---
language: []
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1793370
- loss:CoSENTLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
datasets: []
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: ek wil bietjie moderne rock hoor
sentences:
- request datetime
- turn wemo on
- query cooking
- source_sentence: skakel af die alarm vir woensdag ses v. m.
sentences:
- set alarm
- turn hue light up
- request weather
- source_sentence: speel my top-gegradeerde pop liedjies asseblief
sentences:
- greeting
- request fact
- request datetime
- source_sentence: is dit warm buite
sentences:
- request weather
- play music
- request transport
- source_sentence: maak 'n speellys van al die eminem liedjies en speel dit met skommel
sentences:
- search recipe
- recommend movie
- play music
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: MiniLM dev
type: MiniLM-dev
metrics:
- type: pearson_cosine
value: 0.807743120621169
name: Pearson Cosine
- type: spearman_cosine
value: 0.8111451989044506
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8090992313100879
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8112673840020295
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8107892143621067
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8137277702128023
name: Spearman Euclidean
- type: pearson_dot
value: 0.7013144883870261
name: Pearson Dot
- type: spearman_dot
value: 0.7113684320495312
name: Spearman Dot
- type: pearson_max
value: 0.8107892143621067
name: Pearson Max
- type: spearman_max
value: 0.8137277702128023
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)
- **Maximum Sequence Length:** 128 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': 128, '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("philipp-zettl/MiniLM-amazon_massive_intent-similarity")
# Run inference
sentences = [
"maak 'n speellys van al die eminem liedjies en speel dit met skommel",
'play music',
'recommend movie',
]
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: `MiniLM-dev`
* Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8077 |
| **spearman_cosine** | **0.8111** |
| pearson_manhattan | 0.8091 |
| spearman_manhattan | 0.8113 |
| pearson_euclidean | 0.8108 |
| spearman_euclidean | 0.8137 |
| pearson_dot | 0.7013 |
| spearman_dot | 0.7114 |
| pearson_max | 0.8108 |
| spearman_max | 0.8137 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `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`: 1
- `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`: 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`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | loss | MiniLM-dev_spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:--------------------------:|
