---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:28783032
- loss:MSELoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: It is 98 percent employee-owned.
sentences:
- Nos encontramos en una situación histórica en la que estamos negociando con 12
Estados miembros o vamos a iniciar negociaciones y ante situación en la que el
crecimiento conjunto de Europa se puede hacer realidad en un tiempo previsible.
- OBESSU es formalmente reconocido por el Consejo de Europa y la Unión europea y
es un regular interlocutor de la Comisión europea, Eurocámara, Consejo de Europa
y UNESCO.
- Son el 98% de las empleadas domésticas.
- source_sentence: There is the continuing situation with regard to Article 235, as
the honourable Member has mentioned, and I have to say that Article 235 is the
only possible legal base for the actions that are carried out under the current
Helios programme.
sentences:
- Una investigación inicial en el museo mostró "fallas flagrantes" en la seguridad,
donde funcionaban apropiadamente sólo siete de las 43 cámaras instaladas, informaron
medios estatales.
- La votación de mañana también es importante para nosotros porque hemos presentado
una serie de enmiendas que hacen hincapié en la preferencia comunitaria.
- Continúa la situación en lo que respecta al artículo 235, como ha mencionado su
Señoría, y debo decir que dicho artículo 235 es el único fundamento jurídico posible
para emprender las acciones que se llevan a cabo bajo el actual programa Helios.
- source_sentence: People Finding Gold And Coal At Mining Work
sentences:
- Se emocionaron al recibir a tantos visitantes de diferentes países.
- encontrar oro y carbón en el trabajo minero personas
- '- ¿Está el fax preparado?'
- source_sentence: Come on, darling.
sentences:
- Vamos, cariño.
- – Señor Presidente, por lo que se refiere a la enmienda 15, desearía que se considerase
como un añadido.
- Digger más tarde apareció en Superboy con otro escuadrón.
- source_sentence: Why are you so afraid?
sentences:
- Segundos después, un asaltante enmascarado vuela la puerta de la habitación con
explosivos, mata sus asesores y luego procede a secuestrar a Ashton.
- '- ¿Roma desde mi teléfono?'
- ¿Por qué tienes tanto miedo?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- negative_mse
- src2trg_accuracy
- trg2src_accuracy
- mean_accuracy
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results:
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: mse evaluation en es
type: mse_evaluation-en-es
metrics:
- type: negative_mse
value: -0.046344783157110214
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: translation evaluation en es
type: translation_evaluation-en-es
metrics:
- type: src2trg_accuracy
value: 0.9417
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.9294
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.93555
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: mse evaluation en pt
type: mse_evaluation-en-pt
metrics:
- type: negative_mse
value: -0.04470514878630638
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: translation evaluation en pt
type: translation_evaluation-en-pt
metrics:
- type: src2trg_accuracy
value: 0.9513
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.9384
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.94485
name: Mean Accuracy
- task:
type: knowledge-distillation
name: Knowledge Distillation
dataset:
name: mse evaluation en pt br
type: mse_evaluation-en-pt-br
metrics:
- type: negative_mse
value: -0.039737459272146225
name: Negative Mse
- task:
type: translation
name: Translation
dataset:
name: translation evaluation en pt br
type: translation_evaluation-en-pt-br
metrics:
- type: src2trg_accuracy
value: 0.9789
name: Src2Trg Accuracy
- type: trg2src_accuracy
value: 0.9728
name: Trg2Src Accuracy
- type: mean_accuracy
value: 0.97585
name: Mean Accuracy
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts17 es en
type: sts17-es-en
metrics:
- type: pearson_cosine
value: 0.8102009718548969
name: Pearson Cosine
- type: spearman_cosine
value: 0.817051723930442
name: Spearman Cosine
---
# SentenceTransformer based on BAAI/bge-small-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
# Run inference
sentences = [
'Why are you so afraid?',
'¿Por qué tienes tanto miedo?',
'Segundos después, un asaltante enmascarado vuela la puerta de la habitación con explosivos, mata sus asesores y luego procede a secuestrar a Ashton.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Knowledge Distillation
* Datasets: `mse_evaluation-en-es`, `mse_evaluation-en-pt` and `mse_evaluation-en-pt-br`
* Evaluated with [MSEEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
| Metric | mse_evaluation-en-es | mse_evaluation-en-pt | mse_evaluation-en-pt-br |
|:-----------------|:---------------------|:---------------------|:------------------------|
| **negative_mse** | **-0.0463** | **-0.0447** | **-0.0397** |
