1814 | - | - | - | - | - | - |
| 0.2975 | 62000 | 0.1812 | - | - | - | - | - | - |
| 0.3023 | 63000 | 0.181 | - | - | - | - | - | - |
| 0.3071 | 64000 | 0.1806 | - | - | - | - | - | - |
| 0.3119 | 65000 | 0.1803 | - | - | - | - | - | - |
| 0.3167 | 66000 | 0.1802 | - | - | - | - | - | - |
| 0.3215 | 67000 | 0.1799 | - | - | - | - | - | - |
| 0.3263 | 68000 | 0.1796 | - | - | - | - | - | - |
| 0.3311 | 69000 | 0.1794 | - | - | - | - | - | - |
| 0.3359 | 70000 | 0.1792 | - | - | - | - | - | - |
| 0.3407 | 71000 | 0.179 | - | - | - | - | - | - |
| 0.3455 | 72000 | 0.1788 | - | - | - | - | - | - |
| 0.3503 | 73000 | 0.1787 | - | - | - | - | - | - |
| 0.3551 | 74000 | 0.1785 | - | - | - | - | - | - |
| 0.3599 | 75000 | 0.1783 | - | - | - | - | - | - |
| 0.3647 | 76000 | 0.1782 | - | - | - | - | - | - |
| 0.3695 | 77000 | 0.1781 | - | - | - | - | - | - |
| 0.3743 | 78000 | 0.178 | - | - | - | - | - | - |
| 0.3791 | 79000 | 0.1779 | - | - | - | - | - | - |
| 0.3839 | 80000 | 0.1778 | 0.1825 | 0.4439 | 0.0800 (-0.7200) | 0.0000 (-0.3400) | 0.0100 (-0.2300) | 0.0300 (-0.4300) |
| 0.3887 | 81000 | 0.1778 | - | - | - | - | - | - |
| 0.3934 | 82000 | 0.1776 | - | - | - | - | - | - |
| 0.3982 | 83000 | 0.1776 | - | - | - | - | - | - |
| 0.4030 | 84000 | 0.1775 | - | - | - | - | - | - |
| 0.4078 | 85000 | 0.1775 | - | - | - | - | - | - |
| 0.4126 | 86000 | 0.1774 | - | - | - | - | - | - |
| 0.4174 | 87000 | 0.1774 | - | - | - | - | - | - |
| 0.4222 | 88000 | 0.1773 | - | - | - | - | - | - |
| 0.4270 | 89000 | 0.1773 | - | - | - | - | - | - |
| 0.4318 | 90000 | 0.1772 | - | - | - | - | - | - |
| 0.4366 | 91000 | 0.1772 | - | - | - | - | - | - |
| 0.4414 | 92000 | 0.1772 | - | - | - | - | - | - |
| 0.4462 | 93000 | 0.1772 | - | - | - | - | - | - |
| 0.4510 | 94000 | 0.177 | - | - | - | - | - | - |
| 0.4558 | 95000 | 0.177 | - | - | - | - | - | - |
| 0.4606 | 96000 | 0.177 | - | - | - | - | - | - |
| 0.4654 | 97000 | 0.177 | - | - | - | - | - | - |
| 0.4702 | 98000 | 0.1769 | - | - | - | - | - | - |
| 0.4750 | 99000 | 0.1769 | - | - | - | - | - | - |
| 0.4798 | 100000 | 0.1769 | 0.1776 | 0.4625 | 0.0800 (-0.7200) | 0.0600 (-0.2800) | 0.0000 (-0.2400) | 0.0467 (-0.4133) |
| 0.4846 | 101000 | 0.1769 | - | - | - | - | - | - |
| 0.4894 | 102000 | 0.1769 | - | - | - | - | - | - |
| 0.4942 | 103000 | 0.1769 | - | - | - | - | - | - |
| 0.4990 | 104000 | 0.1768 | - | - | - | - | - | - |
| 0.5038 | 105000 | 0.1768 | - | - | - | - | - | - |
| 0.5086 | 106000 | 0.1768 | - | - | - | - | - | - |
| 0.5134 | 107000 | 0.1768 | - | - | - | - | - | - |
| 0.5182 | 108000 | 0.1767 | - | - | - | - | - | - |
| 0.5230 | 109000 | 0.1766 | - | - | - | - | - | - |
| 0.5278 | 110000 | 0.1767 | - | - | - | - | - | - |
| 0.5326 | 111000 | 0.1767 | - | - | - | - | - | - |
| 0.5374 | 112000 | 0.1767 | - | - | - | - | - | - |
| 0.5422 | 113000 | 0.1766 | - | - | - | - | - | - |
| 0.5470 | 114000 | 0.1766 | - | - | - | - | - | - |
| 0.5518 | 115000 | 0.1766 | - | - | - | - | - | - |
| 0.5566 | 116000 | 0.1765 | - | - | - | - | - | - |
| 0.5614 | 117000 | 0.1765 | - | - | - | - | - | - |
| 0.5662 | 118000 | 0.1765 | - | - | - | - | - | - |
| 0.5710 | 119000 | 0.1765 | - | - | - | - | - | - |
| 0.5758 | 120000 | 0.1765 | 0.1719 | 0.4701 | 0.0600 (-0.7400) | 0.0400 (-0.3000) | 0.0200 (-0.2200) | 0.0400 (-0.4200) |
| 0.5806 | 121000 | 0.1766 | - | - | - | - | - | - |
| 0.5854 | 122000 | 0.1766 | - | - | - | - | - | - |
| 0.5902 | 123000 | 0.1765 | - | - | - | - | - | - |
| 0.5950 | 124000 | 0.1765 | - | - | - | - | - | - |
| 0.5998 | 125000 | 0.1764 | - | - | - | - | - | - |
| 0.6046 | 126000 | 0.1765 | - | - | - | - | - | - |
| 0.6094 | 127000 | 0.1764 | - | - | - | - | - | - |
| 0.6142 | 128000 | 0.1765 | - | - | - | - | - | - |
| 0.6190 | 129000 | 0.1764 | - | - | - | - | - | - |
| 0.6238 | 130000 | 0.1764 | - | - | - | - | - | - |
| 0.6286 | 131000 | 0.1765 | - | - | - | - | - | - |
| 0.6334 | 132000 | 0.1764 | - | - | - | - | - | - |
| 0.6382 | 133000 | 0.1764 | - | - | - | - | - | - |
| 0.6430 | 134000 | 0.1764 | - | - | - | - | - | - |
| 0.6478 | 135000 | 0.1764 | - | - | - | - | - | - |
| 0.6526 | 136000 | 0.1764 | - | - | - | - | - | - |
| 0.6573 | 137000 | 0.1763 | - | - | - | - | - | - |
| 0.6621 | 138000 | 0.1764 | - | - | - | - | - | - |
| 0.6669 | 139000 | 0.1763 | - | - | - | - | - | - |
| 0.6717 | 140000 | 0.1764 | 0.1749 | 0.4864 | 0.1000 (-0.7000) | 0.1000 (-0.2400) | 0.0200 (-0.2200) | 0.0733 (-0.3867) |
| 0.6765 | 141000 | 0.1763 | - | - | - | - | - | - |
| 0.6813 | 142000 | 0.1763 | - | - | - | - | - | - |
| 0.6861 | 143000 | 0.1763 | - | - | - | - | - | - |
| 0.6909 | 144000 | 0.1763 | - | - | - | - | - | - |
| 0.6957 | 145000 | 0.1763 | - | - | - | - | - | - |
| 0.7005 | 146000 | 0.1763 | - | - | - | - | - | - |
| 0.7053 | 147000 | 0.1763 | - | - | - | - | - | - |
| 0.7101 | 148000 | 0.1763 | - | - | - | - | - | - |
| 0.7149 | 149000 | 0.1762 | - | - | - | - | - | - |
| 0.7197 | 150000 | 0.1763 | - | - | - | - | - | - |
| 0.7245 | 151000 | 0.1763 | - | - | - | - | - | - |
| 0.7293 | 152000 | 0.1763 | - | - | - | - | - | - |
| 0.7341 | 153000 | 0.1762 | - | - | - | - | - | - |
| 0.7389 | 154000 | 0.1762 | - | - | - | - | - | - |
| 0.7437 | 155000 | 0.1762 | - | - | - | - | - | - |
| 0.7485 | 156000 | 0.1762 | - | - | - | - | - | - |
| 0.7533 | 157000 | 0.1762 | - | - | - | - | - | - |
| 0.7581 | 158000 | 0.1763 | - | - | - | - | - | - |
| 0.7629 | 159000 | 0.1762 | - | - | - | - | - | - |
| 0.7677 | 160000 | 0.1762 | 0.1704 | 0.4800 | 0.1100 (-0.6900) | 0.0600 (-0.2800) | 0.0600 (-0.1800) | 0.0767 (-0.3833) |
| 0.7725 | 161000 | 0.1763 | - | - | - | - | - | - |
| 0.7773 | 162000 | 0.1762 | - | - | - | - | - | - |
| 0.7821 | 163000 | 0.1762 | - | - | - | - | - | - |
| 0.7869 | 164000 | 0.1763 | - | - | - | - | - | - |
| 0.7917 | 165000 | 0.1762 | - | - | - | - | - | - |
| 0.7965 | 166000 | 0.1761 | - | - | - | - | - | - |
| 0.8013 | 167000 | 0.1762 | - | - | - | - | - | - |
| 0.8061 | 168000 | 0.1762 | - | - | - | - | - | - |
| 0.8109 | 169000 | 0.1762 | - | - | - | - | - | - |
| 0.8157 | 170000 | 0.1762 | - | - | - | - | - | - |
| 0.8205 | 171000 | 0.1762 | - | - | - | - | - | - |
| 0.8253 | 172000 | 0.1762 | - | - | - | - | - | - |
| 0.8301 | 173000 | 0.1762 | - | - | - | - | - | - |
| 0.8349 | 174000 | 0.1762 | - | - | - | - | - | - |
| 0.8397 | 175000 | 0.1762 | - | - | - | - | - | - |
| 0.8445 | 176000 | 0.1762 | - | - | - | - | - | - |
| 0.8493 | 177000 | 0.1762 | - | - | - | - | - | - |
| 0.8541 | 178000 | 0.1762 | - | - | - | - | - | - |
| 0.8589 | 179000 | 0.1761 | - | - | - | - | - | - |
| 0.8637 | 180000 | 0.1761 | 0.1696 | 0.4907 | 0.1000 (-0.7000) | 0.0400 (-0.3000) | 0.0800 (-0.1600) | 0.0733 (-0.3867) |
| 0.8685 | 181000 | 0.1762 | - | - | - | - | - | - |
| 0.8733 | 182000 | 0.1762 | - | - | - | - | - | - |
| 0.8781 | 183000 | 0.1761 | - | - | - | - | - | - |
| 0.8829 | 184000 | 0.1762 | - | - | - | - | - | - |