| 0.0018 | 100 | 10.7509 | - | - |
| 0.0036 | 200 | 9.8726 | - | - |
| 0.0054 | 300 | 8.9837 | - | - |
| 0.0071 | 400 | 7.3162 | - | - |
| 0.0089 | 500 | 8.2842 | - | - |
| 0.0107 | 600 | 6.2254 | - | - |
| 0.0125 | 700 | 6.1004 | - | - |
| 0.0143 | 800 | 5.8583 | - | - |
| 0.0161 | 900 | 6.3118 | - | - |
| 0.0178 | 1000 | 5.7908 | 2.6141 | 0.4045 |
| 0.0196 | 1100 | 5.6907 | - | - |
| 0.0214 | 1200 | 5.6743 | - | - |
| 0.0232 | 1300 | 5.5022 | - | - |
| 0.0250 | 1400 | 5.0283 | - | - |
| 0.0268 | 1500 | 5.2936 | - | - |
| 0.0285 | 1600 | 5.2928 | - | - |
| 0.0303 | 1700 | 5.5088 | - | - |
| 0.0321 | 1800 | 5.3125 | - | - |
| 0.0339 | 1900 | 5.7931 | - | - |
| 0.0357 | 2000 | 5.5979 | 2.3256 | 0.5075 |
| 0.0375 | 2100 | 5.3222 | - | - |
| 0.0393 | 2200 | 5.268 | - | - |
| 0.0410 | 2300 | 5.264 | - | - |
| 0.0428 | 2400 | 4.9437 | - | - |
| 0.0446 | 2500 | 4.9219 | - | - |
| 0.0464 | 2600 | 4.8656 | - | - |
| 0.0482 | 2700 | 5.2733 | - | - |
| 0.0500 | 2800 | 5.0311 | - | - |
| 0.0517 | 2900 | 5.302 | - | - |
| 0.0535 | 3000 | 5.3347 | 2.1545 | 0.6496 |
| 0.0553 | 3100 | 5.1241 | - | - |
| 0.0571 | 3200 | 5.0232 | - | - |
| 0.0589 | 3300 | 4.9932 | - | - |
| 0.0607 | 3400 | 4.9651 | - | - |
| 0.0625 | 3500 | 4.5226 | - | - |
| 0.0642 | 3600 | 4.6666 | - | - |
| 0.0660 | 3700 | 4.8979 | - | - |
| 0.0678 | 3800 | 4.9139 | - | - |
| 0.0696 | 3900 | 4.9241 | - | - |
| 0.0714 | 4000 | 5.2878 | 2.1118 | 0.6948 |
| 0.0732 | 4100 | 5.0776 | - | - |
| 0.0749 | 4200 | 4.934 | - | - |
| 0.0767 | 4300 | 4.9012 | - | - |
| 0.0785 | 4400 | 4.8835 | - | - |
| 0.0803 | 4500 | 4.5886 | - | - |
| 0.0821 | 4600 | 4.7829 | - | - |
| 0.0839 | 4700 | 4.8057 | - | - |
| 0.0856 | 4800 | 4.8761 | - | - |
| 0.0874 | 4900 | 4.6787 | - | - |
| 0.0892 | 5000 | 5.313 | 2.1114 | 0.6770 |
| 0.0910 | 5100 | 5.3036 | - | - |
| 0.0928 | 5200 | 5.0731 | - | - |
| 0.0946 | 5300 | 5.0052 | - | - |
| 0.0964 | 5400 | 4.9494 | - | - |
| 0.0981 | 5500 | 4.836 | - | - |
| 0.0999 | 5600 | 4.6319 | - | - |
| 0.1017 | 5700 | 4.667 | - | - |
| 0.1035 | 5800 | 4.9578 | - | - |
| 0.1053 | 5900 | 4.9473 | - | - |
| 0.1071 | 6000 | 4.9897 | 3.0813 | 0.4424 |
| 0.1088 | 6100 | 5.1704 | - | - |
| 0.1106 | 6200 | 4.8472 | - | - |
| 0.1124 | 6300 | 4.8296 | - | - |
| 0.1142 | 6400 | 4.8287 | - | - |
| 0.1160 | 6500 | 4.6539 | - | - |
| 0.1178 | 6600 | 4.2599 | - | - |
| 0.1196 | 6700 | 4.5506 | - | - |
| 0.1213 | 6800 | 4.6585 | - | - |
| 0.1231 | 6900 | 4.7248 | - | - |