#### Translation
* Datasets: `translation_evaluation-en-es`, `translation_evaluation-en-pt` and `translation_evaluation-en-pt-br`
* Evaluated with [TranslationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TranslationEvaluator)
| Metric | translation_evaluation-en-es | translation_evaluation-en-pt | translation_evaluation-en-pt-br |
|:------------------|:-----------------------------|:-----------------------------|:--------------------------------|
| src2trg_accuracy | 0.9417 | 0.9513 | 0.9789 |
| trg2src_accuracy | 0.9294 | 0.9384 | 0.9728 |
| **mean_accuracy** | **0.9355** | **0.9448** | **0.9758** |
#### Semantic Similarity
* Dataset: `sts17-es-en`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8102 |
| **spearman_cosine** | **0.8171** |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 28,783,032 training samples
* Columns: english, non_english, and label
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | - min: 4 tokens
- mean: 26.03 tokens
- max: 131 tokens
| - min: 4 tokens
- mean: 41.0 tokens
- max: 219 tokens
| |
* Samples:
| english | non_english | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------|
| Unlike last year, the eye of the storm this year is Sindh where flood waters have razed in numerous villages and displaced millions. | A diferencia del año pasado, el ojo de la tormenta este año está en Sindh , donde las aguas de las inundaciones han arrasado numerosas aldeas y desplazado a millones. | [-0.007684561889618635, 0.008107933215796947, -0.011652171611785889, 0.025253329426050186, 0.03101799078285694, ...] |
| Spotted taxis to Jeradda. | Vi taxis a Jeradda. | [0.02564830146729946, -0.0010036780731752515, 0.04843117669224739, -0.002532949671149254, 0.01702607050538063, ...] |
| The page has also inspired several other copycat pages on Facebook with similar titles | Asimismo, la página ha inspirado otras páginas imitadoras en Facebook con títulos similares. | [-0.04195510596036911, -0.026423752307891846, -0.08573361486196518, 0.0040077404119074345, -0.02051585540175438, ...] |
* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Evaluation Dataset
#### Unnamed Dataset
* Size: 30,000 evaluation samples
* Columns: english, non_english, and label
* Approximate statistics based on the first 1000 samples:
| | english | non_english | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:-------------------------------------|
| type | string | string | list |
| details | - min: 3 tokens
- mean: 22.91 tokens
- max: 104 tokens
| - min: 3 tokens
- mean: 36.22 tokens
- max: 203 tokens
| |
* Samples:
| english | non_english | label |
|:-----------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
| It goes with your hair. | Va bien con su pelo. | [0.013736451044678688, -0.014708670787513256, 0.020351335406303406, -0.02381581813097, -0.013853796757757664, ...] |
| We know that criminality is not limited to our continent, but consists of worldwide networks. | Sabemos que la criminalidad no está limitada a nuestro continente, sino que consta de redes mundiales. | [0.03908732160925865, 0.0009072094107978046, 0.0007832577684894204, -0.044317133724689484, 0.09392903745174408, ...] |
| Mm-hm. All except the biggest one. | A todos, excepto al más grande. | [-0.024451246485114098, -0.03345730900764465, -0.04918806627392769, -0.05197983607649803, -0.03147919476032257, ...] |
* Loss: [MSELoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `gradient_accumulation_steps`: 2
- `learning_rate`: 0.0003
- `num_train_epochs`: 8
- `warmup_ratio`: 0.15
- `bf16`: True
- `dataloader_num_workers`: 8
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.0003
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 8
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.15
- `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`: 8
- `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`: None
- `hub_always_push`: False
- `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
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | Validation Loss | mse_evaluation-en-es_negative_mse | translation_evaluation-en-es_mean_accuracy | mse_evaluation-en-pt_negative_mse | translation_evaluation-en-pt_mean_accuracy | mse_evaluation-en-pt-br_negative_mse | translation_evaluation-en-pt-br_mean_accuracy | sts17-es-en_spearman_cosine |
|:------:|:------:|:-------------:|:---------------:|:---------------------------------:|:------------------------------------------:|:---------------------------------:|:------------------------------------------:|:------------------------------------:|:---------------------------------------------:|:---------------------------:|
| 0.0178 | 500 | 0.0009 | - | - | - | - | - | - | - | - |
| 0.0356 | 1000 | 0.0008 | - | - | - | - | - | - | - | - |
| 0.0534 | 1500 | 0.0007 | - | - | - | - | - | - | - | - |
| 0.0712 | 2000 | 0.0007 | 0.0005 | -0.10122959 | 0.6481 | -0.10340506 | 0.6205 | -0.10725543 | 0.5905 | 0.3595 |
| 0.0889 | 2500 | 0.0006 | - | - | - | - | - | - | - | - |
