---
base_model: sentence-transformers/all-MiniLM-L6-v2
datasets: []
language: []
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:724
- loss:CoSENTLoss
widget:
- source_sentence: Financials
sentences:
- What is the financial performance of ABC?
- What companies operate in the same space as ABC?
- What standards are used to evaluate the industry?
- source_sentence: Research
sentences:
- What recent studies have been conducted on ABC?
- What are the key factors considered in rating ABC?
- How is the rating framework applied to the sector?
- source_sentence: Criteria
sentences:
- What are the projected economic impacts of inflation on the technology industry?
- What is the process for assessing the creditworthiness of ABC?
- What are the primary ESG challenges faced by ABC?
- source_sentence: Financials
sentences:
- Can you list the strengths and weaknesses of ABC?
- What is understood by the term sovereign risk?
- Can you provide the financial history of ABC?
- source_sentence: Research
sentences:
- What macroeconomic trends are influencing the credit ratings of the automotive
industry?
- Who are the main rivals of ABC?
- Can you provide the latest research insights on ABC?
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: .nan
name: Pearson Cosine
- type: spearman_cosine
value: .nan
name: Spearman Cosine
- type: pearson_manhattan
value: .nan
name: Pearson Manhattan
- type: spearman_manhattan
value: .nan
name: Spearman Manhattan
- type: pearson_euclidean
value: .nan
name: Pearson Euclidean
- type: spearman_euclidean
value: .nan
name: Spearman Euclidean
- type: pearson_dot
value: .nan
name: Pearson Dot
- type: spearman_dot
value: .nan
name: Spearman Dot
- type: pearson_max
value: .nan
name: Pearson Max
- type: spearman_max
value: .nan
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("ManishThota/QueryRouter")
# Run inference
sentences = [
'Research',
'Can you provide the latest research insights on ABC?',
'Who are the main rivals of ABC?',
]
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
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:--------|
| pearson_cosine | nan |
| **spearman_cosine** | **nan** |
| pearson_manhattan | nan |
| spearman_manhattan | nan |
| pearson_euclidean | nan |
| spearman_euclidean | nan |
| pearson_dot | nan |
| spearman_dot | nan |
| pearson_max | nan |