| 0.1249 | 7000 | 4.6389 | 3.1390 | 0.5199 |
| 0.1267 | 7100 | 4.8133 | - | - |
| 0.1285 | 7200 | 4.8838 | - | - |
| 0.1303 | 7300 | 4.7375 | - | - |
| 0.1320 | 7400 | 4.6357 | - | - |
| 0.1338 | 7500 | 4.7807 | - | - |
| 0.1356 | 7600 | 4.409 | - | - |
| 0.1374 | 7700 | 4.5612 | - | - |
| 0.1392 | 7800 | 4.3731 | - | - |
| 0.1410 | 7900 | 4.622 | - | - |
| 0.1427 | 8000 | 4.5574 | 2.6558 | 0.5814 |
| 0.1445 | 8100 | 4.6542 | - | - |
| 0.1463 | 8200 | 4.7831 | - | - |
| 0.1481 | 8300 | 4.6775 | - | - |
| 0.1499 | 8400 | 4.61 | - | - |
| 0.1517 | 8500 | 4.6416 | - | - |
| 0.1535 | 8600 | 4.3096 | - | - |
| 0.1552 | 8700 | 4.2629 | - | - |
| 0.1570 | 8800 | 4.5151 | - | - |
| 0.1588 | 8900 | 4.5301 | - | - |
| 0.1606 | 9000 | 4.5731 | 2.8939 | 0.5675 |
| 0.1624 | 9100 | 4.4347 | - | - |
| 0.1642 | 9200 | 4.648 | - | - |
| 0.1659 | 9300 | 4.6076 | - | - |
| 0.1677 | 9400 | 4.4229 | - | - |
| 0.1695 | 9500 | 4.4785 | - | - |
| 0.1713 | 9600 | 4.4252 | - | - |
| 0.1731 | 9700 | 4.0223 | - | - |
| 0.1749 | 9800 | 4.1593 | - | - |
| 0.1767 | 9900 | 4.2946 | - | - |
| 0.1784 | 10000 | 4.4888 | 2.7814 | 0.5852 |
| 0.1802 | 10100 | 4.3605 | - | - |
| 0.1820 | 10200 | 4.5952 | - | - |
| 0.1838 | 10300 | 4.709 | - | - |
| 0.1856 | 10400 | 4.5743 | - | - |
| 0.1874 | 10500 | 4.5539 | - | - |
| 0.1891 | 10600 | 4.4427 | - | - |
| 0.1909 | 10700 | 4.1095 | - | - |
| 0.1927 | 10800 | 4.4079 | - | - |
| 0.1945 | 10900 | 4.1667 | - | - |
| 0.1963 | 11000 | 4.2273 | 3.3803 | 0.5663 |
| 0.1981 | 11100 | 4.3333 | - | - |
| 0.1998 | 11200 | 4.5174 | - | - |
| 0.2016 | 11300 | 4.4961 | - | - |
| 0.2034 | 11400 | 4.5746 | - | - |
| 0.2052 | 11500 | 4.731 | - | - |
| 0.2070 | 11600 | 4.4485 | - | - |
| 0.2088 | 11700 | 4.4099 | - | - |
| 0.2106 | 11800 | 3.8921 | - | - |
| 0.2123 | 11900 | 4.2423 | - | - |
| 0.2141 | 12000 | 4.2641 | 3.0230 | 0.6300 |
| 0.2159 | 12100 | 4.2052 | - | - |
| 0.2177 | 12200 | 4.2757 | - | - |
| 0.2195 | 12300 | 4.8586 | - | - |
| 0.2213 | 12400 | 4.5872 | - | - |
| 0.2230 | 12500 | 4.4273 | - | - |
| 0.2248 | 12600 | 4.5728 | - | - |
| 0.2266 | 12700 | 4.4607 | - | - |
| 0.2284 | 12800 | 4.1361 | - | - |
| 0.2302 | 12900 | 4.4781 | - | - |
| 0.2320 | 13000 | 4.145 | 2.7088 | 0.6617 |
| 0.2337 | 13100 | 4.3366 | - | - |
| 0.2355 | 13200 | 4.2699 | - | - |
| 0.2373 | 13300 | 4.3397 | - | - |
| 0.2391 | 13400 | 4.6033 | - | - |
| 0.2409 | 13500 | 4.2292 | - | - |
| 0.2427 | 13600 | 4.3399 | - | - |
| 0.2445 | 13700 | 4.5222 | - | - |
| 0.2462 | 13800 | 4.2185 | - | - |
| 0.2480 | 13900 | 3.9426 | - | - |
| 0.2498 | 14000 | 4.2146 | 2.6014 | 0.6724 |