| 0.1067 | 3000 | 0.0006 | - | - | - | - | - | - | - | - |
| 0.1245 | 3500 | 0.0006 | - | - | - | - | - | - | - | - |
| 0.1423 | 4000 | 0.0006 | 0.0004 | -0.08365556 | 0.7992 | -0.08426243 | 0.8051 | -0.08419664 | 0.8281 | 0.4937 |
| 0.1601 | 4500 | 0.0005 | - | - | - | - | - | - | - | - |
| 0.1779 | 5000 | 0.0005 | - | - | - | - | - | - | - | - |
| 0.1957 | 5500 | 0.0005 | - | - | - | - | - | - | - | - |
| 0.2135 | 6000 | 0.0005 | 0.0004 | -0.07376316 | 0.8593 | -0.073814325 | 0.8695 | -0.07152441 | 0.9081 | 0.6214 |
| 0.2312 | 6500 | 0.0005 | - | - | - | - | - | - | - | - |
| 0.2490 | 7000 | 0.0005 | - | - | - | - | - | - | - | - |
| 0.2668 | 7500 | 0.0005 | - | - | - | - | - | - | - | - |
| 0.2846 | 8000 | 0.0005 | 0.0004 | -0.06761765 | 0.8821 | -0.067249514 | 0.8938 | -0.06428356 | 0.9331 | 0.6909 |
| 0.3024 | 8500 | 0.0005 | - | - | - | - | - | - | - | - |
| 0.3202 | 9000 | 0.0005 | - | - | - | - | - | - | - | - |
| 0.3380 | 9500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.3558 | 10000 | 0.0004 | 0.0003 | -0.06365564 | 0.8960 | -0.06300899 | 0.9072 | -0.059927516 | 0.9428 | 0.7220 |
| 0.3736 | 10500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.3913 | 11000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.4091 | 11500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.4269 | 12000 | 0.0004 | 0.0003 | -0.060988236 | 0.905 | -0.060110345 | 0.9161 | -0.0567778 | 0.9517 | 0.7385 |
| 0.4447 | 12500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.4625 | 13000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.4803 | 13500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.4981 | 14000 | 0.0004 | 0.0003 | -0.05906638 | 0.9109 | -0.0581316 | 0.9222 | -0.05460985 | 0.9569 | 0.7502 |
| 0.5159 | 14500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.5336 | 15000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.5514 | 15500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.5692 | 16000 | 0.0004 | 0.0003 | -0.057560425 | 0.9152 | -0.05654549 | 0.9244 | -0.052867033 | 0.9596 | 0.7595 |
| 0.5870 | 16500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.6048 | 17000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.6226 | 17500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.6404 | 18000 | 0.0004 | 0.0003 | -0.05640703 | 0.9160 | -0.055421203 | 0.9271 | -0.051537633 | 0.9621 | 0.7660 |
| 0.6582 | 18500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.6760 | 19000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.6937 | 19500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.7115 | 20000 | 0.0004 | 0.0003 | -0.055573538 | 0.9194 | -0.054422 | 0.9296 | -0.050369795 | 0.9634 | 0.7793 |
| 0.7293 | 20500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.7471 | 21000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.7649 | 21500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.7827 | 22000 | 0.0004 | 0.0003 | -0.054716494 | 0.9215 | -0.05353505 | 0.9318 | -0.049468324 | 0.9652 | 0.7807 |
| 0.8005 | 22500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.8183 | 23000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.8360 | 23500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.8538 | 24000 | 0.0004 | 0.0003 | -0.054093935 | 0.9230 | -0.052881736 | 0.9325 | -0.04880123 | 0.9662 | 0.7913 |
| 0.8716 | 24500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.8894 | 25000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.9072 | 25500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.9250 | 26000 | 0.0004 | 0.0003 | -0.05346901 | 0.9239 | -0.0522654 | 0.9354 | -0.048117638 | 0.9675 | 0.7909 |
| 0.9428 | 26500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.9606 | 27000 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.9784 | 27500 | 0.0004 | - | - | - | - | - | - | - | - |
| 0.9961 | 28000 | 0.0004 | 0.0003 | -0.05307414 | 0.9252 | -0.051890973 | 0.9358 | -0.047557026 | 0.968 | 0.7980 |
| 1.0139 | 28500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.0317 | 29000 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.0495 | 29500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.0673 | 30000 | 0.0004 | 0.0003 | -0.052725878 | 0.9255 | -0.051456288 | 0.9363 | -0.04720709 | 0.9678 | 0.7961 |
| 1.0851 | 30500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.1029 | 31000 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.1206 | 31500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.1384 | 32000 | 0.0004 | 0.0003 | -0.05232365 | 0.9254 | -0.051122643 | 0.9365 | -0.046895616 | 0.9697 | 0.7980 |
| 1.1562 | 32500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.1740 | 33000 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.1918 | 33500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.2096 | 34000 | 0.0004 | 0.0003 | -0.052056298 | 0.9265 | -0.050777122 | 0.9382 | -0.04632004 | 0.9692 | 0.8033 |