| spearman_max | nan |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 724 training samples
* Columns: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | - min: 3 tokens
- mean: 3.27 tokens
- max: 4 tokens
| - min: 9 tokens
- mean: 14.23 tokens
- max: 29 tokens
| - min: 1.0
- mean: 1.0
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:--------------------|:-------------------------------------------------|:-----------------|
| Rating | What rating does XYZ have? | 1.0 |
| Rating | Can you provide the rating for XYZ? | 1.0 |
| Rating | How is XYZ rated? | 1.0 |
* Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 60 evaluation samples
* Columns: sentence1, sentence2, and score
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | - min: 3 tokens
- mean: 3.25 tokens
- max: 4 tokens
| - min: 9 tokens
- mean: 12.48 tokens
- max: 20 tokens
| - min: 1.0
- mean: 1.0
- max: 1.0
|
* Samples:
| sentence1 | sentence2 | score |
|:--------------------|:-------------------------------------------------|:-----------------|
| Rating | What is the current rating of ABC? | 1.0 |
| Rating | Can you tell me the rating for ABC? | 1.0 |
| Rating | What rating has ABC been assigned? | 1.0 |
* Loss: [CoSENTLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `warmup_ratio`: 0.1
- `save_only_model`: True
- `seed`: 33
- `fp16`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `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`: True
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 33
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
|:----------:|:-------:|:-------------:|:-------:|:-----------------------:|
| 0.0220 | 2 | - | 0.0 | nan |
| 0.0440 | 4 | - | 0.0 | nan |
| 0.0659 | 6 | - | 0.0 | nan |
| 0.0879 | 8 | - | 0.0 | nan |
| 0.1099 | 10 | - | 0.0 | nan |
| 0.1319 | 12 | - | 0.0 | nan |
| 0.1538 | 14 | - | 0.0 | nan |
| 0.1758 | 16 | - | 0.0 | nan |
| 0.1978 | 18 | - | 0.0 | nan |
| 0.2198 | 20 | - | 0.0 | nan |
| 0.2418 | 22 | - | 0.0 | nan |
| 0.2637 | 24 | - | 0.0 | nan |
| 0.2857 | 26 | - | 0.0 | nan |
| 0.3077 | 28 | - | 0.0 | nan |
| 0.3297 | 30 | - | 0.0 | nan |
| 0.3516 | 32 | - | 0.0 | nan |
| 0.3736 | 34 | - | 0.0 | nan |
| 0.3956 | 36 | - | 0.0 | nan |
| 0.4176 | 38 | - | 0.0 | nan |
| 0.4396 | 40 | - | 0.0 | nan |
| 0.4615 | 42 | - | 0.0 | nan |
| 0.4835 | 44 | - | 0.0 | nan |
| 0.5055 | 46 | - | 0.0 | nan |
| 0.5275 | 48 | - | 0.0 | nan |