| 0.2516 | 14100 | 4.2534 | - | - |
| 0.2534 | 14200 | 4.1765 | - | - |
| 0.2552 | 14300 | 4.117 | - | - |
| 0.2569 | 14400 | 5.0908 | - | - |
| 0.2587 | 14500 | 4.488 | - | - |
| 0.2605 | 14600 | 4.4429 | - | - |
| 0.2623 | 14700 | 4.3688 | - | - |
| 0.2641 | 14800 | 4.4857 | - | - |
| 0.2659 | 14900 | 4.1763 | - | - |
| 0.2677 | 15000 | 4.4425 | 2.6388 | 0.6842 |
| 0.2694 | 15100 | 4.4277 | - | - |
| 0.2712 | 15200 | 4.3841 | - | - |
| 0.2730 | 15300 | 4.4 | - | - |
| 0.2748 | 15400 | 4.55 | - | - |
| 0.2766 | 15500 | 4.4769 | - | - |
| 0.2784 | 15600 | 4.3918 | - | - |
| 0.2801 | 15700 | 4.554 | - | - |
| 0.2819 | 15800 | 4.406 | - | - |
| 0.2837 | 15900 | 4.0593 | - | - |
| 0.2855 | 16000 | 4.3586 | 2.5251 | 0.7238 |
| 0.2873 | 16100 | 4.2308 | - | - |
| 0.2891 | 16200 | 4.469 | - | - |
| 0.2908 | 16300 | 4.2312 | - | - |
| 0.2926 | 16400 | 4.2695 | - | - |
| 0.2944 | 16500 | 4.5821 | - | - |
| 0.2962 | 16600 | 4.5623 | - | - |
| 0.2980 | 16700 | 4.1865 | - | - |
| 0.2998 | 16800 | 4.4228 | - | - |
| 0.3016 | 16900 | 4.0553 | - | - |
| 0.3033 | 17000 | 3.7183 | 2.6050 | 0.7319 |
| 0.3051 | 17100 | 4.1849 | - | - |
| 0.3069 | 17200 | 4.2975 | - | - |
| 0.3087 | 17300 | 4.4272 | - | - |
| 0.3105 | 17400 | 4.0634 | - | - |
| 0.3123 | 17500 | 4.8608 | - | - |
| 0.3140 | 17600 | 4.4146 | - | - |
| 0.3158 | 17700 | 4.2655 | - | - |
| 0.3176 | 17800 | 4.3814 | - | - |
| 0.3194 | 17900 | 4.3972 | - | - |
| 0.3212 | 18000 | 3.8868 | 2.4737 | 0.7500 |
| 0.3230 | 18100 | 4.434 | - | - |
| 0.3248 | 18200 | 4.2213 | - | - |
| 0.3265 | 18300 | 4.4632 | - | - |
| 0.3283 | 18400 | 4.4001 | - | - |
| 0.3301 | 18500 | 4.8262 | - | - |
| 0.3319 | 18600 | 4.5022 | - | - |
| 0.3337 | 18700 | 4.4148 | - | - |
| 0.3355 | 18800 | 4.2182 | - | - |
| 0.3372 | 18900 | 4.2127 | - | - |
| 0.3390 | 19000 | 4.051 | 2.4633 | 0.7575 |
| 0.3408 | 19100 | 3.655 | - | - |
| 0.3426 | 19200 | 4.2441 | - | - |
| 0.3444 | 19300 | 4.3494 | - | - |
| 0.3462 | 19400 | 4.1824 | - | - |
| 0.3479 | 19500 | 4.3528 | - | - |
| 0.3497 | 19600 | 5.6073 | - | - |
| 0.3515 | 19700 | 4.8231 | - | - |
| 0.3533 | 19800 | 4.5816 | - | - |
| 0.3551 | 19900 | 4.5812 | - | - |
| 0.3569 | 20000 | 4.637 | 2.1229 | 0.7945 |
| 0.3587 | 20100 | 4.2619 | - | - |
| 0.3604 | 20200 | 4.5645 | - | - |
| 0.3622 | 20300 | 4.7248 | - | - |
| 0.3640 | 20400 | 4.5665 | - | - |
| 0.3658 | 20500 | 4.5628 | - | - |
| 0.3676 | 20600 | 4.8494 | - | - |
| 0.3694 | 20700 | 4.4338 | - | - |
| 0.3711 | 20800 | 4.3256 | - | - |
| 0.3729 | 20900 | 4.4388 | - | - |
| 0.3747 | 21000 | 4.158 | 2.3475 | 0.7732 |
| 0.3765 | 21100 | 3.962 | - | - |