| 1.2274 | 34500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.2452 | 35000 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.2629 | 35500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.2807 | 36000 | 0.0004 | 0.0003 | -0.051708605 | 0.9264 | -0.050396774 | 0.9375 | -0.04602845 | 0.9700 | 0.8045 |
| 1.2985 | 36500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.3163 | 37000 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.3341 | 37500 | 0.0004 | - | - | - | - | - | - | - | - |
| 1.3519 | 38000 | 0.0003 | 0.0003 | -0.05145791 | 0.9271 | -0.050056294 | 0.9383 | -0.045601957 | 0.9699 | 0.8009 |
| 1.3697 | 38500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.3875 | 39000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.4053 | 39500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.4230 | 40000 | 0.0003 | 0.0003 | -0.05112224 | 0.9286 | -0.049697492 | 0.9385 | -0.04513698 | 0.9706 | 0.8026 |
| 1.4408 | 40500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.4586 | 41000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.4764 | 41500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.4942 | 42000 | 0.0003 | 0.0003 | -0.05098933 | 0.9277 | -0.04952781 | 0.9397 | -0.045018516 | 0.9706 | 0.8064 |
| 1.5120 | 42500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.5298 | 43000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.5476 | 43500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.5653 | 44000 | 0.0003 | 0.0003 | -0.05063988 | 0.9300 | -0.04921307 | 0.9405 | -0.044630885 | 0.971 | 0.8053 |
| 1.5831 | 44500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.6009 | 45000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.6187 | 45500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.6365 | 46000 | 0.0003 | 0.0003 | -0.050518364 | 0.9292 | -0.04895482 | 0.9400 | -0.044507742 | 0.9717 | 0.8025 |
| 1.6543 | 46500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.6721 | 47000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.6899 | 47500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.7077 | 48000 | 0.0003 | 0.0003 | -0.05039251 | 0.9295 | -0.048876137 | 0.9402 | -0.044356253 | 0.9717 | 0.8000 |
| 1.7254 | 48500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.7432 | 49000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.7610 | 49500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.7788 | 50000 | 0.0003 | 0.0003 | -0.050093956 | 0.9299 | -0.04855838 | 0.9405 | -0.044041798 | 0.9715 | 0.8029 |
| 1.7966 | 50500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.8144 | 51000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.8322 | 51500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.8500 | 52000 | 0.0003 | 0.0003 | -0.04991082 | 0.9303 | -0.04846586 | 0.9408 | -0.043873988 | 0.972 | 0.8081 |
| 1.8677 | 52500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.8855 | 53000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.9033 | 53500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.9211 | 54000 | 0.0003 | 0.0003 | -0.049736623 | 0.9308 | -0.048238307 | 0.9412 | -0.04365188 | 0.9719 | 0.8024 |
| 1.9389 | 54500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.9567 | 55000 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.9745 | 55500 | 0.0003 | - | - | - | - | - | - | - | - |
| 1.9923 | 56000 | 0.0003 | 0.0003 | -0.049630426 | 0.9304 | -0.048146106 | 0.9409 | -0.043522894 | 0.9724 | 0.8083 |
| 2.0100 | 56500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.0278 | 57000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.0456 | 57500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.0634 | 58000 | 0.0003 | 0.0003 | -0.04953543 | 0.9315 | -0.04801518 | 0.9408 | -0.04336739 | 0.9729 | 0.8088 |
| 2.0812 | 58500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.0990 | 59000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.1168 | 59500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.1346 | 60000 | 0.0003 | 0.0003 | -0.049395695 | 0.9298 | -0.047923535 | 0.9415 | -0.04315244 | 0.9718 | 0.8021 |
| 2.1523 | 60500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.1701 | 61000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.1879 | 61500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.2057 | 62000 | 0.0003 | 0.0003 | -0.04926271 | 0.9308 | -0.04775018 | 0.9415 | -0.043019187 | 0.9728 | 0.8044 |
| 2.2235 | 62500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.2413 | 63000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.2591 | 63500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.2769 | 64000 | 0.0003 | 0.0003 | -0.049204785 | 0.9310 | -0.047680687 | 0.9414 | -0.04296754 | 0.9726 | 0.8056 |
| 2.2946 | 64500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.3124 | 65000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.3302 | 65500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.3480 | 66000 | 0.0003 | 0.0003 | -0.049087144 | 0.9310 | -0.04754995 | 0.9413 | -0.042845335 | 0.9732 | 0.8008 |