| 0.8877 | 185000 | 0.1762 | - | - | - | - | - | - |
| 0.8925 | 186000 | 0.1761 | - | - | - | - | - | - |
| 0.8973 | 187000 | 0.1761 | - | - | - | - | - | - |
| 0.9021 | 188000 | 0.1762 | - | - | - | - | - | - |
| 0.9069 | 189000 | 0.1761 | - | - | - | - | - | - |
| 0.9117 | 190000 | 0.1761 | - | - | - | - | - | - |
| 0.9164 | 191000 | 0.1762 | - | - | - | - | - | - |
| 0.9212 | 192000 | 0.1761 | - | - | - | - | - | - |
| 0.9260 | 193000 | 0.1762 | - | - | - | - | - | - |
| 0.9308 | 194000 | 0.1761 | - | - | - | - | - | - |
| 0.9356 | 195000 | 0.1761 | - | - | - | - | - | - |
| 0.9404 | 196000 | 0.1761 | - | - | - | - | - | - |
| 0.9452 | 197000 | 0.1761 | - | - | - | - | - | - |
| 0.9500 | 198000 | 0.1762 | - | - | - | - | - | - |
| 0.9548 | 199000 | 0.1761 | - | - | - | - | - | - |
| 0.9596 | 200000 | 0.1761 | 0.1681 | 0.4989 | 0.1600 (-0.6400) | 0.0200 (-0.3200) | 0.0400 (-0.2000) | 0.0733 (-0.3867) |
| 0.9644 | 201000 | 0.1761 | - | - | - | - | - | - |
| 0.9692 | 202000 | 0.1762 | - | - | - | - | - | - |
| 0.9740 | 203000 | 0.1761 | - | - | - | - | - | - |
| 0.9788 | 204000 | 0.1761 | - | - | - | - | - | - |
| 0.9836 | 205000 | 0.1761 | - | - | - | - | - | - |
| 0.9884 | 206000 | 0.1761 | - | - | - | - | - | - |
| 0.9932 | 207000 | 0.1761 | - | - | - | - | - | - |
| 0.9980 | 208000 | 0.1761 | - | - | - | - | - | - |
| 1.0028 | 209000 | 0.1761 | - | - | - | - | - | - |
| 1.0076 | 210000 | 0.1761 | - | - | - | - | - | - |
| 1.0124 | 211000 | 0.1761 | - | - | - | - | - | - |
| 1.0172 | 212000 | 0.1761 | - | - | - | - | - | - |
| 1.0220 | 213000 | 0.1761 | - | - | - | - | - | - |
| 1.0268 | 214000 | 0.1761 | - | - | - | - | - | - |
| 1.0316 | 215000 | 0.1761 | - | - | - | - | - | - |
| 1.0364 | 216000 | 0.1761 | - | - | - | - | - | - |
| 1.0412 | 217000 | 0.1761 | - | - | - | - | - | - |
| 1.0460 | 218000 | 0.1761 | - | - | - | - | - | - |
| 1.0508 | 219000 | 0.1761 | - | - | - | - | - | - |
| 1.0556 | 220000 | 0.1762 | 0.1666 | 0.5001 | 0.1600 (-0.6400) | 0.1000 (-0.2400) | 0.0200 (-0.2200) | 0.0933 (-0.3667) |
| 1.0604 | 221000 | 0.1761 | - | - | - | - | - | - |
| 1.0652 | 222000 | 0.176 | - | - | - | - | - | - |
| 1.0700 | 223000 | 0.1761 | - | - | - | - | - | - |
| 1.0748 | 224000 | 0.1761 | - | - | - | - | - | - |
| 1.0796 | 225000 | 0.1761 | - | - | - | - | - | - |
| 1.0844 | 226000 | 0.176 | - | - | - | - | - | - |
| 1.0892 | 227000 | 0.1761 | - | - | - | - | - | - |
| 1.0940 | 228000 | 0.176 | - | - | - | - | - | - |
| 1.0988 | 229000 | 0.1761 | - | - | - | - | - | - |
| 1.1036 | 230000 | 0.176 | - | - | - | - | - | - |
| 1.1084 | 231000 | 0.1761 | - | - | - | - | - | - |
| 1.1132 | 232000 | 0.1761 | - | - | - | - | - | - |
| 1.1180 | 233000 | 0.176 | - | - | - | - | - | - |
| 1.1228 | 234000 | 0.1761 | - | - | - | - | - | - |
| 1.1276 | 235000 | 0.1761 | - | - | - | - | - | - |
| 1.1324 | 236000 | 0.1761 | - | - | - | - | - | - |
| 1.1372 | 237000 | 0.1761 | - | - | - | - | - | - |
| 1.1420 | 238000 | 0.1761 | - | - | - | - | - | - |
| 1.1468 | 239000 | 0.176 | - | - | - | - | - | - |
| 1.1516 | 240000 | 0.176 | 0.1679 | 0.4962 | 0.1400 (-0.6600) | 0.0500 (-0.2900) | 0.0400 (-0.2000) | 0.0767 (-0.3833) |
| 1.1564 | 241000 | 0.1761 | - | - | - | - | - | - |
| 1.1612 | 242000 | 0.176 | - | - | - | - | - | - |
| 1.1660 | 243000 | 0.176 | - | - | - | - | - | - |
| 1.1708 | 244000 | 0.1761 | - | - | - | - | - | - |
| 1.1756 | 245000 | 0.176 | - | - | - | - | - | - |
| 1.1803 | 246000 | 0.176 | - | - | - | - | - | - |
| 1.1851 | 247000 | 0.176 | - | - | - | - | - | - |
| 1.1899 | 248000 | 0.176 | - | - | - | - | - | - |
| 1.1947 | 249000 | 0.176 | - | - | - | - | - | - |
| 1.1995 | 250000 | 0.1761 | - | - | - | - | - | - |
| 1.2043 | 251000 | 0.176 | - | - | - | - | - | - |
| 1.2091 | 252000 | 0.176 | - | - | - | - | - | - |
| 1.2139 | 253000 | 0.176 | - | - | - | - | - | - |
| 1.2187 | 254000 | 0.176 | - | - | - | - | - | - |
| 1.2235 | 255000 | 0.1761 | - | - | - | - | - | - |
| 1.2283 | 256000 | 0.1761 | - | - | - | - | - | - |
| 1.2331 | 257000 | 0.176 | - | - | - | - | - | - |
| 1.2379 | 258000 | 0.176 | - | - | - | - | - | - |
| 1.2427 | 259000 | 0.176 | - | - | - | - | - | - |
| 1.2475 | 260000 | 0.176 | 0.1683 | 0.5059 | 0.1400 (-0.6600) | 0.0800 (-0.2600) | 0.0400 (-0.2000) | 0.0867 (-0.3733) |
| 1.2523 | 261000 | 0.1761 | - | - | - | - | - | - |
| 1.2571 | 262000 | 0.176 | - | - | - | - | - | - |
| 1.2619 | 263000 | 0.176 | - | - | - | - | - | - |
| 1.2667 | 264000 | 0.176 | - | - | - | - | - | - |
| 1.2715 | 265000 | 0.1761 | - | - | - | - | - | - |
| 1.2763 | 266000 | 0.176 | - | - | - | - | - | - |
| 1.2811 | 267000 | 0.176 | - | - | - | - | - | - |
| 1.2859 | 268000 | 0.176 | - | - | - | - | - | - |
| 1.2907 | 269000 | 0.176 | - | - | - | - | - | - |
| 1.2955 | 270000 | 0.1759 | - | - | - | - | - | - |
| 1.3003 | 271000 | 0.1761 | - | - | - | - | - | - |
| 1.3051 | 272000 | 0.176 | - | - | - | - | - | - |
| 1.3099 | 273000 | 0.1758 | - | - | - | - | - | - |
| 1.3147 | 274000 | 0.1761 | - | - | - | - | - | - |
| 1.3195 | 275000 | 0.176 | - | - | - | - | - | - |
| 1.3243 | 276000 | 0.176 | - | - | - | - | - | - |
| 1.3291 | 277000 | 0.1761 | - | - | - | - | - | - |
| 1.3339 | 278000 | 0.1759 | - | - | - | - | - | - |
| 1.3387 | 279000 | 0.176 | - | - | - | - | - | - |
| 1.3435 | 280000 | 0.1759 | 0.1656 | 0.5091 | 0.1400 (-0.6600) | 0.0600 (-0.2800) | 0.0500 (-0.1900) | 0.0833 (-0.3767) |
| 1.3483 | 281000 | 0.176 | - | - | - | - | - | - |
| 1.3531 | 282000 | 0.176 | - | - | - | - | - | - |
| 1.3579 | 283000 | 0.1759 | - | - | - | - | - | - |
| 1.3627 | 284000 | 0.176 | - | - | - | - | - | - |
| 1.3675 | 285000 | 0.176 | - | - | - | - | - | - |
| 1.3723 | 286000 | 0.1759 | - | - | - | - | - | - |
| 1.3771 | 287000 | 0.1759 | - | - | - | - | - | - |
| 1.3819 | 288000 | 0.176 | - | - | - | - | - | - |
| 1.3867 | 289000 | 0.1759 | - | - | - | - | - | - |
| 1.3915 | 290000 | 0.1759 | - | - | - | - | - | - |
| 1.3963 | 291000 | 0.176 | - | - | - | - | - | - |
| 1.4011 | 292000 | 0.1759 | - | - | - | - | - | - |
| 1.4059 | 293000 | 0.1759 | - | - | - | - | - | - |
| 1.4107 | 294000 | 0.1759 | - | - | - | - | - | - |
| 1.4155 | 295000 | 0.1759 | - | - | - | - | - | - |
| 1.4203 | 296000 | 0.1759 | - | - | - | - | - | - |
| 1.4251 | 297000 | 0.1759 | - | - | - | - | - | - |
| 1.4299 | 298000 | 0.1759 | - | - | - | - | - | - |
| 1.4347 | 299000 | 0.1759 | - | - | - | - | - | - |
| 1.4394 | 300000 | 0.1759 | 0.1669 | 0.4971 | 0.1000 (-0.7000) | 0.0500 (-0.2900) | 0.0400 (-0.2000) | 0.0633 (-0.3967) |
| 1.4442 | 301000 | 0.176 | - | - | - | - | - | - |
| 1.4490 | 302000 | 0.1759 | - | - | - | - | - | - |
| 1.4538 | 303000 | 0.1758 | - | - | - | - | - | - |
| 1.4586 | 304000 | 0.176 | - | - | - | - | - | - |
| 1.4634 | 305000 | 0.1759 | - | - | - | - | - | - |
| 1.4682 | 306000 | 0.1759 | - | - | - | - | - | - |
| 1.4730 | 307000 | 0.1759 | - | - | - | - | - | - |