| 0.3783 | 21200 | 3.931 | - | - |
| 0.3801 | 21300 | 4.0345 | - | - |
| 0.3818 | 21400 | 4.319 | - | - |
| 0.3836 | 21500 | 4.1329 | - | - |
| 0.3854 | 21600 | 4.245 | - | - |
| 0.3872 | 21700 | 4.518 | - | - |
| 0.3890 | 21800 | 4.4653 | - | - |
| 0.3908 | 21900 | 4.2777 | - | - |
| 0.3926 | 22000 | 4.3358 | 2.1933 | 0.7845 |
| 0.3943 | 22100 | 4.2291 | - | - |
| 0.3961 | 22200 | 3.8067 | - | - |
| 0.3979 | 22300 | 4.2039 | - | - |
| 0.3997 | 22400 | 4.0104 | - | - |
| 0.4015 | 22500 | 4.2346 | - | - |
| 0.4033 | 22600 | 4.0056 | - | - |
| 0.4050 | 22700 | 5.6038 | - | - |
| 0.4068 | 22800 | 5.1185 | - | - |
| 0.4086 | 22900 | 4.924 | - | - |
| 0.4104 | 23000 | 4.7841 | 1.9839 | 0.7956 |
| 0.4122 | 23100 | 4.7953 | - | - |
| 0.4140 | 23200 | 4.4229 | - | - |
| 0.4158 | 23300 | 4.6432 | - | - |
| 0.4175 | 23400 | 4.5284 | - | - |
| 0.4193 | 23500 | 4.7215 | - | - |
| 0.4211 | 23600 | 4.7432 | - | - |
| 0.4229 | 23700 | 5.0136 | - | - |
| 0.4247 | 23800 | 4.7958 | - | - |
| 0.4265 | 23900 | 4.6827 | - | - |
| 0.4282 | 24000 | 4.6665 | 1.9663 | 0.7870 |
| 0.4300 | 24100 | 4.5074 | - | - |
| 0.4318 | 24200 | 4.4189 | - | - |
| 0.4336 | 24300 | 4.4586 | - | - |
| 0.4354 | 24400 | 4.6421 | - | - |
| 0.4372 | 24500 | 4.4281 | - | - |
| 0.4389 | 24600 | 4.5153 | - | - |
| 0.4407 | 24700 | 4.9942 | - | - |
| 0.4425 | 24800 | 5.11 | - | - |
| 0.4443 | 24900 | 4.7071 | - | - |
| 0.4461 | 25000 | 4.6257 | 1.9461 | 0.7935 |
| 0.4479 | 25100 | 4.6576 | - | - |
| 0.4497 | 25200 | 4.6103 | - | - |
| 0.4514 | 25300 | 4.2066 | - | - |
| 0.4532 | 25400 | 4.6869 | - | - |
| 0.4550 | 25500 | 4.7575 | - | - |
| 0.4568 | 25600 | 4.6081 | - | - |
| 0.4586 | 25700 | 4.8144 | - | - |
| 0.4604 | 25800 | 5.2007 | - | - |
| 0.4621 | 25900 | 4.8367 | - | - |
| 0.4639 | 26000 | 4.5258 | 1.9131 | 0.7993 |
| 0.4657 | 26100 | 4.4784 | - | - |
| 0.4675 | 26200 | 4.5568 | - | - |
| 0.4693 | 26300 | 4.2591 | - | - |
| 0.4711 | 26400 | 4.4521 | - | - |
| 0.4729 | 26500 | 4.4041 | - | - |
| 0.4746 | 26600 | 4.4926 | - | - |
| 0.4764 | 26700 | 4.1686 | - | - |
| 0.4782 | 26800 | 4.6294 | - | - |
| 0.4800 | 26900 | 4.6889 | - | - |
| 0.4818 | 27000 | 4.5765 | 1.9539 | 0.7961 |
| 0.4836 | 27100 | 4.3427 | - | - |
| 0.4853 | 27200 | 4.5275 | - | - |
| 0.4871 | 27300 | 4.4186 | - | - |
| 0.4889 | 27400 | 4.0163 | - | - |
| 0.4907 | 27500 | 4.3204 | - | - |
| 0.4925 | 27600 | 4.179 | - | - |
| 0.4943 | 27700 | 4.3838 | - | - |
| 0.4960 | 27800 | 4.2631 | - | - |
| 0.4978 | 27900 | 4.7177 | - | - |
| 0.4996 | 28000 | 4.5161 | 2.0116 | 0.7935 |
| 0.5014 | 28100 | 4.2861 | - | - |