| 2.3658 | 66500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.3836 | 67000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.4014 | 67500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.4192 | 68000 | 0.0003 | 0.0003 | -0.049001597 | 0.9308 | -0.04741441 | 0.9419 | -0.042673677 | 0.9733 | 0.8080 |
| 2.4369 | 68500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.4547 | 69000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.4725 | 69500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.4903 | 70000 | 0.0003 | 0.0003 | -0.048882827 | 0.9321 | -0.047341634 | 0.9422 | -0.042661835 | 0.9734 | 0.8087 |
| 2.5081 | 70500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.5259 | 71000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.5437 | 71500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.5615 | 72000 | 0.0003 | 0.0003 | -0.048752587 | 0.9314 | -0.04728313 | 0.9415 | -0.042584546 | 0.9734 | 0.8069 |
| 2.5793 | 72500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.5970 | 73000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.6148 | 73500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.6326 | 74000 | 0.0003 | 0.0003 | -0.04871502 | 0.9319 | -0.047124594 | 0.9424 | -0.04228281 | 0.9733 | 0.8082 |
| 2.6504 | 74500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.6682 | 75000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.6860 | 75500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.7038 | 76000 | 0.0003 | 0.0003 | -0.04866153 | 0.9320 | -0.047041744 | 0.9432 | -0.04232903 | 0.9736 | 0.8078 |
| 2.7216 | 76500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.7393 | 77000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.7571 | 77500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.7749 | 78000 | 0.0003 | 0.0003 | -0.04851333 | 0.9323 | -0.046925455 | 0.9424 | -0.04223377 | 0.9735 | 0.8134 |
| 2.7927 | 78500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.8105 | 79000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.8283 | 79500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.8461 | 80000 | 0.0003 | 0.0003 | -0.048524972 | 0.9329 | -0.04693048 | 0.9425 | -0.042133827 | 0.9725 | 0.8085 |
| 2.8639 | 80500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.8817 | 81000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.8994 | 81500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.9172 | 82000 | 0.0003 | 0.0003 | -0.048406757 | 0.9336 | -0.04686156 | 0.9433 | -0.042108946 | 0.9736 | 0.8098 |
| 2.9350 | 82500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.9528 | 83000 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.9706 | 83500 | 0.0003 | - | - | - | - | - | - | - | - |
| 2.9884 | 84000 | 0.0003 | 0.0003 | -0.048344385 | 0.9317 | -0.046766046 | 0.9432 | -0.042005215 | 0.9736 | 0.8109 |
| 3.0062 | 84500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.0239 | 85000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.0417 | 85500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.0595 | 86000 | 0.0003 | 0.0003 | -0.048280284 | 0.9314 | -0.04669383 | 0.9431 | -0.041867185 | 0.9738 | 0.8065 |
| 3.0773 | 86500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.0951 | 87000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.1129 | 87500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.1307 | 88000 | 0.0003 | 0.0003 | -0.04822296 | 0.9325 | -0.046689868 | 0.9425 | -0.041927855 | 0.9735 | 0.8103 |
| 3.1485 | 88500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.1662 | 89000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.1840 | 89500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.2018 | 90000 | 0.0003 | 0.0003 | -0.048173092 | 0.9318 | -0.046601273 | 0.9422 | -0.041765593 | 0.9741 | 0.8067 |
| 3.2196 | 90500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.2374 | 91000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.2552 | 91500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.2730 | 92000 | 0.0003 | 0.0003 | -0.04806846 | 0.9333 | -0.046525463 | 0.9425 | -0.041717805 | 0.9735 | 0.8069 |
| 3.2908 | 92500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.3086 | 93000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.3263 | 93500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.3441 | 94000 | 0.0003 | 0.0003 | -0.04804208 | 0.9337 | -0.04647612 | 0.9432 | -0.041626267 | 0.9737 | 0.8102 |
| 3.3619 | 94500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.3797 | 95000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.3975 | 95500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.4153 | 96000 | 0.0003 | 0.0003 | -0.047956012 | 0.9349 | -0.046421595 | 0.9436 | -0.04157625 | 0.9745 | 0.8101 |