| 1.4778 | 308000 | 0.1759 | - | - | - | - | - | - |
| 1.4826 | 309000 | 0.1759 | - | - | - | - | - | - |
| 1.4874 | 310000 | 0.176 | - | - | - | - | - | - |
| 1.4922 | 311000 | 0.1759 | - | - | - | - | - | - |
| 1.4970 | 312000 | 0.1759 | - | - | - | - | - | - |
| 1.5018 | 313000 | 0.1759 | - | - | - | - | - | - |
| 1.5066 | 314000 | 0.1758 | - | - | - | - | - | - |
| 1.5114 | 315000 | 0.1759 | - | - | - | - | - | - |
| 1.5162 | 316000 | 0.1759 | - | - | - | - | - | - |
| 1.5210 | 317000 | 0.1759 | - | - | - | - | - | - |
| 1.5258 | 318000 | 0.1759 | - | - | - | - | - | - |
| 1.5306 | 319000 | 0.176 | - | - | - | - | - | - |
| 1.5354 | 320000 | 0.1758 | 0.1658 | 0.5066 | 0.1200 (-0.6800) | 0.0500 (-0.2900) | 0.0400 (-0.2000) | 0.0700 (-0.3900) |
| 1.5402 | 321000 | 0.1759 | - | - | - | - | - | - |
| 1.5450 | 322000 | 0.1759 | - | - | - | - | - | - |
| 1.5498 | 323000 | 0.1759 | - | - | - | - | - | - |
| 1.5546 | 324000 | 0.1759 | - | - | - | - | - | - |
| 1.5594 | 325000 | 0.1758 | - | - | - | - | - | - |
| 1.5642 | 326000 | 0.1759 | - | - | - | - | - | - |
| 1.5690 | 327000 | 0.1759 | - | - | - | - | - | - |
| 1.5738 | 328000 | 0.1759 | - | - | - | - | - | - |
| 1.5786 | 329000 | 0.1759 | - | - | - | - | - | - |
| 1.5834 | 330000 | 0.1759 | - | - | - | - | - | - |
| 1.5882 | 331000 | 0.1759 | - | - | - | - | - | - |
| 1.5930 | 332000 | 0.1759 | - | - | - | - | - | - |
| 1.5978 | 333000 | 0.1759 | - | - | - | - | - | - |
| 1.6026 | 334000 | 0.1759 | - | - | - | - | - | - |
| 1.6074 | 335000 | 0.1759 | - | - | - | - | - | - |
| 1.6122 | 336000 | 0.1759 | - | - | - | - | - | - |
| 1.6170 | 337000 | 0.1759 | - | - | - | - | - | - |
| 1.6218 | 338000 | 0.1759 | - | - | - | - | - | - |
| 1.6266 | 339000 | 0.1759 | - | - | - | - | - | - |
| 1.6314 | 340000 | 0.1759 | 0.1668 | 0.5066 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0200 (-0.2200) | 0.0600 (-0.4000) |
| 1.6362 | 341000 | 0.1759 | - | - | - | - | - | - |
| 1.6410 | 342000 | 0.1759 | - | - | - | - | - | - |
| 1.6458 | 343000 | 0.1758 | - | - | - | - | - | - |
| 1.6506 | 344000 | 0.1759 | - | - | - | - | - | - |
| 1.6554 | 345000 | 0.1759 | - | - | - | - | - | - |
| 1.6602 | 346000 | 0.1759 | - | - | - | - | - | - |
| 1.6650 | 347000 | 0.1759 | - | - | - | - | - | - |
| 1.6698 | 348000 | 0.1759 | - | - | - | - | - | - |
| 1.6746 | 349000 | 0.1759 | - | - | - | - | - | - |
| 1.6794 | 350000 | 0.1759 | - | - | - | - | - | - |
| 1.6842 | 351000 | 0.1758 | - | - | - | - | - | - |
| 1.6890 | 352000 | 0.1759 | - | - | - | - | - | - |
| 1.6938 | 353000 | 0.1759 | - | - | - | - | - | - |
| 1.6986 | 354000 | 0.1759 | - | - | - | - | - | - |
| 1.7033 | 355000 | 0.1759 | - | - | - | - | - | - |
| 1.7081 | 356000 | 0.1759 | - | - | - | - | - | - |
| 1.7129 | 357000 | 0.1759 | - | - | - | - | - | - |
| 1.7177 | 358000 | 0.1759 | - | - | - | - | - | - |
| 1.7225 | 359000 | 0.1759 | - | - | - | - | - | - |
| 1.7273 | 360000 | 0.1759 | 0.1652 | 0.5137 | 0.1200 (-0.6800) | 0.0300 (-0.3100) | 0.0400 (-0.2000) | 0.0633 (-0.3967) |
| 1.7321 | 361000 | 0.1758 | - | - | - | - | - | - |
| 1.7369 | 362000 | 0.1758 | - | - | - | - | - | - |
| 1.7417 | 363000 | 0.1758 | - | - | - | - | - | - |
| 1.7465 | 364000 | 0.1758 | - | - | - | - | - | - |
| 1.7513 | 365000 | 0.1759 | - | - | - | - | - | - |
| 1.7561 | 366000 | 0.176 | - | - | - | - | - | - |
| 1.7609 | 367000 | 0.1758 | - | - | - | - | - | - |
| 1.7657 | 368000 | 0.1759 | - | - | - | - | - | - |
| 1.7705 | 369000 | 0.1759 | - | - | - | - | - | - |
| 1.7753 | 370000 | 0.1759 | - | - | - | - | - | - |
| 1.7801 | 371000 | 0.1759 | - | - | - | - | - | - |
| 1.7849 | 372000 | 0.1759 | - | - | - | - | - | - |
| 1.7897 | 373000 | 0.1759 | - | - | - | - | - | - |
| 1.7945 | 374000 | 0.1759 | - | - | - | - | - | - |
| 1.7993 | 375000 | 0.1758 | - | - | - | - | - | - |
| 1.8041 | 376000 | 0.1759 | - | - | - | - | - | - |
| 1.8089 | 377000 | 0.1759 | - | - | - | - | - | - |
| 1.8137 | 378000 | 0.1758 | - | - | - | - | - | - |
| 1.8185 | 379000 | 0.1759 | - | - | - | - | - | - |
| 1.8233 | 380000 | 0.1758 | 0.1653 | 0.5147 | 0.1000 (-0.7000) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0600 (-0.4000) |
| 1.8281 | 381000 | 0.1759 | - | - | - | - | - | - |
| 1.8329 | 382000 | 0.1759 | - | - | - | - | - | - |
| 1.8377 | 383000 | 0.1759 | - | - | - | - | - | - |
| 1.8425 | 384000 | 0.1759 | - | - | - | - | - | - |
| 1.8473 | 385000 | 0.1759 | - | - | - | - | - | - |
| 1.8521 | 386000 | 0.1759 | - | - | - | - | - | - |
| 1.8569 | 387000 | 0.1758 | - | - | - | - | - | - |
| 1.8617 | 388000 | 0.1759 | - | - | - | - | - | - |
| 1.8665 | 389000 | 0.1759 | - | - | - | - | - | - |
| 1.8713 | 390000 | 0.1759 | - | - | - | - | - | - |
| 1.8761 | 391000 | 0.1758 | - | - | - | - | - | - |
| 1.8809 | 392000 | 0.1758 | - | - | - | - | - | - |
| 1.8857 | 393000 | 0.1759 | - | - | - | - | - | - |
| 1.8905 | 394000 | 0.1759 | - | - | - | - | - | - |
| 1.8953 | 395000 | 0.1758 | - | - | - | - | - | - |
| 1.9001 | 396000 | 0.1759 | - | - | - | - | - | - |
| 1.9049 | 397000 | 0.176 | - | - | - | - | - | - |
| 1.9097 | 398000 | 0.1759 | - | - | - | - | - | - |
| 1.9145 | 399000 | 0.1759 | - | - | - | - | - | - |
| 1.9193 | 400000 | 0.1759 | 0.1658 | 0.5055 | 0.1400 (-0.6600) | 0.0700 (-0.2700) | 0.0400 (-0.2000) | 0.0833 (-0.3767) |
| 1.9241 | 401000 | 0.1759 | - | - | - | - | - | - |
| 1.9289 | 402000 | 0.1759 | - | - | - | - | - | - |
| 1.9337 | 403000 | 0.1759 | - | - | - | - | - | - |
| 1.9385 | 404000 | 0.1759 | - | - | - | - | - | - |
| 1.9433 | 405000 | 0.1759 | - | - | - | - | - | - |
| 1.9481 | 406000 | 0.1759 | - | - | - | - | - | - |
| 1.9529 | 407000 | 0.1758 | - | - | - | - | - | - |
| 1.9577 | 408000 | 0.1759 | - | - | - | - | - | - |
| 1.9624 | 409000 | 0.1759 | - | - | - | - | - | - |
| 1.9672 | 410000 | 0.1759 | - | - | - | - | - | - |
| 1.9720 | 411000 | 0.1759 | - | - | - | - | - | - |
| 1.9768 | 412000 | 0.1759 | - | - | - | - | - | - |
| 1.9816 | 413000 | 0.1759 | - | - | - | - | - | - |
| 1.9864 | 414000 | 0.1758 | - | - | - | - | - | - |
| 1.9912 | 415000 | 0.1759 | - | - | - | - | - | - |
| 1.9960 | 416000 | 0.1758 | - | - | - | - | - | - |
| 2.0008 | 417000 | 0.1758 | - | - | - | - | - | - |
| 2.0056 | 418000 | 0.1758 | - | - | - | - | - | - |
| 2.0104 | 419000 | 0.1759 | - | - | - | - | - | - |
| 2.0152 | 420000 | 0.1759 | 0.1649 | 0.5218 | 0.1800 (-0.6200) | 0.0800 (-0.2600) | 0.0400 (-0.2000) | 0.1000 (-0.3600) |
| 2.0200 | 421000 | 0.1759 | - | - | - | - | - | - |
| 2.0248 | 422000 | 0.1759 | - | - | - | - | - | - |
| 2.0296 | 423000 | 0.1759 | - | - | - | - | - | - |
| 2.0344 | 424000 | 0.1759 | - | - | - | - | - | - |
| 2.0392 | 425000 | 0.1759 | - | - | - | - | - | - |
| 2.0440 | 426000 | 0.1759 | - | - | - | - | - | - |
| 2.0488 | 427000 | 0.1758 | - | - | - | - | - | - |
| 2.0536 | 428000 | 0.1759 | - | - | - | - | - | - |
| 2.0584 | 429000 | 0.176 | - | - | - | - | - | - |