| 0.5032 | 28200 | 4.4123 | - | - |
| 0.5050 | 28300 | 4.293 | - | - |
| 0.5068 | 28400 | 4.2346 | - | - |
| 0.5085 | 28500 | 4.3355 | - | - |
| 0.5103 | 28600 | 4.4616 | - | - |
| 0.5121 | 28700 | 4.2409 | - | - |
| 0.5139 | 28800 | 4.2398 | - | - |
| 0.5157 | 28900 | 4.7412 | - | - |
| 0.5175 | 29000 | 4.5044 | 2.1008 | 0.7859 |
| 0.5192 | 29100 | 4.4556 | - | - |
| 0.5210 | 29200 | 4.2938 | - | - |
| 0.5228 | 29300 | 4.4962 | - | - |
| 0.5246 | 29400 | 4.477 | - | - |
| 0.5264 | 29500 | 4.2602 | - | - |
| 0.5282 | 29600 | 4.4231 | - | - |
| 0.5300 | 29700 | 4.2165 | - | - |
| 0.5317 | 29800 | 4.3729 | - | - |
| 0.5335 | 29900 | 4.2414 | - | - |
| 0.5353 | 30000 | 4.9937 | 2.0884 | 0.7702 |
| 0.5371 | 30100 | 4.5737 | - | - |
| 0.5389 | 30200 | 4.4517 | - | - |
| 0.5407 | 30300 | 4.4178 | - | - |
| 0.5424 | 30400 | 4.3514 | - | - |
| 0.5442 | 30500 | 3.9723 | - | - |
| 0.5460 | 30600 | 4.3707 | - | - |
| 0.5478 | 30700 | 4.2235 | - | - |
| 0.5496 | 30800 | 4.4278 | - | - |
| 0.5514 | 30900 | 4.2914 | - | - |
| 0.5531 | 31000 | 4.5636 | 2.3277 | 0.7454 |
| 0.5549 | 31100 | 4.4889 | - | - |
| 0.5567 | 31200 | 4.3211 | - | - |
| 0.5585 | 31300 | 4.404 | - | - |
| 0.5603 | 31400 | 4.2117 | - | - |
| 0.5621 | 31500 | 4.1126 | - | - |
| 0.5639 | 31600 | 4.1737 | - | - |
| 0.5656 | 31700 | 4.203 | - | - |
| 0.5674 | 31800 | 4.1093 | - | - |
| 0.5692 | 31900 | 4.0702 | - | - |
| 0.5710 | 32000 | 4.4189 | 2.6265 | 0.7375 |
| 0.5728 | 32100 | 4.9817 | - | - |
| 0.5746 | 32200 | 4.4736 | - | - |
| 0.5763 | 32300 | 4.348 | - | - |
| 0.5781 | 32400 | 4.5404 | - | - |
| 0.5799 | 32500 | 4.2987 | - | - |
| 0.5817 | 32600 | 4.0725 | - | - |
| 0.5835 | 32700 | 4.5469 | - | - |
| 0.5853 | 32800 | 4.4367 | - | - |
| 0.5870 | 32900 | 4.3369 | - | - |
| 0.5888 | 33000 | 4.2292 | 2.5687 | 0.7213 |
| 0.5906 | 33100 | 4.7929 | - | - |
| 0.5924 | 33200 | 4.4123 | - | - |
| 0.5942 | 33300 | 4.1699 | - | - |
| 0.5960 | 33400 | 4.4021 | - | - |
| 0.5978 | 33500 | 4.5257 | - | - |
| 0.5995 | 33600 | 3.7222 | - | - |
| 0.6013 | 33700 | 4.0746 | - | - |
| 0.6031 | 33800 | 4.1399 | - | - |
| 0.6049 | 33900 | 3.9957 | - | - |
| 0.6067 | 34000 | 4.093 | 2.4645 | 0.7524 |
| 0.6085 | 34100 | 4.2929 | - | - |
| 0.6102 | 34200 | 4.4765 | - | - |
| 0.6120 | 34300 | 4.3871 | - | - |
| 0.6138 | 34400 | 4.385 | - | - |
| 0.6156 | 34500 | 4.1455 | - | - |
| 0.6174 | 34600 | 3.7689 | - | - |
| 0.6192 | 34700 | 3.6574 | - | - |
| 0.6210 | 34800 | 4.2426 | - | - |
| 0.6227 | 34900 | 4.293 | - | - |
| 0.6245 | 35000 | 4.1368 | 2.4370 | 0.7765 |
| 0.6263 | 35100 | 3.6174 | - | - |
| 0.6281 | 35200 | 4.7763 | - | - |