| 3.4331 | 96500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.4509 | 97000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.4686 | 97500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.4864 | 98000 | 0.0003 | 0.0003 | -0.04786362 | 0.9340 | -0.046320286 | 0.9440 | -0.04152808 | 0.9747 | 0.8075 |
| 3.5042 | 98500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.5220 | 99000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.5398 | 99500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.5576 | 100000 | 0.0003 | 0.0003 | -0.04788445 | 0.9331 | -0.04631946 | 0.9435 | -0.041442778 | 0.9738 | 0.8050 |
| 3.5754 | 100500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.5932 | 101000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.6110 | 101500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.6287 | 102000 | 0.0003 | 0.0003 | -0.047823686 | 0.9340 | -0.046267267 | 0.9434 | -0.041403364 | 0.9738 | 0.8120 |
| 3.6465 | 102500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.6643 | 103000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.6821 | 103500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.6999 | 104000 | 0.0003 | 0.0003 | -0.047792524 | 0.9339 | -0.046203755 | 0.9437 | -0.04136616 | 0.9744 | 0.8145 |
| 3.7177 | 104500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.7355 | 105000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.7533 | 105500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.7710 | 106000 | 0.0003 | 0.0003 | -0.047719087 | 0.9336 | -0.046151668 | 0.9434 | -0.04131788 | 0.9739 | 0.8092 |
| 3.7888 | 106500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.8066 | 107000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.8244 | 107500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.8422 | 108000 | 0.0003 | 0.0003 | -0.047663696 | 0.9340 | -0.046100874 | 0.9437 | -0.041290164 | 0.9743 | 0.8110 |
| 3.8600 | 108500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.8778 | 109000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.8956 | 109500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.9134 | 110000 | 0.0003 | 0.0003 | -0.04758864 | 0.9342 | -0.045986332 | 0.9441 | -0.04112862 | 0.9746 | 0.8107 |
| 3.9311 | 110500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.9489 | 111000 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.9667 | 111500 | 0.0003 | - | - | - | - | - | - | - | - |
| 3.9845 | 112000 | 0.0003 | 0.0003 | -0.047555514 | 0.9334 | -0.04596298 | 0.9442 | -0.041138608 | 0.9747 | 0.8179 |
| 4.0023 | 112500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.0201 | 113000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.0379 | 113500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.0556 | 114000 | 0.0003 | 0.0003 | -0.047533713 | 0.9337 | -0.045918535 | 0.9436 | -0.041128766 | 0.9738 | 0.8093 |
| 4.0734 | 114500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.0912 | 115000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.1090 | 115500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.1268 | 116000 | 0.0003 | 0.0003 | -0.047445226 | 0.9337 | -0.04587704 | 0.9437 | -0.04101509 | 0.9745 | 0.8124 |
| 4.1446 | 116500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.1624 | 117000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.1802 | 117500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.1979 | 118000 | 0.0003 | 0.0003 | -0.047467772 | 0.9333 | -0.04589099 | 0.944 | -0.04104045 | 0.9747 | 0.8118 |
| 4.2157 | 118500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.2335 | 119000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.2513 | 119500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.2691 | 120000 | 0.0003 | 0.0003 | -0.047381546 | 0.9332 | -0.045793895 | 0.944 | -0.040915057 | 0.974 | 0.8102 |
| 4.2869 | 120500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.3047 | 121000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.3225 | 121500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.3403 | 122000 | 0.0003 | 0.0003 | -0.047399823 | 0.9331 | -0.045784745 | 0.9444 | -0.040900033 | 0.9744 | 0.8168 |
| 4.3580 | 122500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.3758 | 123000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.3936 | 123500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.4114 | 124000 | 0.0003 | 0.0003 | -0.04735861 | 0.9335 | -0.045788392 | 0.9442 | -0.04084509 | 0.9746 | 0.8194 |
| 4.4292 | 124500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.4470 | 125000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.4648 | 125500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.4826 | 126000 | 0.0003 | 0.0003 | -0.04727602 | 0.9330 | -0.04571327 | 0.9435 | -0.04079213 | 0.9747 | 0.8191 |
| 4.5003 | 126500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.5181 | 127000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.5359 | 127500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.5537 | 128000 | 0.0003 | 0.0003 | -0.04724487 | 0.9328 | -0.045612063 | 0.9444 | -0.040754147 | 0.9748 | 0.8109 |