| 2.0632 | 430000 | 0.1759 | - | - | - | - | - | - |
| 2.0680 | 431000 | 0.1758 | - | - | - | - | - | - |
| 2.0728 | 432000 | 0.1759 | - | - | - | - | - | - |
| 2.0776 | 433000 | 0.1758 | - | - | - | - | - | - |
| 2.0824 | 434000 | 0.1759 | - | - | - | - | - | - |
| 2.0872 | 435000 | 0.1759 | - | - | - | - | - | - |
| 2.0920 | 436000 | 0.1758 | - | - | - | - | - | - |
| 2.0968 | 437000 | 0.176 | - | - | - | - | - | - |
| 2.1016 | 438000 | 0.1759 | - | - | - | - | - | - |
| 2.1064 | 439000 | 0.1759 | - | - | - | - | - | - |
| 2.1112 | 440000 | 0.1759 | 0.1648 | 0.5162 | 0.1800 (-0.6200) | 0.0800 (-0.2600) | 0.0400 (-0.2000) | 0.1000 (-0.3600) |
| 2.1160 | 441000 | 0.1759 | - | - | - | - | - | - |
| 2.1208 | 442000 | 0.1759 | - | - | - | - | - | - |
| 2.1256 | 443000 | 0.1759 | - | - | - | - | - | - |
| 2.1304 | 444000 | 0.1759 | - | - | - | - | - | - |
| 2.1352 | 445000 | 0.1759 | - | - | - | - | - | - |
| 2.1400 | 446000 | 0.176 | - | - | - | - | - | - |
| 2.1448 | 447000 | 0.1759 | - | - | - | - | - | - |
| 2.1496 | 448000 | 0.1758 | - | - | - | - | - | - |
| 2.1544 | 449000 | 0.1759 | - | - | - | - | - | - |
| 2.1592 | 450000 | 0.1758 | - | - | - | - | - | - |
| 2.1640 | 451000 | 0.1758 | - | - | - | - | - | - |
| 2.1688 | 452000 | 0.1759 | - | - | - | - | - | - |
| 2.1736 | 453000 | 0.1759 | - | - | - | - | - | - |
| 2.1784 | 454000 | 0.1759 | - | - | - | - | - | - |
| 2.1832 | 455000 | 0.1759 | - | - | - | - | - | - |
| 2.1880 | 456000 | 0.1759 | - | - | - | - | - | - |
| 2.1928 | 457000 | 0.1758 | - | - | - | - | - | - |
| 2.1976 | 458000 | 0.176 | - | - | - | - | - | - |
| 2.2024 | 459000 | 0.1758 | - | - | - | - | - | - |
| 2.2072 | 460000 | 0.1759 | 0.1650 | 0.5117 | 0.1500 (-0.6500) | 0.0600 (-0.2800) | 0.0400 (-0.2000) | 0.0833 (-0.3767) |
| 2.2120 | 461000 | 0.1759 | - | - | - | - | - | - |
| 2.2168 | 462000 | 0.1759 | - | - | - | - | - | - |
| 2.2216 | 463000 | 0.1759 | - | - | - | - | - | - |
| 2.2263 | 464000 | 0.1759 | - | - | - | - | - | - |
| 2.2311 | 465000 | 0.1758 | - | - | - | - | - | - |
| 2.2359 | 466000 | 0.1759 | - | - | - | - | - | - |
| 2.2407 | 467000 | 0.1758 | - | - | - | - | - | - |
| 2.2455 | 468000 | 0.1759 | - | - | - | - | - | - |
| 2.2503 | 469000 | 0.1759 | - | - | - | - | - | - |
| 2.2551 | 470000 | 0.1759 | - | - | - | - | - | - |
| 2.2599 | 471000 | 0.1759 | - | - | - | - | - | - |
| 2.2647 | 472000 | 0.1758 | - | - | - | - | - | - |
| 2.2695 | 473000 | 0.1759 | - | - | - | - | - | - |
| 2.2743 | 474000 | 0.1758 | - | - | - | - | - | - |
| 2.2791 | 475000 | 0.1759 | - | - | - | - | - | - |
| 2.2839 | 476000 | 0.1759 | - | - | - | - | - | - |
| 2.2887 | 477000 | 0.1758 | - | - | - | - | - | - |
| 2.2935 | 478000 | 0.1759 | - | - | - | - | - | - |
| 2.2983 | 479000 | 0.1759 | - | - | - | - | - | - |
| 2.3031 | 480000 | 0.176 | 0.1648 | 0.5137 | 0.1600 (-0.6400) | 0.0600 (-0.2800) | 0.0400 (-0.2000) | 0.0867 (-0.3733) |
| 2.3079 | 481000 | 0.1759 | - | - | - | - | - | - |
| 2.3127 | 482000 | 0.1759 | - | - | - | - | - | - |
| 2.3175 | 483000 | 0.1759 | - | - | - | - | - | - |
| 2.3223 | 484000 | 0.1759 | - | - | - | - | - | - |
| 2.3271 | 485000 | 0.1759 | - | - | - | - | - | - |
| 2.3319 | 486000 | 0.1759 | - | - | - | - | - | - |
| 2.3367 | 487000 | 0.1759 | - | - | - | - | - | - |
| 2.3415 | 488000 | 0.1758 | - | - | - | - | - | - |
| 2.3463 | 489000 | 0.1758 | - | - | - | - | - | - |
| 2.3511 | 490000 | 0.1758 | - | - | - | - | - | - |
| 2.3559 | 491000 | 0.1759 | - | - | - | - | - | - |
| 2.3607 | 492000 | 0.1758 | - | - | - | - | - | - |
| 2.3655 | 493000 | 0.1759 | - | - | - | - | - | - |
| 2.3703 | 494000 | 0.1758 | - | - | - | - | - | - |
| 2.3751 | 495000 | 0.1758 | - | - | - | - | - | - |
| 2.3799 | 496000 | 0.1758 | - | - | - | - | - | - |
| 2.3847 | 497000 | 0.1758 | - | - | - | - | - | - |
| 2.3895 | 498000 | 0.1758 | - | - | - | - | - | - |
| 2.3943 | 499000 | 0.1759 | - | - | - | - | - | - |
| 2.3991 | 500000 | 0.1758 | 0.1648 | 0.5102 | 0.1600 (-0.6400) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0800 (-0.3800) |
| 2.4039 | 501000 | 0.1758 | - | - | - | - | - | - |
| 2.4087 | 502000 | 0.1759 | - | - | - | - | - | - |
| 2.4135 | 503000 | 0.1758 | - | - | - | - | - | - |
| 2.4183 | 504000 | 0.1758 | - | - | - | - | - | - |
| 2.4231 | 505000 | 0.1758 | - | - | - | - | - | - |
| 2.4279 | 506000 | 0.1758 | - | - | - | - | - | - |
| 2.4327 | 507000 | 0.1758 | - | - | - | - | - | - |
| 2.4375 | 508000 | 0.1758 | - | - | - | - | - | - |
| 2.4423 | 509000 | 0.1758 | - | - | - | - | - | - |
| 2.4471 | 510000 | 0.1758 | - | - | - | - | - | - |
| 2.4519 | 511000 | 0.1757 | - | - | - | - | - | - |
| 2.4567 | 512000 | 0.1758 | - | - | - | - | - | - |
| 2.4615 | 513000 | 0.1759 | - | - | - | - | - | - |
| 2.4663 | 514000 | 0.1758 | - | - | - | - | - | - |
| 2.4711 | 515000 | 0.1758 | - | - | - | - | - | - |
| 2.4759 | 516000 | 0.1758 | - | - | - | - | - | - |
| 2.4807 | 517000 | 0.1759 | - | - | - | - | - | - |
| 2.4854 | 518000 | 0.1759 | - | - | - | - | - | - |
| 2.4902 | 519000 | 0.1758 | - | - | - | - | - | - |
| 2.4950 | 520000 | 0.1759 | 0.1646 | 0.5154 | 0.1600 (-0.6400) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0800 (-0.3800) |
| 2.4998 | 521000 | 0.1758 | - | - | - | - | - | - |
| 2.5046 | 522000 | 0.1758 | - | - | - | - | - | - |
| 2.5094 | 523000 | 0.1758 | - | - | - | - | - | - |
| 2.5142 | 524000 | 0.1759 | - | - | - | - | - | - |
| 2.5190 | 525000 | 0.1758 | - | - | - | - | - | - |
| 2.5238 | 526000 | 0.1758 | - | - | - | - | - | - |
| 2.5286 | 527000 | 0.1758 | - | - | - | - | - | - |
| 2.5334 | 528000 | 0.1758 | - | - | - | - | - | - |
| 2.5382 | 529000 | 0.1759 | - | - | - | - | - | - |
| 2.5430 | 530000 | 0.1758 | - | - | - | - | - | - |
| 2.5478 | 531000 | 0.1758 | - | - | - | - | - | - |
| 2.5526 | 532000 | 0.1758 | - | - | - | - | - | - |
| 2.5574 | 533000 | 0.1758 | - | - | - | - | - | - |
| 2.5622 | 534000 | 0.1758 | - | - | - | - | - | - |
| 2.5670 | 535000 | 0.1758 | - | - | - | - | - | - |
| 2.5718 | 536000 | 0.1758 | - | - | - | - | - | - |
| 2.5766 | 537000 | 0.1758 | - | - | - | - | - | - |
| 2.5814 | 538000 | 0.1758 | - | - | - | - | - | - |
| 2.5862 | 539000 | 0.1758 | - | - | - | - | - | - |
| 2.5910 | 540000 | 0.1759 | 0.1641 | 0.5201 | 0.1400 (-0.6600) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0733 (-0.3867) |
| 2.5958 | 541000 | 0.1758 | - | - | - | - | - | - |
| 2.6006 | 542000 | 0.1758 | - | - | - | - | - | - |
| 2.6054 | 543000 | 0.1759 | - | - | - | - | - | - |
| 2.6102 | 544000 | 0.1758 | - | - | - | - | - | - |
| 2.6150 | 545000 | 0.1759 | - | - | - | - | - | - |
| 2.6198 | 546000 | 0.1758 | - | - | - | - | - | - |
| 2.6246 | 547000 | 0.1758 | - | - | - | - | - | - |
| 2.6294 | 548000 | 0.1758 | - | - | - | - | - | - |
| 2.6342 | 549000 | 0.1758 | - | - | - | - | - | - |
| 2.6390 | 550000 | 0.1758 | - | - | - | - | - | - |
| 2.6438 | 551000 | 0.1758 | - | - | - | - | - | - |