| 0.6299 | 35300 | 4.3121 | - | - |
| 0.6317 | 35400 | 4.1886 | - | - |
| 0.6334 | 35500 | 4.3538 | - | - |
| 0.6352 | 35600 | 4.0285 | - | - |
| 0.6370 | 35700 | 3.4691 | - | - |
| 0.6388 | 35800 | 4.2732 | - | - |
| 0.6406 | 35900 | 4.2052 | - | - |
| 0.6424 | 36000 | 4.0452 | 2.4680 | 0.7732 |
| 0.6441 | 36100 | 3.9032 | - | - |
| 0.6459 | 36200 | 4.2608 | - | - |
| 0.6477 | 36300 | 4.262 | - | - |
| 0.6495 | 36400 | 4.1138 | - | - |
| 0.6513 | 36500 | 4.248 | - | - |
| 0.6531 | 36600 | 4.1163 | - | - |
| 0.6549 | 36700 | 3.6375 | - | - |
| 0.6566 | 36800 | 4.0768 | - | - |
| 0.6584 | 36900 | 4.0268 | - | - |
| 0.6602 | 37000 | 4.0129 | 2.6361 | 0.7702 |
| 0.6620 | 37100 | 3.7976 | - | - |
| 0.6638 | 37200 | 4.2518 | - | - |
| 0.6656 | 37300 | 4.5011 | - | - |
| 0.6673 | 37400 | 4.4488 | - | - |
| 0.6691 | 37500 | 3.9798 | - | - |
| 0.6709 | 37600 | 4.027 | - | - |
| 0.6727 | 37700 | 4.0342 | - | - |
| 0.6745 | 37800 | 3.8229 | - | - |
| 0.6763 | 37900 | 4.0573 | - | - |
| 0.6781 | 38000 | 4.1739 | 2.4511 | 0.7935 |
| 0.6798 | 38100 | 4.57 | - | - |
| 0.6816 | 38200 | 3.9108 | - | - |
| 0.6834 | 38300 | 4.3569 | - | - |
| 0.6852 | 38400 | 4.3775 | - | - |
| 0.6870 | 38500 | 4.2887 | - | - |
| 0.6888 | 38600 | 4.144 | - | - |
| 0.6905 | 38700 | 4.5112 | - | - |
| 0.6923 | 38800 | 3.5093 | - | - |
| 0.6941 | 38900 | 3.9626 | - | - |
| 0.6959 | 39000 | 4.024 | 2.4241 | 0.7868 |
| 0.6977 | 39100 | 4.0671 | - | - |
| 0.6995 | 39200 | 3.9545 | - | - |
| 0.7012 | 39300 | 4.0036 | - | - |
| 0.7030 | 39400 | 4.3796 | - | - |
| 0.7048 | 39500 | 4.2912 | - | - |
| 0.7066 | 39600 | 4.1181 | - | - |
| 0.7084 | 39700 | 4.1437 | - | - |
| 0.7102 | 39800 | 3.8734 | - | - |
| 0.7120 | 39900 | 3.7678 | - | - |
| 0.7137 | 40000 | 4.2327 | 2.3937 | 0.7956 |
| 0.7155 | 40100 | 3.8276 | - | - |
| 0.7173 | 40200 | 4.2885 | - | - |
| 0.7191 | 40300 | 4.019 | - | - |
| 0.7209 | 40400 | 4.6898 | - | - |
| 0.7227 | 40500 | 4.2398 | - | - |
| 0.7244 | 40600 | 4.317 | - | - |
| 0.7262 | 40700 | 4.2543 | - | - |
| 0.7280 | 40800 | 4.1048 | - | - |
| 0.7298 | 40900 | 3.4243 | - | - |
| 0.7316 | 41000 | 4.0587 | 2.2848 | 0.8035 |
| 0.7334 | 41100 | 4.2112 | - | - |
| 0.7351 | 41200 | 4.0331 | - | - |
| 0.7369 | 41300 | 4.2361 | - | - |
| 0.7387 | 41400 | 4.3818 | - | - |
| 0.7405 | 41500 | 4.1311 | - | - |
| 0.7423 | 41600 | 4.0607 | - | - |
| 0.7441 | 41700 | 4.1277 | - | - |
| 0.7459 | 41800 | 3.8844 | - | - |
| 0.7476 | 41900 | 3.6138 | - | - |
| 0.7494 | 42000 | 3.7973 | 2.4197 | 0.8045 |
| 0.7512 | 42100 | 4.0854 | - | - |
| 0.7530 | 42200 | 4.0926 | - | - |