| 0.5495 | 50 | - | 0.0 | nan |
| 0.5714 | 52 | - | 0.0 | nan |
| 0.5934 | 54 | - | 0.0 | nan |
| 0.6154 | 56 | - | 0.0 | nan |
| 0.6374 | 58 | - | 0.0 | nan |
| 0.6593 | 60 | - | 0.0 | nan |
| 0.6813 | 62 | - | 0.0 | nan |
| 0.7033 | 64 | - | 0.0 | nan |
| 0.7253 | 66 | - | 0.0 | nan |
| 0.7473 | 68 | - | 0.0 | nan |
| 0.7692 | 70 | - | 0.0 | nan |
| 0.7912 | 72 | - | 0.0 | nan |
| 0.8132 | 74 | - | 0.0 | nan |
| 0.8352 | 76 | - | 0.0 | nan |
| 0.8571 | 78 | - | 0.0 | nan |
| 0.8791 | 80 | - | 0.0 | nan |
| 0.9011 | 82 | - | 0.0 | nan |
| 0.9231 | 84 | - | 0.0 | nan |
| 0.9451 | 86 | - | 0.0 | nan |
| 0.9670 | 88 | - | 0.0 | nan |
| 0.9890 | 90 | - | 0.0 | nan |
| 1.0110 | 92 | - | 0.0 | nan |
| 1.0330 | 94 | - | 0.0 | nan |
| 1.0549 | 96 | - | 0.0 | nan |
| 1.0769 | 98 | - | 0.0 | nan |
| 1.0989 | 100 | - | 0.0 | nan |
| 1.1209 | 102 | - | 0.0 | nan |
| 1.1429 | 104 | - | 0.0 | nan |
| 1.1648 | 106 | - | 0.0 | nan |
| 1.1868 | 108 | - | 0.0 | nan |
| 1.2088 | 110 | - | 0.0 | nan |
| 1.2308 | 112 | - | 0.0 | nan |
| 1.2527 | 114 | - | 0.0 | nan |
| 1.2747 | 116 | - | 0.0 | nan |
| 1.2967 | 118 | - | 0.0 | nan |
| 1.3187 | 120 | - | 0.0 | nan |
| 1.3407 | 122 | - | 0.0 | nan |
| 1.3626 | 124 | - | 0.0 | nan |
| 1.3846 | 126 | - | 0.0 | nan |
| 1.4066 | 128 | - | 0.0 | nan |
| 1.4286 | 130 | - | 0.0 | nan |
| 1.4505 | 132 | - | 0.0 | nan |
| 1.4725 | 134 | - | 0.0 | nan |
| 1.4945 | 136 | - | 0.0 | nan |
| 1.5165 | 138 | - | 0.0 | nan |
| 1.5385 | 140 | - | 0.0 | nan |
| 1.5604 | 142 | - | 0.0 | nan |
| 1.5824 | 144 | - | 0.0 | nan |
| 1.6044 | 146 | - | 0.0 | nan |
| 1.6264 | 148 | - | 0.0 | nan |
| 1.6484 | 150 | - | 0.0 | nan |
| 1.6703 | 152 | - | 0.0 | nan |
| 1.6923 | 154 | - | 0.0 | nan |
| 1.7143 | 156 | - | 0.0 | nan |
| 1.7363 | 158 | - | 0.0 | nan |
| 1.7582 | 160 | - | 0.0 | nan |
| 1.7802 | 162 | - | 0.0 | nan |
| 1.8022 | 164 | - | 0.0 | nan |
| 1.8242 | 166 | - | 0.0 | nan |
| 1.8462 | 168 | - | 0.0 | nan |
| 1.8681 | 170 | - | 0.0 | nan |
| 1.8901 | 172 | - | 0.0 | nan |
| 1.9121 | 174 | - | 0.0 | nan |
| 1.9341 | 176 | - | 0.0 | nan |
| 1.9560 | 178 | - | 0.0 | nan |
| 1.9780 | 180 | - | 0.0 | nan |
| 2.0 | 182 | - | 0.0 | nan |
| 2.0220 | 184 | - | 0.0 | nan |
| 2.0440 | 186 | - | 0.0 | nan |
| 2.0659 | 188 | - | 0.0 | nan |
| 2.0879 | 190 | - | 0.0 | nan |
| 2.1099 | 192 | - | 0.0 | nan |
| 2.1319 | 194 | - | 0.0 | nan |