| 2.6486 | 552000 | 0.1758 | - | - | - | - | - | - |
| 2.6534 | 553000 | 0.1759 | - | - | - | - | - | - |
| 2.6582 | 554000 | 0.1758 | - | - | - | - | - | - |
| 2.6630 | 555000 | 0.1758 | - | - | - | - | - | - |
| 2.6678 | 556000 | 0.1758 | - | - | - | - | - | - |
| 2.6726 | 557000 | 0.1758 | - | - | - | - | - | - |
| 2.6774 | 558000 | 0.1759 | - | - | - | - | - | - |
| 2.6822 | 559000 | 0.1759 | - | - | - | - | - | - |
| 2.6870 | 560000 | 0.1758 | 0.1647 | 0.5082 | 0.1400 (-0.6600) | 0.0200 (-0.3200) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 2.6918 | 561000 | 0.1758 | - | - | - | - | - | - |
| 2.6966 | 562000 | 0.1759 | - | - | - | - | - | - |
| 2.7014 | 563000 | 0.1759 | - | - | - | - | - | - |
| 2.7062 | 564000 | 0.1758 | - | - | - | - | - | - |
| 2.7110 | 565000 | 0.1759 | - | - | - | - | - | - |
| 2.7158 | 566000 | 0.1758 | - | - | - | - | - | - |
| 2.7206 | 567000 | 0.1759 | - | - | - | - | - | - |
| 2.7254 | 568000 | 0.1758 | - | - | - | - | - | - |
| 2.7302 | 569000 | 0.1759 | - | - | - | - | - | - |
| 2.7350 | 570000 | 0.1758 | - | - | - | - | - | - |
| 2.7398 | 571000 | 0.1758 | - | - | - | - | - | - |
| 2.7446 | 572000 | 0.1757 | - | - | - | - | - | - |
| 2.7493 | 573000 | 0.1758 | - | - | - | - | - | - |
| 2.7541 | 574000 | 0.1759 | - | - | - | - | - | - |
| 2.7589 | 575000 | 0.1758 | - | - | - | - | - | - |
| 2.7637 | 576000 | 0.1758 | - | - | - | - | - | - |
| 2.7685 | 577000 | 0.1758 | - | - | - | - | - | - |
| 2.7733 | 578000 | 0.1759 | - | - | - | - | - | - |
| 2.7781 | 579000 | 0.1758 | - | - | - | - | - | - |
| 2.7829 | 580000 | 0.1758 | 0.1643 | 0.5162 | 0.1400 (-0.6600) | 0.0600 (-0.2800) | 0.0400 (-0.2000) | 0.0800 (-0.3800) |
| 2.7877 | 581000 | 0.1759 | - | - | - | - | - | - |
| 2.7925 | 582000 | 0.1758 | - | - | - | - | - | - |
| 2.7973 | 583000 | 0.1758 | - | - | - | - | - | - |
| 2.8021 | 584000 | 0.1758 | - | - | - | - | - | - |
| 2.8069 | 585000 | 0.1759 | - | - | - | - | - | - |
| 2.8117 | 586000 | 0.1758 | - | - | - | - | - | - |
| 2.8165 | 587000 | 0.1758 | - | - | - | - | - | - |
| 2.8213 | 588000 | 0.1758 | - | - | - | - | - | - |
| 2.8261 | 589000 | 0.1758 | - | - | - | - | - | - |
| 2.8309 | 590000 | 0.1758 | - | - | - | - | - | - |
| 2.8357 | 591000 | 0.1759 | - | - | - | - | - | - |
| 2.8405 | 592000 | 0.1758 | - | - | - | - | - | - |
| 2.8453 | 593000 | 0.1759 | - | - | - | - | - | - |
| 2.8501 | 594000 | 0.1759 | - | - | - | - | - | - |
| 2.8549 | 595000 | 0.1758 | - | - | - | - | - | - |
| 2.8597 | 596000 | 0.1757 | - | - | - | - | - | - |
| 2.8645 | 597000 | 0.1758 | - | - | - | - | - | - |
| 2.8693 | 598000 | 0.1759 | - | - | - | - | - | - |
| 2.8741 | 599000 | 0.1759 | - | - | - | - | - | - |
| 2.8789 | 600000 | 0.1758 | 0.1643 | 0.5172 | 0.1300 (-0.6700) | 0.0700 (-0.2700) | 0.0400 (-0.2000) | 0.0800 (-0.3800) |
| 2.8837 | 601000 | 0.1758 | - | - | - | - | - | - |
| 2.8885 | 602000 | 0.1759 | - | - | - | - | - | - |
| 2.8933 | 603000 | 0.1759 | - | - | - | - | - | - |
| 2.8981 | 604000 | 0.1758 | - | - | - | - | - | - |
| 2.9029 | 605000 | 0.1759 | - | - | - | - | - | - |
| 2.9077 | 606000 | 0.1758 | - | - | - | - | - | - |
| 2.9125 | 607000 | 0.1758 | - | - | - | - | - | - |
| 2.9173 | 608000 | 0.1759 | - | - | - | - | - | - |
| 2.9221 | 609000 | 0.1759 | - | - | - | - | - | - |
| 2.9269 | 610000 | 0.1758 | - | - | - | - | - | - |
| 2.9317 | 611000 | 0.1758 | - | - | - | - | - | - |
| 2.9365 | 612000 | 0.1759 | - | - | - | - | - | - |
| 2.9413 | 613000 | 0.1758 | - | - | - | - | - | - |
| 2.9461 | 614000 | 0.1759 | - | - | - | - | - | - |
| 2.9509 | 615000 | 0.1759 | - | - | - | - | - | - |
| 2.9557 | 616000 | 0.1758 | - | - | - | - | - | - |
| 2.9605 | 617000 | 0.1758 | - | - | - | - | - | - |
| 2.9653 | 618000 | 0.1759 | - | - | - | - | - | - |
| 2.9701 | 619000 | 0.1759 | - | - | - | - | - | - |
| 2.9749 | 620000 | 0.1759 | 0.1641 | 0.5180 | 0.1400 (-0.6600) | 0.0500 (-0.2900) | 0.0400 (-0.2000) | 0.0767 (-0.3833) |
| 2.9797 | 621000 | 0.1758 | - | - | - | - | - | - |
| 2.9845 | 622000 | 0.1758 | - | - | - | - | - | - |
| 2.9893 | 623000 | 0.1759 | - | - | - | - | - | - |
| 2.9941 | 624000 | 0.1759 | - | - | - | - | - | - |
| 2.9989 | 625000 | 0.1758 | - | - | - | - | - | - |
| 3.0037 | 626000 | 0.1758 | - | - | - | - | - | - |
| 3.0084 | 627000 | 0.1759 | - | - | - | - | - | - |
| 3.0132 | 628000 | 0.1758 | - | - | - | - | - | - |
| 3.0180 | 629000 | 0.1759 | - | - | - | - | - | - |
| 3.0228 | 630000 | 0.1759 | - | - | - | - | - | - |
| 3.0276 | 631000 | 0.1758 | - | - | - | - | - | - |
| 3.0324 | 632000 | 0.1759 | - | - | - | - | - | - |
| 3.0372 | 633000 | 0.1758 | - | - | - | - | - | - |
| 3.0420 | 634000 | 0.1759 | - | - | - | - | - | - |
| 3.0468 | 635000 | 0.1758 | - | - | - | - | - | - |
| 3.0516 | 636000 | 0.1759 | - | - | - | - | - | - |
| 3.0564 | 637000 | 0.1759 | - | - | - | - | - | - |
| 3.0612 | 638000 | 0.1758 | - | - | - | - | - | - |
| 3.0660 | 639000 | 0.1758 | - | - | - | - | - | - |
| 3.0708 | 640000 | 0.1759 | 0.1642 | 0.5145 | 0.1400 (-0.6600) | 0.0700 (-0.2700) | 0.0400 (-0.2000) | 0.0833 (-0.3767) |
| 3.0756 | 641000 | 0.1759 | - | - | - | - | - | - |
| 3.0804 | 642000 | 0.1758 | - | - | - | - | - | - |
| 3.0852 | 643000 | 0.1758 | - | - | - | - | - | - |
| 3.0900 | 644000 | 0.1758 | - | - | - | - | - | - |
| 3.0948 | 645000 | 0.1759 | - | - | - | - | - | - |
| 3.0996 | 646000 | 0.1759 | - | - | - | - | - | - |
| 3.1044 | 647000 | 0.1758 | - | - | - | - | - | - |
| 3.1092 | 648000 | 0.1759 | - | - | - | - | - | - |
| 3.1140 | 649000 | 0.1759 | - | - | - | - | - | - |
| 3.1188 | 650000 | 0.1759 | - | - | - | - | - | - |
| 3.1236 | 651000 | 0.1759 | - | - | - | - | - | - |
| 3.1284 | 652000 | 0.1759 | - | - | - | - | - | - |
| 3.1332 | 653000 | 0.1759 | - | - | - | - | - | - |
| 3.1380 | 654000 | 0.1759 | - | - | - | - | - | - |
| 3.1428 | 655000 | 0.1759 | - | - | - | - | - | - |
| 3.1476 | 656000 | 0.1758 | - | - | - | - | - | - |
| 3.1524 | 657000 | 0.1759 | - | - | - | - | - | - |
| 3.1572 | 658000 | 0.1758 | - | - | - | - | - | - |
| 3.1620 | 659000 | 0.1758 | - | - | - | - | - | - |
| 3.1668 | 660000 | 0.1759 | 0.1640 | 0.5179 | 0.1200 (-0.6800) | 0.0500 (-0.2900) | 0.0400 (-0.2000) | 0.0700 (-0.3900) |
| 3.1716 | 661000 | 0.1759 | - | - | - | - | - | - |
| 3.1764 | 662000 | 0.1758 | - | - | - | - | - | - |
| 3.1812 | 663000 | 0.1758 | - | - | - | - | - | - |
| 3.1860 | 664000 | 0.1759 | - | - | - | - | - | - |
| 3.1908 | 665000 | 0.1759 | - | - | - | - | - | - |
| 3.1956 | 666000 | 0.1759 | - | - | - | - | - | - |
| 3.2004 | 667000 | 0.1759 | - | - | - | - | - | - |
| 3.2052 | 668000 | 0.1758 | - | - | - | - | - | - |
| 3.2100 | 669000 | 0.1759 | - | - | - | - | - | - |
| 3.2148 | 670000 | 0.1759 | - | - | - | - | - | - |
| 3.2196 | 671000 | 0.1758 | - | - | - | - | - | - |
| 3.2244 | 672000 | 0.1759 | - | - | - | - | - | - |
| 3.2292 | 673000 | 0.1759 | - | - | - | - | - | - |
| 3.2340 | 674000 | 0.1758 | - | - | - | - | - | - |