| 4.5715 | 128500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.5893 | 129000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.6071 | 129500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.6249 | 130000 | 0.0003 | 0.0003 | -0.047237676 | 0.9351 | -0.04558688 | 0.9440 | -0.040685352 | 0.9747 | 0.8180 |
| 4.6427 | 130500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.6604 | 131000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.6782 | 131500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.6960 | 132000 | 0.0003 | 0.0003 | -0.047139905 | 0.9355 | -0.04554107 | 0.9441 | -0.040673897 | 0.9751 | 0.8108 |
| 4.7138 | 132500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.7316 | 133000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.7494 | 133500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.7672 | 134000 | 0.0003 | 0.0003 | -0.047172163 | 0.9341 | -0.04553711 | 0.9432 | -0.04068736 | 0.9751 | 0.8122 |
| 4.7850 | 134500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.8027 | 135000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.8205 | 135500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.8383 | 136000 | 0.0003 | 0.0003 | -0.04712533 | 0.9348 | -0.045525618 | 0.9437 | -0.040563405 | 0.9747 | 0.8150 |
| 4.8561 | 136500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.8739 | 137000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.8917 | 137500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.9095 | 138000 | 0.0003 | 0.0003 | -0.047049224 | 0.9345 | -0.045493715 | 0.9433 | -0.040560503 | 0.9744 | 0.8134 |
| 4.9273 | 138500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.9451 | 139000 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.9628 | 139500 | 0.0003 | - | - | - | - | - | - | - | - |
| 4.9806 | 140000 | 0.0003 | 0.0003 | -0.047017988 | 0.9340 | -0.045397308 | 0.944 | -0.040537592 | 0.9745 | 0.8143 |
| 4.9984 | 140500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.0162 | 141000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.0340 | 141500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.0518 | 142000 | 0.0003 | 0.0003 | -0.047043595 | 0.9355 | -0.045408167 | 0.9443 | -0.040538292 | 0.9748 | 0.8144 |
| 5.0696 | 142500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.0873 | 143000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.1051 | 143500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.1229 | 144000 | 0.0003 | 0.0003 | -0.04705367 | 0.9355 | -0.045425817 | 0.9442 | -0.040479798 | 0.9747 | 0.8121 |
| 5.1407 | 144500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.1585 | 145000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.1763 | 145500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.1941 | 146000 | 0.0003 | 0.0003 | -0.046963915 | 0.9352 | -0.04530141 | 0.9442 | -0.04043464 | 0.9747 | 0.8117 |
| 5.2119 | 146500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.2296 | 147000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.2474 | 147500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.2652 | 148000 | 0.0003 | 0.0003 | -0.04690311 | 0.9348 | -0.045300145 | 0.9454 | -0.040396217 | 0.9757 | 0.8158 |
| 5.2830 | 148500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.3008 | 149000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.3186 | 149500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.3364 | 150000 | 0.0003 | 0.0003 | -0.04689872 | 0.9344 | -0.04525921 | 0.9444 | -0.040379316 | 0.9751 | 0.8148 |
| 5.3542 | 150500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.3720 | 151000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.3897 | 151500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.4075 | 152000 | 0.0003 | 0.0003 | -0.046854757 | 0.9349 | -0.04521157 | 0.9442 | -0.040353984 | 0.9747 | 0.8115 |
| 5.4253 | 152500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.4431 | 153000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.4609 | 153500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.4787 | 154000 | 0.0003 | 0.0003 | -0.046820126 | 0.9353 | -0.045219637 | 0.9447 | -0.04032354 | 0.975 | 0.8158 |
| 5.4965 | 154500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.5143 | 155000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.5320 | 155500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.5498 | 156000 | 0.0003 | 0.0003 | -0.046734486 | 0.9351 | -0.045157243 | 0.9443 | -0.04024718 | 0.9748 | 0.8164 |
| 5.5676 | 156500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.5854 | 157000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.6032 | 157500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.6210 | 158000 | 0.0003 | 0.0003 | -0.046766065 | 0.9356 | -0.045193 | 0.9448 | -0.04020705 | 0.9750 | 0.8161 |
| 5.6388 | 158500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.6566 | 159000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.6744 | 159500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.6921 | 160000 | 0.0003 | 0.0003 | -0.046653003 | 0.9349 | -0.045091264 | 0.9447 | -0.040132213 | 0.9752 | 0.8159 |