| 2.1538 | 196 | - | 0.0 | nan |
| 2.1758 | 198 | - | 0.0 | nan |
| 2.1978 | 200 | - | 0.0 | nan |
| 2.2198 | 202 | - | 0.0 | nan |
| 2.2418 | 204 | - | 0.0 | nan |
| 2.2637 | 206 | - | 0.0 | nan |
| 2.2857 | 208 | - | 0.0 | nan |
| 2.3077 | 210 | - | 0.0 | nan |
| 2.3297 | 212 | - | 0.0 | nan |
| 2.3516 | 214 | - | 0.0 | nan |
| 2.3736 | 216 | - | 0.0 | nan |
| 2.3956 | 218 | - | 0.0 | nan |
| 2.4176 | 220 | - | 0.0 | nan |
| 2.4396 | 222 | - | 0.0 | nan |
| 2.4615 | 224 | - | 0.0 | nan |
| 2.4835 | 226 | - | 0.0 | nan |
| 2.5055 | 228 | - | 0.0 | nan |
| 2.5275 | 230 | - | 0.0 | nan |
| 2.5495 | 232 | - | 0.0 | nan |
| 2.5714 | 234 | - | 0.0 | nan |
| 2.5934 | 236 | - | 0.0 | nan |
| 2.6154 | 238 | - | 0.0 | nan |
| 2.6374 | 240 | - | 0.0 | nan |
| 2.6593 | 242 | - | 0.0 | nan |
| 2.6813 | 244 | - | 0.0 | nan |
| 2.7033 | 246 | - | 0.0 | nan |
| 2.7253 | 248 | - | 0.0 | nan |
| 2.7473 | 250 | - | 0.0 | nan |
| 2.7692 | 252 | - | 0.0 | nan |
| 2.7912 | 254 | - | 0.0 | nan |
| 2.8132 | 256 | - | 0.0 | nan |
| 2.8352 | 258 | - | 0.0 | nan |
| 2.8571 | 260 | - | 0.0 | nan |
| 2.8791 | 262 | - | 0.0 | nan |
| 2.9011 | 264 | - | 0.0 | nan |
| 2.9231 | 266 | - | 0.0 | nan |
| 2.9451 | 268 | - | 0.0 | nan |
| 2.9670 | 270 | - | 0.0 | nan |
| 2.9890 | 272 | - | 0.0 | nan |
| 3.0110 | 274 | - | 0.0 | nan |
| 3.0330 | 276 | - | 0.0 | nan |
| 3.0549 | 278 | - | 0.0 | nan |
| 3.0769 | 280 | - | 0.0 | nan |
| 3.0989 | 282 | - | 0.0 | nan |
| 3.1209 | 284 | - | 0.0 | nan |
| 3.1429 | 286 | - | 0.0 | nan |
| 3.1648 | 288 | - | 0.0 | nan |
| 3.1868 | 290 | - | 0.0 | nan |
| 3.2088 | 292 | - | 0.0 | nan |
| 3.2308 | 294 | - | 0.0 | nan |
| 3.2527 | 296 | - | 0.0 | nan |
| 3.2747 | 298 | - | 0.0 | nan |
| 3.2967 | 300 | - | 0.0 | nan |
| 3.3187 | 302 | - | 0.0 | nan |
| 3.3407 | 304 | - | 0.0 | nan |
| 3.3626 | 306 | - | 0.0 | nan |
| 3.3846 | 308 | - | 0.0 | nan |
| 3.4066 | 310 | - | 0.0 | nan |
| 3.4286 | 312 | - | 0.0 | nan |
| 3.4505 | 314 | - | 0.0 | nan |
| 3.4725 | 316 | - | 0.0 | nan |
| 3.4945 | 318 | - | 0.0 | nan |
| 3.5165 | 320 | - | 0.0 | nan |
| 3.5385 | 322 | - | 0.0 | nan |
| 3.5604 | 324 | - | 0.0 | nan |
| 3.5824 | 326 | - | 0.0 | nan |
| 3.6044 | 328 | - | 0.0 | nan |
| 3.6264 | 330 | - | 0.0 | nan |
| 3.6484 | 332 | - | 0.0 | nan |
| 3.6703 | 334 | - | 0.0 | nan |
| 3.6923 | 336 | - | 0.0 | nan |
| 3.7143 | 338 | - | 0.0 | nan |