| 3.2388 | 675000 | 0.1759 | - | - | - | - | - | - |
| 3.2436 | 676000 | 0.1758 | - | - | - | - | - | - |
| 3.2484 | 677000 | 0.1758 | - | - | - | - | - | - |
| 3.2532 | 678000 | 0.176 | - | - | - | - | - | - |
| 3.2580 | 679000 | 0.1758 | - | - | - | - | - | - |
| 3.2628 | 680000 | 0.1759 | 0.1638 | 0.5175 | 0.1400 (-0.6600) | 0.0600 (-0.2800) | 0.0400 (-0.2000) | 0.0800 (-0.3800) |
| 3.2676 | 681000 | 0.1758 | - | - | - | - | - | - |
| 3.2723 | 682000 | 0.1759 | - | - | - | - | - | - |
| 3.2771 | 683000 | 0.1758 | - | - | - | - | - | - |
| 3.2819 | 684000 | 0.1759 | - | - | - | - | - | - |
| 3.2867 | 685000 | 0.1759 | - | - | - | - | - | - |
| 3.2915 | 686000 | 0.1759 | - | - | - | - | - | - |
| 3.2963 | 687000 | 0.1758 | - | - | - | - | - | - |
| 3.3011 | 688000 | 0.1759 | - | - | - | - | - | - |
| 3.3059 | 689000 | 0.1759 | - | - | - | - | - | - |
| 3.3107 | 690000 | 0.1758 | - | - | - | - | - | - |
| 3.3155 | 691000 | 0.1759 | - | - | - | - | - | - |
| 3.3203 | 692000 | 0.1759 | - | - | - | - | - | - |
| 3.3251 | 693000 | 0.1759 | - | - | - | - | - | - |
| 3.3299 | 694000 | 0.1759 | - | - | - | - | - | - |
| 3.3347 | 695000 | 0.1758 | - | - | - | - | - | - |
| 3.3395 | 696000 | 0.1758 | - | - | - | - | - | - |
| 3.3443 | 697000 | 0.1758 | - | - | - | - | - | - |
| 3.3491 | 698000 | 0.1759 | - | - | - | - | - | - |
| 3.3539 | 699000 | 0.1758 | - | - | - | - | - | - |
| 3.3587 | 700000 | 0.1758 | 0.1639 | 0.5180 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 3.3635 | 701000 | 0.1759 | - | - | - | - | - | - |
| 3.3683 | 702000 | 0.1759 | - | - | - | - | - | - |
| 3.3731 | 703000 | 0.1758 | - | - | - | - | - | - |
| 3.3779 | 704000 | 0.1758 | - | - | - | - | - | - |
| 3.3827 | 705000 | 0.1758 | - | - | - | - | - | - |
| 3.3875 | 706000 | 0.1758 | - | - | - | - | - | - |
| 3.3923 | 707000 | 0.1758 | - | - | - | - | - | - |
| 3.3971 | 708000 | 0.1759 | - | - | - | - | - | - |
| 3.4019 | 709000 | 0.1758 | - | - | - | - | - | - |
| 3.4067 | 710000 | 0.1758 | - | - | - | - | - | - |
| 3.4115 | 711000 | 0.1758 | - | - | - | - | - | - |
| 3.4163 | 712000 | 0.1758 | - | - | - | - | - | - |
| 3.4211 | 713000 | 0.1759 | - | - | - | - | - | - |
| 3.4259 | 714000 | 0.1758 | - | - | - | - | - | - |
| 3.4307 | 715000 | 0.1758 | - | - | - | - | - | - |
| 3.4355 | 716000 | 0.1758 | - | - | - | - | - | - |
| 3.4403 | 717000 | 0.1759 | - | - | - | - | - | - |
| 3.4451 | 718000 | 0.1759 | - | - | - | - | - | - |
| 3.4499 | 719000 | 0.1758 | - | - | - | - | - | - |
| 3.4547 | 720000 | 0.1758 | 0.1640 | 0.5177 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 3.4595 | 721000 | 0.1759 | - | - | - | - | - | - |
| 3.4643 | 722000 | 0.1758 | - | - | - | - | - | - |
| 3.4691 | 723000 | 0.1759 | - | - | - | - | - | - |
| 3.4739 | 724000 | 0.1758 | - | - | - | - | - | - |
| 3.4787 | 725000 | 0.1759 | - | - | - | - | - | - |
| 3.4835 | 726000 | 0.1759 | - | - | - | - | - | - |
| 3.4883 | 727000 | 0.1759 | - | - | - | - | - | - |
| 3.4931 | 728000 | 0.1759 | - | - | - | - | - | - |
| 3.4979 | 729000 | 0.1758 | - | - | - | - | - | - |
| 3.5027 | 730000 | 0.1758 | - | - | - | - | - | - |
| 3.5075 | 731000 | 0.1758 | - | - | - | - | - | - |
| 3.5123 | 732000 | 0.1758 | - | - | - | - | - | - |
| 3.5171 | 733000 | 0.1758 | - | - | - | - | - | - |
| 3.5219 | 734000 | 0.1758 | - | - | - | - | - | - |
| 3.5267 | 735000 | 0.1758 | - | - | - | - | - | - |
| 3.5314 | 736000 | 0.1759 | - | - | - | - | - | - |
| 3.5362 | 737000 | 0.1758 | - | - | - | - | - | - |
| 3.5410 | 738000 | 0.1759 | - | - | - | - | - | - |
| 3.5458 | 739000 | 0.1758 | - | - | - | - | - | - |
| 3.5506 | 740000 | 0.1759 | 0.1640 | 0.5182 | 0.1600 (-0.6400) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0800 (-0.3800) |
| 3.5554 | 741000 | 0.1759 | - | - | - | - | - | - |
| 3.5602 | 742000 | 0.1758 | - | - | - | - | - | - |
| 3.5650 | 743000 | 0.1758 | - | - | - | - | - | - |
| 3.5698 | 744000 | 0.1758 | - | - | - | - | - | - |
| 3.5746 | 745000 | 0.1758 | - | - | - | - | - | - |
| 3.5794 | 746000 | 0.1759 | - | - | - | - | - | - |
| 3.5842 | 747000 | 0.1759 | - | - | - | - | - | - |
| 3.5890 | 748000 | 0.1759 | - | - | - | - | - | - |
| 3.5938 | 749000 | 0.1759 | - | - | - | - | - | - |
| 3.5986 | 750000 | 0.1758 | - | - | - | - | - | - |
| 3.6034 | 751000 | 0.1758 | - | - | - | - | - | - |
| 3.6082 | 752000 | 0.1759 | - | - | - | - | - | - |
| 3.6130 | 753000 | 0.1759 | - | - | - | - | - | - |
| 3.6178 | 754000 | 0.1759 | - | - | - | - | - | - |
| 3.6226 | 755000 | 0.1759 | - | - | - | - | - | - |
| 3.6274 | 756000 | 0.1759 | - | - | - | - | - | - |
| 3.6322 | 757000 | 0.1759 | - | - | - | - | - | - |
| 3.6370 | 758000 | 0.1758 | - | - | - | - | - | - |
| 3.6418 | 759000 | 0.1759 | - | - | - | - | - | - |
| 3.6466 | 760000 | 0.1758 | 0.1636 | 0.5179 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 3.6514 | 761000 | 0.1759 | - | - | - | - | - | - |
| 3.6562 | 762000 | 0.1758 | - | - | - | - | - | - |
| 3.6610 | 763000 | 0.1758 | - | - | - | - | - | - |
| 3.6658 | 764000 | 0.1758 | - | - | - | - | - | - |
| 3.6706 | 765000 | 0.1759 | - | - | - | - | - | - |
| 3.6754 | 766000 | 0.1759 | - | - | - | - | - | - |
| 3.6802 | 767000 | 0.1759 | - | - | - | - | - | - |
| 3.6850 | 768000 | 0.1759 | - | - | - | - | - | - |
| 3.6898 | 769000 | 0.1759 | - | - | - | - | - | - |
| 3.6946 | 770000 | 0.1758 | - | - | - | - | - | - |
| 3.6994 | 771000 | 0.1759 | - | - | - | - | - | - |
| 3.7042 | 772000 | 0.1759 | - | - | - | - | - | - |
| 3.7090 | 773000 | 0.1759 | - | - | - | - | - | - |
| 3.7138 | 774000 | 0.1758 | - | - | - | - | - | - |
| 3.7186 | 775000 | 0.1759 | - | - | - | - | - | - |
| 3.7234 | 776000 | 0.1759 | - | - | - | - | - | - |
| 3.7282 | 777000 | 0.1759 | - | - | - | - | - | - |
| 3.7330 | 778000 | 0.1759 | - | - | - | - | - | - |
| 3.7378 | 779000 | 0.1758 | - | - | - | - | - | - |
| 3.7426 | 780000 | 0.1758 | 0.1637 | 0.5182 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 3.7474 | 781000 | 0.1758 | - | - | - | - | - | - |
| 3.7522 | 782000 | 0.1759 | - | - | - | - | - | - |
| 3.7570 | 783000 | 0.1759 | - | - | - | - | - | - |
| 3.7618 | 784000 | 0.1758 | - | - | - | - | - | - |
| 3.7666 | 785000 | 0.1759 | - | - | - | - | - | - |
| 3.7714 | 786000 | 0.1759 | - | - | - | - | - | - |
| 3.7762 | 787000 | 0.1758 | - | - | - | - | - | - |
| 3.7810 | 788000 | 0.176 | - | - | - | - | - | - |
| 3.7858 | 789000 | 0.1759 | - | - | - | - | - | - |
| 3.7906 | 790000 | 0.1759 | - | - | - | - | - | - |
| 3.7953 | 791000 | 0.1759 | - | - | - | - | - | - |
| 3.8001 | 792000 | 0.1759 | - | - | - | - | - | - |
| 3.8049 | 793000 | 0.1759 | - | - | - | - | - | - |
| 3.8097 | 794000 | 0.1758 | - | - | - | - | - | - |
| 3.8145 | 795000 | 0.1758 | - | - | - | - | - | - |
| 3.8193 | 796000 | 0.1759 | - | - | - | - | - | - |
| 3.8241 | 797000 | 0.1758 | - | - | - | - | - | - |
| 3.8289 | 798000 | 0.1759 | - | - | - | - | - | - |