| 5.7099 | 160500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.7277 | 161000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.7455 | 161500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.7633 | 162000 | 0.0003 | 0.0003 | -0.046689577 | 0.9350 | -0.045103475 | 0.9439 | -0.040139068 | 0.9752 | 0.8148 |
| 5.7811 | 162500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.7989 | 163000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.8167 | 163500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.8344 | 164000 | 0.0003 | 0.0003 | -0.04665651 | 0.9354 | -0.04505335 | 0.9442 | -0.040089488 | 0.9745 | 0.8172 |
| 5.8522 | 164500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.8700 | 165000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.8878 | 165500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.9056 | 166000 | 0.0003 | 0.0003 | -0.046620764 | 0.9353 | -0.045048986 | 0.9445 | -0.04012648 | 0.9752 | 0.8171 |
| 5.9234 | 166500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.9412 | 167000 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.9590 | 167500 | 0.0003 | - | - | - | - | - | - | - | - |
| 5.9768 | 168000 | 0.0003 | 0.0003 | -0.046547405 | 0.9346 | -0.044982277 | 0.9449 | -0.03998514 | 0.9749 | 0.8148 |
| 5.9945 | 168500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.0123 | 169000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.0301 | 169500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.0479 | 170000 | 0.0003 | 0.0003 | -0.046575766 | 0.9353 | -0.044996176 | 0.9445 | -0.040046997 | 0.9748 | 0.8173 |
| 6.0657 | 170500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.0835 | 171000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.1013 | 171500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.1190 | 172000 | 0.0003 | 0.0003 | -0.04653697 | 0.9344 | -0.04489271 | 0.9442 | -0.039989993 | 0.9751 | 0.8117 |
| 6.1368 | 172500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.1546 | 173000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.1724 | 173500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.1902 | 174000 | 0.0003 | 0.0003 | -0.046540864 | 0.9354 | -0.044921882 | 0.9445 | -0.039948173 | 0.9753 | 0.8154 |
| 6.2080 | 174500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.2258 | 175000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.2436 | 175500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.2613 | 176000 | 0.0003 | 0.0003 | -0.046473563 | 0.935 | -0.044842277 | 0.9445 | -0.039841603 | 0.9749 | 0.8173 |
| 6.2791 | 176500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.2969 | 177000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.3147 | 177500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.3325 | 178000 | 0.0003 | 0.0003 | -0.046434972 | 0.9357 | -0.044857066 | 0.9446 | -0.03987878 | 0.9747 | 0.8137 |
| 6.3503 | 178500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.3681 | 179000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.3859 | 179500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.4037 | 180000 | 0.0003 | 0.0003 | -0.046446227 | 0.9348 | -0.044805177 | 0.9443 | -0.03994019 | 0.9748 | 0.8147 |
| 6.4214 | 180500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.4392 | 181000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.4570 | 181500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.4748 | 182000 | 0.0003 | 0.0003 | -0.046411477 | 0.9350 | -0.044835974 | 0.9449 | -0.039863113 | 0.9753 | 0.8177 |
| 6.4926 | 182500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.5104 | 183000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.5282 | 183500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.5460 | 184000 | 0.0003 | 0.0003 | -0.04641683 | 0.9354 | -0.04482221 | 0.9449 | -0.039854627 | 0.9752 | 0.8135 |
| 6.5637 | 184500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.5815 | 185000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.5993 | 185500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.6171 | 186000 | 0.0003 | 0.0003 | -0.04635241 | 0.9343 | -0.044773895 | 0.9448 | -0.039753385 | 0.9750 | 0.8183 |
| 6.6349 | 186500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.6527 | 187000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.6705 | 187500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.6883 | 188000 | 0.0003 | 0.0003 | -0.046331976 | 0.9351 | -0.044707794 | 0.9445 | -0.039761487 | 0.9752 | 0.8197 |
| 6.7060 | 188500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.7238 | 189000 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.7416 | 189500 | 0.0003 | - | - | - | - | - | - | - | - |
| 6.7594 | 190000 | 0.0003 | 0.0003 | -0.046344783 | 0.9355 | -0.04470515 | 0.9448 | -0.03973746 | 0.9758 | 0.8171 |