| 3.7363 | 340 | - | 0.0 | nan |
| 3.7582 | 342 | - | 0.0 | nan |
| 3.7802 | 344 | - | 0.0 | nan |
| 3.8022 | 346 | - | 0.0 | nan |
| 3.8242 | 348 | - | 0.0 | nan |
| 3.8462 | 350 | - | 0.0 | nan |
| 3.8681 | 352 | - | 0.0 | nan |
| 3.8901 | 354 | - | 0.0 | nan |
| 3.9121 | 356 | - | 0.0 | nan |
| 3.9341 | 358 | - | 0.0 | nan |
| 3.9560 | 360 | - | 0.0 | nan |
| 3.9780 | 362 | - | 0.0 | nan |
| 4.0 | 364 | - | 0.0 | nan |
| 4.0220 | 366 | - | 0.0 | nan |
| 4.0440 | 368 | - | 0.0 | nan |
| 4.0659 | 370 | - | 0.0 | nan |
| 4.0879 | 372 | - | 0.0 | nan |
| 4.1099 | 374 | - | 0.0 | nan |
| 4.1319 | 376 | - | 0.0 | nan |
| 4.1538 | 378 | - | 0.0 | nan |
| 4.1758 | 380 | - | 0.0 | nan |
| 4.1978 | 382 | - | 0.0 | nan |
| 4.2198 | 384 | - | 0.0 | nan |
| 4.2418 | 386 | - | 0.0 | nan |
| 4.2637 | 388 | - | 0.0 | nan |
| 4.2857 | 390 | - | 0.0 | nan |
| 4.3077 | 392 | - | 0.0 | nan |
| 4.3297 | 394 | - | 0.0 | nan |
| 4.3516 | 396 | - | 0.0 | nan |
| 4.3736 | 398 | - | 0.0 | nan |
| 4.3956 | 400 | - | 0.0 | nan |
| 4.4176 | 402 | - | 0.0 | nan |
| 4.4396 | 404 | - | 0.0 | nan |
| 4.4615 | 406 | - | 0.0 | nan |
| 4.4835 | 408 | - | 0.0 | nan |
| 4.5055 | 410 | - | 0.0 | nan |
| 4.5275 | 412 | - | 0.0 | nan |
| 4.5495 | 414 | - | 0.0 | nan |
| 4.5714 | 416 | - | 0.0 | nan |
| 4.5934 | 418 | - | 0.0 | nan |
| 4.6154 | 420 | - | 0.0 | nan |
| 4.6374 | 422 | - | 0.0 | nan |
| 4.6593 | 424 | - | 0.0 | nan |
| 4.6813 | 426 | - | 0.0 | nan |
| 4.7033 | 428 | - | 0.0 | nan |
| 4.7253 | 430 | - | 0.0 | nan |
| 4.7473 | 432 | - | 0.0 | nan |
| 4.7692 | 434 | - | 0.0 | nan |
| 4.7912 | 436 | - | 0.0 | nan |
| 4.8132 | 438 | - | 0.0 | nan |
| 4.8352 | 440 | - | 0.0 | nan |
| 4.8571 | 442 | - | 0.0 | nan |
| 4.8791 | 444 | - | 0.0 | nan |
| 4.9011 | 446 | - | 0.0 | nan |
| 4.9231 | 448 | - | 0.0 | nan |
| 4.9451 | 450 | - | 0.0 | nan |
| 4.9670 | 452 | - | 0.0 | nan |
| 4.9890 | 454 | - | 0.0 | nan |
| 5.0110 | 456 | - | 0.0 | nan |
| 5.0330 | 458 | - | 0.0 | nan |
| 5.0549 | 460 | - | 0.0 | nan |
| 5.0769 | 462 | - | 0.0 | nan |
| 5.0989 | 464 | - | 0.0 | nan |
| 5.1209 | 466 | - | 0.0 | nan |
| 5.1429 | 468 | - | 0.0 | nan |
| 5.1648 | 470 | - | 0.0 | nan |
| 5.1868 | 472 | - | 0.0 | nan |
| 5.2088 | 474 | - | 0.0 | nan |
| 5.2308 | 476 | - | 0.0 | nan |
| 5.2527 | 478 | - | 0.0 | nan |
| 5.2747 | 480 | - | 0.0 | nan |
| 5.2967 | 482 | - | 0.0 | nan |