| 3.8337 | 799000 | 0.1759 | - | - | - | - | - | - |
| 3.8385 | 800000 | 0.1759 | 0.1636 | 0.5176 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 3.8433 | 801000 | 0.1759 | - | - | - | - | - | - |
| 3.8481 | 802000 | 0.1759 | - | - | - | - | - | - |
| 3.8529 | 803000 | 0.1759 | - | - | - | - | - | - |
| 3.8577 | 804000 | 0.1758 | - | - | - | - | - | - |
| 3.8625 | 805000 | 0.1758 | - | - | - | - | - | - |
| 3.8673 | 806000 | 0.1759 | - | - | - | - | - | - |
| 3.8721 | 807000 | 0.1759 | - | - | - | - | - | - |
| 3.8769 | 808000 | 0.1758 | - | - | - | - | - | - |
| 3.8817 | 809000 | 0.1758 | - | - | - | - | - | - |
| 3.8865 | 810000 | 0.1759 | - | - | - | - | - | - |
| 3.8913 | 811000 | 0.1759 | - | - | - | - | - | - |
| 3.8961 | 812000 | 0.1758 | - | - | - | - | - | - |
| 3.9009 | 813000 | 0.1759 | - | - | - | - | - | - |
| 3.9057 | 814000 | 0.1759 | - | - | - | - | - | - |
| 3.9105 | 815000 | 0.1759 | - | - | - | - | - | - |
| 3.9153 | 816000 | 0.1759 | - | - | - | - | - | - |
| 3.9201 | 817000 | 0.1759 | - | - | - | - | - | - |
| 3.9249 | 818000 | 0.1759 | - | - | - | - | - | - |
| 3.9297 | 819000 | 0.1759 | - | - | - | - | - | - |
| 3.9345 | 820000 | 0.1759 | 0.1636 | 0.5186 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 3.9393 | 821000 | 0.1759 | - | - | - | - | - | - |
| 3.9441 | 822000 | 0.1759 | - | - | - | - | - | - |
| 3.9489 | 823000 | 0.1759 | - | - | - | - | - | - |
| 3.9537 | 824000 | 0.1759 | - | - | - | - | - | - |
| 3.9585 | 825000 | 0.1759 | - | - | - | - | - | - |
| 3.9633 | 826000 | 0.1759 | - | - | - | - | - | - |
| 3.9681 | 827000 | 0.176 | - | - | - | - | - | - |
| 3.9729 | 828000 | 0.1759 | - | - | - | - | - | - |
| 3.9777 | 829000 | 0.1759 | - | - | - | - | - | - |
| 3.9825 | 830000 | 0.1758 | - | - | - | - | - | - |
| 3.9873 | 831000 | 0.1759 | - | - | - | - | - | - |
| 3.9921 | 832000 | 0.1759 | - | - | - | - | - | - |
| 3.9969 | 833000 | 0.1758 | - | - | - | - | - | - |
| 4.0017 | 834000 | 0.1759 | - | - | - | - | - | - |
| 4.0065 | 835000 | 0.1758 | - | - | - | - | - | - |
| 4.0113 | 836000 | 0.1759 | - | - | - | - | - | - |
| 4.0161 | 837000 | 0.1759 | - | - | - | - | - | - |
| 4.0209 | 838000 | 0.1759 | - | - | - | - | - | - |
| 4.0257 | 839000 | 0.1759 | - | - | - | - | - | - |
| 4.0305 | 840000 | 0.1759 | 0.1634 | 0.5187 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.0353 | 841000 | 0.1759 | - | - | - | - | - | - |
| 4.0401 | 842000 | 0.1759 | - | - | - | - | - | - |
| 4.0449 | 843000 | 0.1759 | - | - | - | - | - | - |
| 4.0497 | 844000 | 0.1759 | - | - | - | - | - | - |
| 4.0544 | 845000 | 0.1759 | - | - | - | - | - | - |
| 4.0592 | 846000 | 0.176 | - | - | - | - | - | - |
| 4.0640 | 847000 | 0.1758 | - | - | - | - | - | - |
| 4.0688 | 848000 | 0.1759 | - | - | - | - | - | - |
| 4.0736 | 849000 | 0.1759 | - | - | - | - | - | - |
| 4.0784 | 850000 | 0.1759 | - | - | - | - | - | - |
| 4.0832 | 851000 | 0.1758 | - | - | - | - | - | - |
| 4.0880 | 852000 | 0.176 | - | - | - | - | - | - |
| 4.0928 | 853000 | 0.1758 | - | - | - | - | - | - |
| 4.0976 | 854000 | 0.176 | - | - | - | - | - | - |
| 4.1024 | 855000 | 0.1759 | - | - | - | - | - | - |
| 4.1072 | 856000 | 0.1759 | - | - | - | - | - | - |
| 4.1120 | 857000 | 0.1759 | - | - | - | - | - | - |
| 4.1168 | 858000 | 0.1759 | - | - | - | - | - | - |
| 4.1216 | 859000 | 0.1759 | - | - | - | - | - | - |
| 4.1264 | 860000 | 0.1759 | 0.1634 | 0.5190 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.1312 | 861000 | 0.176 | - | - | - | - | - | - |
| 4.1360 | 862000 | 0.1759 | - | - | - | - | - | - |
| 4.1408 | 863000 | 0.176 | - | - | - | - | - | - |
| 4.1456 | 864000 | 0.1759 | - | - | - | - | - | - |
| 4.1504 | 865000 | 0.1759 | - | - | - | - | - | - |
| 4.1552 | 866000 | 0.1759 | - | - | - | - | - | - |
| 4.1600 | 867000 | 0.1759 | - | - | - | - | - | - |
| 4.1648 | 868000 | 0.1759 | - | - | - | - | - | - |
| 4.1696 | 869000 | 0.1759 | - | - | - | - | - | - |
| 4.1744 | 870000 | 0.1759 | - | - | - | - | - | - |
| 4.1792 | 871000 | 0.1759 | - | - | - | - | - | - |
| 4.1840 | 872000 | 0.1759 | - | - | - | - | - | - |
| 4.1888 | 873000 | 0.1759 | - | - | - | - | - | - |
| 4.1936 | 874000 | 0.1759 | - | - | - | - | - | - |
| 4.1984 | 875000 | 0.176 | - | - | - | - | - | - |
| 4.2032 | 876000 | 0.1759 | - | - | - | - | - | - |
| 4.2080 | 877000 | 0.1759 | - | - | - | - | - | - |
| 4.2128 | 878000 | 0.1759 | - | - | - | - | - | - |
| 4.2176 | 879000 | 0.1759 | - | - | - | - | - | - |
| 4.2224 | 880000 | 0.1759 | 0.1633 | 0.5195 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.2272 | 881000 | 0.176 | - | - | - | - | - | - |
| 4.2320 | 882000 | 0.1759 | - | - | - | - | - | - |
| 4.2368 | 883000 | 0.1759 | - | - | - | - | - | - |
| 4.2416 | 884000 | 0.1759 | - | - | - | - | - | - |
| 4.2464 | 885000 | 0.1759 | - | - | - | - | - | - |
| 4.2512 | 886000 | 0.176 | - | - | - | - | - | - |
| 4.2560 | 887000 | 0.176 | - | - | - | - | - | - |
| 4.2608 | 888000 | 0.176 | - | - | - | - | - | - |
| 4.2656 | 889000 | 0.1759 | - | - | - | - | - | - |
| 4.2704 | 890000 | 0.1759 | - | - | - | - | - | - |
| 4.2752 | 891000 | 0.1759 | - | - | - | - | - | - |
| 4.2800 | 892000 | 0.1759 | - | - | - | - | - | - |
| 4.2848 | 893000 | 0.1759 | - | - | - | - | - | - |
| 4.2896 | 894000 | 0.1759 | - | - | - | - | - | - |
| 4.2944 | 895000 | 0.1759 | - | - | - | - | - | - |
| 4.2992 | 896000 | 0.1759 | - | - | - | - | - | - |
| 4.3040 | 897000 | 0.176 | - | - | - | - | - | - |
| 4.3088 | 898000 | 0.1759 | - | - | - | - | - | - |
| 4.3136 | 899000 | 0.1759 | - | - | - | - | - | - |
| 4.3183 | 900000 | 0.1759 | 0.1633 | 0.5195 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.3231 | 901000 | 0.176 | - | - | - | - | - | - |
| 4.3279 | 902000 | 0.1759 | - | - | - | - | - | - |
| 4.3327 | 903000 | 0.1759 | - | - | - | - | - | - |
| 4.3375 | 904000 | 0.1759 | - | - | - | - | - | - |
| 4.3423 | 905000 | 0.1759 | - | - | - | - | - | - |
| 4.3471 | 906000 | 0.1759 | - | - | - | - | - | - |
| 4.3519 | 907000 | 0.1759 | - | - | - | - | - | - |
| 4.3567 | 908000 | 0.1759 | - | - | - | - | - | - |
| 4.3615 | 909000 | 0.1759 | - | - | - | - | - | - |
| 4.3663 | 910000 | 0.1759 | - | - | - | - | - | - |
| 4.3711 | 911000 | 0.1759 | - | - | - | - | - | - |
| 4.3759 | 912000 | 0.1758 | - | - | - | - | - | - |
| 4.3807 | 913000 | 0.1759 | - | - | - | - | - | - |
| 4.3855 | 914000 | 0.1759 | - | - | - | - | - | - |
| 4.3903 | 915000 | 0.1759 | - | - | - | - | - | - |
| 4.3951 | 916000 | 0.1759 | - | - | - | - | - | - |
| 4.3999 | 917000 | 0.1759 | - | - | - | - | - | - |
| 4.4047 | 918000 | 0.1758 | - | - | - | - | - | - |
| 4.4095 | 919000 | 0.1759 | - | - | - | - | - | - |
| 4.4143 | 920000 | 0.1758 | 0.1632 | 0.5194 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.4191 | 921000 | 0.1759 | - | - | - | - | - | - |
| 4.4239 | 922000 | 0.1758 | - | - | - | - | - | - |
| 4.4287 | 923000 | 0.1759 | - | - | - | - | - | - |