| 5.3187 | 484 | - | 0.0 | nan |
| 5.3407 | 486 | - | 0.0 | nan |
| 5.3626 | 488 | - | 0.0 | nan |
| 5.3846 | 490 | - | 0.0 | nan |
| 5.4066 | 492 | - | 0.0 | nan |
| 5.4286 | 494 | - | 0.0 | nan |
| 5.4505 | 496 | - | 0.0 | nan |
| 5.4725 | 498 | - | 0.0 | nan |
| **5.4945** | **500** | **0.0** | **0.0** | **nan** |
| 5.5165 | 502 | - | 0.0 | nan |
| 5.5385 | 504 | - | 0.0 | nan |
| 5.5604 | 506 | - | 0.0 | nan |
| 5.5824 | 508 | - | 0.0 | nan |
| 5.6044 | 510 | - | 0.0 | nan |
| 5.6264 | 512 | - | 0.0 | nan |
| 5.6484 | 514 | - | 0.0 | nan |
| 5.6703 | 516 | - | 0.0 | nan |
| 5.6923 | 518 | - | 0.0 | nan |
| 5.7143 | 520 | - | 0.0 | nan |
| 5.7363 | 522 | - | 0.0 | nan |
| 5.7582 | 524 | - | 0.0 | nan |
| 5.7802 | 526 | - | 0.0 | nan |
| 5.8022 | 528 | - | 0.0 | nan |
| 5.8242 | 530 | - | 0.0 | nan |
| 5.8462 | 532 | - | 0.0 | nan |
| 5.8681 | 534 | - | 0.0 | nan |
| 5.8901 | 536 | - | 0.0 | nan |
| 5.9121 | 538 | - | 0.0 | nan |
| 5.9341 | 540 | - | 0.0 | nan |
| 5.9560 | 542 | - | 0.0 | nan |
| 5.9780 | 544 | - | 0.0 | nan |
| 6.0 | 546 | - | 0.0 | nan |
| 6.0220 | 548 | - | 0.0 | nan |
| 6.0440 | 550 | - | 0.0 | nan |
| 6.0659 | 552 | - | 0.0 | nan |
| 6.0879 | 554 | - | 0.0 | nan |
| 6.1099 | 556 | - | 0.0 | nan |
| 6.1319 | 558 | - | 0.0 | nan |
| 6.1538 | 560 | - | 0.0 | nan |
| 6.1758 | 562 | - | 0.0 | nan |
| 6.1978 | 564 | - | 0.0 | nan |
| 6.2198 | 566 | - | 0.0 | nan |
| 6.2418 | 568 | - | 0.0 | nan |
| 6.2637 | 570 | - | 0.0 | nan |
| 6.2857 | 572 | - | 0.0 | nan |
| 6.3077 | 574 | - | 0.0 | nan |
| 6.3297 | 576 | - | 0.0 | nan |
| 6.3516 | 578 | - | 0.0 | nan |
| 6.3736 | 580 | - | 0.0 | nan |
| 6.3956 | 582 | - | 0.0 | nan |
| 6.4176 | 584 | - | 0.0 | nan |
| 6.4396 | 586 | - | 0.0 | nan |
| 6.4615 | 588 | - | 0.0 | nan |
| 6.4835 | 590 | - | 0.0 | nan |
| 6.5055 | 592 | - | 0.0 | nan |
| 6.5275 | 594 | - | 0.0 | nan |
| 6.5495 | 596 | - | 0.0 | nan |
| 6.5714 | 598 | - | 0.0 | nan |
| 6.5934 | 600 | - | 0.0 | nan |
| 6.6154 | 602 | - | 0.0 | nan |
| 6.6374 | 604 | - | 0.0 | nan |
| 6.6593 | 606 | - | 0.0 | nan |
| 6.6813 | 608 | - | 0.0 | nan |
| 6.7033 | 610 | - | 0.0 | nan |
| 6.7253 | 612 | - | 0.0 | nan |
| 6.7473 | 614 | - | 0.0 | nan |
| 6.7692 | 616 | - | 0.0 | nan |
| 6.7912 | 618 | - | 0.0 | nan |
| 6.8132 | 620 | - | 0.0 | nan |
| 6.8352 | 622 | - | 0.0 | nan |
| 6.8571 | 624 | - | 0.0 | nan |
| 6.8791 | 626 | - | 0.0 | nan |
| 6.9011 | 628 | - | 0.0 | nan |
| 6.9231 | 630 | - | 0.0 | nan |
| 6.9451 | 632 | - | 0.0 | nan |
| 6.9670 | 634 | - | 0.0 | nan |
| 6.9890 | 636 | - | 0.0 | nan |
| 7.0110 | 638 | - | 0.0 | nan |
| 7.0330 | 640 | - | 0.0 | nan |
| 7.0549 | 642 | - | 0.0 | nan |
| 7.0769 | 644 | - | 0.0 | nan |
| 7.0989 | 646 | - | 0.0 | nan |
| 7.1209 | 648 | - | 0.0 | nan |
| 7.1429 | 650 | - | 0.0 | nan |
| 7.1648 | 652 | - | 0.0 | nan |
| 7.1868 | 654 | - | 0.0 | nan |
| 7.2088 | 656 | - | 0.0 | nan |
| 7.2308 | 658 | - | 0.0 | nan |
| 7.2527 | 660 | - | 0.0 | nan |
| 7.2747 | 662 | - | 0.0 | nan |
| 7.2967 | 664 | - | 0.0 | nan |
| 7.3187 | 666 | - | 0.0 | nan |
| 7.3407 | 668 | - | 0.0 | nan |
| 7.3626 | 670 | - | 0.0 | nan |
| 7.3846 | 672 | - | 0.0 | nan |
| 7.4066 | 674 | - | 0.0 | nan |
| 7.4286 | 676 | - | 0.0 | nan |
| 7.4505 | 678 | - | 0.0 | nan |
| 7.4725 | 680 | - | 0.0 | nan |
| 7.4945 | 682 | - | 0.0 | nan |
| 7.5165 | 684 | - | 0.0 | nan |
| 7.5385 | 686 | - | 0.0 | nan |
| 7.5604 | 688 | - | 0.0 | nan |
| 7.5824 | 690 | - | 0.0 | nan |
| 7.6044 | 692 | - | 0.0 | nan |
| 7.6264 | 694 | - | 0.0 | nan |
| 7.6484 | 696 | - | 0.0 | nan |
| 7.6703 | 698 | - | 0.0 | nan |
| 7.6923 | 700 | - | 0.0 | nan |
| 7.7143 | 702 | - | 0.0 | nan |
| 7.7363 | 704 | - | 0.0 | nan |
| 7.7582 | 706 | - | 0.0 | nan |
| 7.7802 | 708 | - | 0.0 | nan |
| 7.8022 | 710 | - | 0.0 | nan |
| 7.8242 | 712 | - | 0.0 | nan |
| 7.8462 | 714 | - | 0.0 | nan |
| 7.8681 | 716 | - | 0.0 | nan |
| 7.8901 | 718 | - | 0.0 | nan |
| 7.9121 | 720 | - | 0.0 | nan |
| 7.9341 | 722 | - | 0.0 | nan |
| 7.9560 | 724 | - | 0.0 | nan |
| 7.9780 | 726 | - | 0.0 | nan |
| 8.0 | 728 | - | 0.0 | nan |
| 8.0220 | 730 | - | 0.0 | nan |
| 8.0440 | 732 | - | 0.0 | nan |
| 8.0659 | 734 | - | 0.0 | nan |
| 8.0879 | 736 | - | 0.0 | nan |
| 8.1099 | 738 | - | 0.0 | nan |
| 8.1319 | 740 | - | 0.0 | nan |
| 8.1538 | 742 | - | 0.0 | nan |
| 8.1758 | 744 | - | 0.0 | nan |
| 8.1978 | 746 | - | 0.0 | nan |
| 8.2198 | 748 | - | 0.0 | nan |
| 8.2418 | 750 | - | 0.0 | nan |
| 8.2637 | 752 | - | 0.0 | nan |
| 8.2857 | 754 | - | 0.0 | nan |
| 8.3077 | 756 | - | 0.0 | nan |
| 8.3297 | 758 | - | 0.0 | nan |
| 8.3516 | 760 | - | 0.0 | nan |
| 8.3736 | 762 | - | 0.0 | nan |
| 8.3956 | 764 | - | 0.0 | nan |
| 8.4176 | 766 | - | 0.0 | nan |
| 8.4396 | 768 | - | 0.0 | nan |