| 4.4335 | 924000 | 0.1758 | - | - | - | - | - | - |
| 4.4383 | 925000 | 0.1759 | - | - | - | - | - | - |
| 4.4431 | 926000 | 0.176 | - | - | - | - | - | - |
| 4.4479 | 927000 | 0.1759 | - | - | - | - | - | - |
| 4.4527 | 928000 | 0.1758 | - | - | - | - | - | - |
| 4.4575 | 929000 | 0.1759 | - | - | - | - | - | - |
| 4.4623 | 930000 | 0.1759 | - | - | - | - | - | - |
| 4.4671 | 931000 | 0.1758 | - | - | - | - | - | - |
| 4.4719 | 932000 | 0.1759 | - | - | - | - | - | - |
| 4.4767 | 933000 | 0.1759 | - | - | - | - | - | - |
| 4.4815 | 934000 | 0.1759 | - | - | - | - | - | - |
| 4.4863 | 935000 | 0.1759 | - | - | - | - | - | - |
| 4.4911 | 936000 | 0.1758 | - | - | - | - | - | - |
| 4.4959 | 937000 | 0.1759 | - | - | - | - | - | - |
| 4.5007 | 938000 | 0.1758 | - | - | - | - | - | - |
| 4.5055 | 939000 | 0.1759 | - | - | - | - | - | - |
| 4.5103 | 940000 | 0.1758 | 0.1632 | 0.5196 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.5151 | 941000 | 0.1759 | - | - | - | - | - | - |
| 4.5199 | 942000 | 0.1758 | - | - | - | - | - | - |
| 4.5247 | 943000 | 0.1758 | - | - | - | - | - | - |
| 4.5295 | 944000 | 0.1759 | - | - | - | - | - | - |
| 4.5343 | 945000 | 0.1758 | - | - | - | - | - | - |
| 4.5391 | 946000 | 0.1759 | - | - | - | - | - | - |
| 4.5439 | 947000 | 0.1759 | - | - | - | - | - | - |
| 4.5487 | 948000 | 0.1759 | - | - | - | - | - | - |
| 4.5535 | 949000 | 0.1759 | - | - | - | - | - | - |
| 4.5583 | 950000 | 0.1759 | - | - | - | - | - | - |
| 4.5631 | 951000 | 0.1759 | - | - | - | - | - | - |
| 4.5679 | 952000 | 0.1759 | - | - | - | - | - | - |
| 4.5727 | 953000 | 0.1759 | - | - | - | - | - | - |
| 4.5774 | 954000 | 0.1758 | - | - | - | - | - | - |
| 4.5822 | 955000 | 0.1759 | - | - | - | - | - | - |
| 4.5870 | 956000 | 0.1759 | - | - | - | - | - | - |
| 4.5918 | 957000 | 0.1759 | - | - | - | - | - | - |
| 4.5966 | 958000 | 0.1758 | - | - | - | - | - | - |
| 4.6014 | 959000 | 0.1758 | - | - | - | - | - | - |
| 4.6062 | 960000 | 0.1759 | 0.1632 | 0.5197 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.6110 | 961000 | 0.1759 | - | - | - | - | - | - |
| 4.6158 | 962000 | 0.1759 | - | - | - | - | - | - |
| 4.6206 | 963000 | 0.1759 | - | - | - | - | - | - |
| 4.6254 | 964000 | 0.1759 | - | - | - | - | - | - |
| 4.6302 | 965000 | 0.1759 | - | - | - | - | - | - |
| 4.6350 | 966000 | 0.1759 | - | - | - | - | - | - |
| 4.6398 | 967000 | 0.1759 | - | - | - | - | - | - |
| 4.6446 | 968000 | 0.1759 | - | - | - | - | - | - |
| 4.6494 | 969000 | 0.1759 | - | - | - | - | - | - |
| 4.6542 | 970000 | 0.1759 | - | - | - | - | - | - |
| 4.6590 | 971000 | 0.1759 | - | - | - | - | - | - |
| 4.6638 | 972000 | 0.1759 | - | - | - | - | - | - |
| 4.6686 | 973000 | 0.1759 | - | - | - | - | - | - |
| 4.6734 | 974000 | 0.1759 | - | - | - | - | - | - |
| 4.6782 | 975000 | 0.1759 | - | - | - | - | - | - |
| 4.6830 | 976000 | 0.1759 | - | - | - | - | - | - |
| 4.6878 | 977000 | 0.1759 | - | - | - | - | - | - |
| 4.6926 | 978000 | 0.1759 | - | - | - | - | - | - |
| 4.6974 | 979000 | 0.1759 | - | - | - | - | - | - |
| 4.7022 | 980000 | 0.1759 | 0.1631 | 0.5198 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.7070 | 981000 | 0.1759 | - | - | - | - | - | - |
| 4.7118 | 982000 | 0.1759 | - | - | - | - | - | - |
| 4.7166 | 983000 | 0.1758 | - | - | - | - | - | - |
| 4.7214 | 984000 | 0.1759 | - | - | - | - | - | - |
| 4.7262 | 985000 | 0.1759 | - | - | - | - | - | - |
| 4.7310 | 986000 | 0.1759 | - | - | - | - | - | - |
| 4.7358 | 987000 | 0.1758 | - | - | - | - | - | - |
| 4.7406 | 988000 | 0.1758 | - | - | - | - | - | - |
| 4.7454 | 989000 | 0.1758 | - | - | - | - | - | - |
| 4.7502 | 990000 | 0.1759 | - | - | - | - | - | - |
| 4.7550 | 991000 | 0.1759 | - | - | - | - | - | - |
| 4.7598 | 992000 | 0.1759 | - | - | - | - | - | - |
| 4.7646 | 993000 | 0.1759 | - | - | - | - | - | - |
| 4.7694 | 994000 | 0.1759 | - | - | - | - | - | - |
| 4.7742 | 995000 | 0.1759 | - | - | - | - | - | - |
| 4.7790 | 996000 | 0.1759 | - | - | - | - | - | - |
| 4.7838 | 997000 | 0.1759 | - | - | - | - | - | - |
| 4.7886 | 998000 | 0.176 | - | - | - | - | - | - |
| 4.7934 | 999000 | 0.1759 | - | - | - | - | - | - |
| **4.7982** | **1000000** | **0.1758** | **0.1631** | **0.5198** | **0.1200 (-0.6800)** | **0.0400 (-0.3000)** | **0.0400 (-0.2000)** | **0.0667 (-0.3933)** |
| 4.8030 | 1001000 | 0.1759 | - | - | - | - | - | - |
| 4.8078 | 1002000 | 0.176 | - | - | - | - | - | - |
| 4.8126 | 1003000 | 0.1758 | - | - | - | - | - | - |
| 4.8174 | 1004000 | 0.1759 | - | - | - | - | - | - |
| 4.8222 | 1005000 | 0.1759 | - | - | - | - | - | - |
| 4.8270 | 1006000 | 0.1759 | - | - | - | - | - | - |
| 4.8318 | 1007000 | 0.1759 | - | - | - | - | - | - |
| 4.8366 | 1008000 | 0.1759 | - | - | - | - | - | - |
| 4.8413 | 1009000 | 0.1759 | - | - | - | - | - | - |
| 4.8461 | 1010000 | 0.1759 | - | - | - | - | - | - |
| 4.8509 | 1011000 | 0.176 | - | - | - | - | - | - |
| 4.8557 | 1012000 | 0.1759 | - | - | - | - | - | - |
| 4.8605 | 1013000 | 0.1758 | - | - | - | - | - | - |
| 4.8653 | 1014000 | 0.1759 | - | - | - | - | - | - |
| 4.8701 | 1015000 | 0.176 | - | - | - | - | - | - |
| 4.8749 | 1016000 | 0.1759 | - | - | - | - | - | - |
| 4.8797 | 1017000 | 0.1759 | - | - | - | - | - | - |
| 4.8845 | 1018000 | 0.176 | - | - | - | - | - | - |
| 4.8893 | 1019000 | 0.1759 | - | - | - | - | - | - |
| 4.8941 | 1020000 | 0.1759 | 0.1631 | 0.5199 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.8989 | 1021000 | 0.176 | - | - | - | - | - | - |
| 4.9037 | 1022000 | 0.176 | - | - | - | - | - | - |
| 4.9085 | 1023000 | 0.1759 | - | - | - | - | - | - |
| 4.9133 | 1024000 | 0.1759 | - | - | - | - | - | - |
| 4.9181 | 1025000 | 0.176 | - | - | - | - | - | - |
| 4.9229 | 1026000 | 0.1759 | - | - | - | - | - | - |
| 4.9277 | 1027000 | 0.1759 | - | - | - | - | - | - |
| 4.9325 | 1028000 | 0.1759 | - | - | - | - | - | - |
| 4.9373 | 1029000 | 0.1759 | - | - | - | - | - | - |
| 4.9421 | 1030000 | 0.1759 | - | - | - | - | - | - |
| 4.9469 | 1031000 | 0.1759 | - | - | - | - | - | - |
| 4.9517 | 1032000 | 0.1759 | - | - | - | - | - | - |
| 4.9565 | 1033000 | 0.1759 | - | - | - | - | - | - |
| 4.9613 | 1034000 | 0.1759 | - | - | - | - | - | - |
| 4.9661 | 1035000 | 0.1759 | - | - | - | - | - | - |
| 4.9709 | 1036000 | 0.1759 | - | - | - | - | - | - |
| 4.9757 | 1037000 | 0.1759 | - | - | - | - | - | - |
| 4.9805 | 1038000 | 0.1759 | - | - | - | - | - | - |
| 4.9853 | 1039000 | 0.1759 | - | - | - | - | - | - |
| 4.9901 | 1040000 | 0.1759 | 0.1631 | 0.5199 | 0.1200 (-0.6800) | 0.0400 (-0.3000) | 0.0400 (-0.2000) | 0.0667 (-0.3933) |
| 4.9949 | 1041000 | 0.1759 | - | - | - | - | - | - |
| 4.9997 | 1042000 | 0.1759 | - | - | - | - | - | - |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 5.1.0
- Transformers: 4.56.0
- PyTorch: 2.8.0+cu128
- Accelerate: 1.10.1
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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