| 8.4615 | 770 | - | 0.0 | nan |
| 8.4835 | 772 | - | 0.0 | nan |
| 8.5055 | 774 | - | 0.0 | nan |
| 8.5275 | 776 | - | 0.0 | nan |
| 8.5495 | 778 | - | 0.0 | nan |
| 8.5714 | 780 | - | 0.0 | nan |
| 8.5934 | 782 | - | 0.0 | nan |
| 8.6154 | 784 | - | 0.0 | nan |
| 8.6374 | 786 | - | 0.0 | nan |
| 8.6593 | 788 | - | 0.0 | nan |
| 8.6813 | 790 | - | 0.0 | nan |
| 8.7033 | 792 | - | 0.0 | nan |
| 8.7253 | 794 | - | 0.0 | nan |
| 8.7473 | 796 | - | 0.0 | nan |
| 8.7692 | 798 | - | 0.0 | nan |
| 8.7912 | 800 | - | 0.0 | nan |
| 8.8132 | 802 | - | 0.0 | nan |
| 8.8352 | 804 | - | 0.0 | nan |
| 8.8571 | 806 | - | 0.0 | nan |
| 8.8791 | 808 | - | 0.0 | nan |
| 8.9011 | 810 | - | 0.0 | nan |
| 8.9231 | 812 | - | 0.0 | nan |
| 8.9451 | 814 | - | 0.0 | nan |
| 8.9670 | 816 | - | 0.0 | nan |
| 8.9890 | 818 | - | 0.0 | nan |
| 9.0110 | 820 | - | 0.0 | nan |
| 9.0330 | 822 | - | 0.0 | nan |
| 9.0549 | 824 | - | 0.0 | nan |
| 9.0769 | 826 | - | 0.0 | nan |
| 9.0989 | 828 | - | 0.0 | nan |
| 9.1209 | 830 | - | 0.0 | nan |
| 9.1429 | 832 | - | 0.0 | nan |
| 9.1648 | 834 | - | 0.0 | nan |
| 9.1868 | 836 | - | 0.0 | nan |
| 9.2088 | 838 | - | 0.0 | nan |
| 9.2308 | 840 | - | 0.0 | nan |
| 9.2527 | 842 | - | 0.0 | nan |
| 9.2747 | 844 | - | 0.0 | nan |
| 9.2967 | 846 | - | 0.0 | nan |
| 9.3187 | 848 | - | 0.0 | nan |
| 9.3407 | 850 | - | 0.0 | nan |
| 9.3626 | 852 | - | 0.0 | nan |
| 9.3846 | 854 | - | 0.0 | nan |
| 9.4066 | 856 | - | 0.0 | nan |
| 9.4286 | 858 | - | 0.0 | nan |
| 9.4505 | 860 | - | 0.0 | nan |
| 9.4725 | 862 | - | 0.0 | nan |
| 9.4945 | 864 | - | 0.0 | nan |
| 9.5165 | 866 | - | 0.0 | nan |
| 9.5385 | 868 | - | 0.0 | nan |
| 9.5604 | 870 | - | 0.0 | nan |
| 9.5824 | 872 | - | 0.0 | nan |
| 9.6044 | 874 | - | 0.0 | nan |
| 9.6264 | 876 | - | 0.0 | nan |
| 9.6484 | 878 | - | 0.0 | nan |
| 9.6703 | 880 | - | 0.0 | nan |
| 9.6923 | 882 | - | 0.0 | nan |
| 9.7143 | 884 | - | 0.0 | nan |
| 9.7363 | 886 | - | 0.0 | nan |
| 9.7582 | 888 | - | 0.0 | nan |
| 9.7802 | 890 | - | 0.0 | nan |
| 9.8022 | 892 | - | 0.0 | nan |
| 9.8242 | 894 | - | 0.0 | nan |
| 9.8462 | 896 | - | 0.0 | nan |
| 9.8681 | 898 | - | 0.0 | nan |
| 9.8901 | 900 | - | 0.0 | nan |
| 9.9121 | 902 | - | 0.0 | nan |
| 9.9341 | 904 | - | 0.0 | nan |
| 9.9560 | 906 | - | 0.0 | nan |
| 9.9780 | 908 | - | 0.0 | nan |
| 10.0 | 910 | - | 0.0 | nan |
* The bold row denotes the saved checkpoint.