| 0.7548 | 42300 | 3.9821 | - | - |
| 0.7566 | 42400 | 4.5564 | - | - |
| 0.7583 | 42500 | 6.1707 | - | - |
| 0.7601 | 42600 | 5.4598 | - | - |
| 0.7619 | 42700 | 5.2202 | - | - |
| 0.7637 | 42800 | 5.1402 | - | - |
| 0.7655 | 42900 | 4.8446 | - | - |
| 0.7673 | 43000 | 4.5341 | 1.9710 | 0.8181 |
| 0.7691 | 43100 | 5.0068 | - | - |
| 0.7708 | 43200 | 5.0099 | - | - |
| 0.7726 | 43300 | 4.7986 | - | - |
| 0.7744 | 43400 | 5.0468 | - | - |
| 0.7762 | 43500 | 5.135 | - | - |
| 0.7780 | 43600 | 4.8018 | - | - |
| 0.7798 | 43700 | 4.6291 | - | - |
| 0.7815 | 43800 | 4.6119 | - | - |
| 0.7833 | 43900 | 4.5318 | - | - |
| 0.7851 | 44000 | 3.9703 | 1.9790 | 0.8211 |
| 0.7869 | 44100 | 4.461 | - | - |
| 0.7887 | 44200 | 4.5536 | - | - |
| 0.7905 | 44300 | 4.411 | - | - |
| 0.7922 | 44400 | 4.5796 | - | - |
| 0.7940 | 44500 | 4.7385 | - | - |
| 0.7958 | 44600 | 4.6635 | - | - |
| 0.7976 | 44700 | 4.4808 | - | - |
| 0.7994 | 44800 | 4.5565 | - | - |
| 0.8012 | 44900 | 4.4707 | - | - |
| 0.8030 | 45000 | 3.9981 | 1.9823 | 0.8197 |
| 0.8047 | 45100 | 4.119 | - | - |
| 0.8065 | 45200 | 4.4209 | - | - |
| 0.8083 | 45300 | 4.3268 | - | - |
| 0.8101 | 45400 | 4.2979 | - | - |
| 0.8119 | 45500 | 4.413 | - | - |
| 0.8137 | 45600 | 4.3317 | - | - |
| 0.8154 | 45700 | 4.3683 | - | - |
| 0.8172 | 45800 | 4.0769 | - | - |
| 0.8190 | 45900 | 4.304 | - | - |
| 0.8208 | 46000 | 4.0985 | 2.0490 | 0.8183 |
| 0.8226 | 46100 | 3.8719 | - | - |
| 0.8244 | 46200 | 4.1843 | - | - |
| 0.8262 | 46300 | 4.2131 | - | - |
| 0.8279 | 46400 | 4.3327 | - | - |
| 0.8297 | 46500 | 3.8533 | - | - |
| 0.8315 | 46600 | 5.2854 | - | - |
| 0.8333 | 46700 | 5.2465 | - | - |
| 0.8351 | 46800 | 5.0221 | - | - |
| 0.8369 | 46900 | 4.9466 | - | - |
| 0.8386 | 47000 | 5.0361 | 1.8252 | 0.8360 |
| 0.8404 | 47100 | 4.3676 | - | - |
| 0.8422 | 47200 | 4.619 | - | - |
| 0.8440 | 47300 | 4.6412 | - | - |
| 0.8458 | 47400 | 4.7874 | - | - |
| 0.8476 | 47500 | 4.663 | - | - |
| 0.8493 | 47600 | 4.7068 | - | - |
| 0.8511 | 47700 | 4.5889 | - | - |
| 0.8529 | 47800 | 4.3468 | - | - |
| 0.8547 | 47900 | 4.4393 | - | - |
| 0.8565 | 48000 | 4.5488 | 1.9117 | 0.8176 |
| 0.8583 | 48100 | 4.0933 | - | - |
| 0.8601 | 48200 | 3.7754 | - | - |
| 0.8618 | 48300 | 4.1346 | - | - |
| 0.8636 | 48400 | 4.402 | - | - |
| 0.8654 | 48500 | 4.0163 | - | - |
| 0.8672 | 48600 | 4.3405 | - | - |
| 0.8690 | 48700 | 4.7694 | - | - |
| 0.8708 | 48800 | 4.4457 | - | - |
| 0.8725 | 48900 | 4.3679 | - | - |
| 0.8743 | 49000 | 4.3283 | 1.9392 | 0.8251 |
| 0.8761 | 49100 | 4.6855 | - | - |
| 0.8779 | 49200 | 3.881 | - | - |
| 0.8797 | 49300 | 4.1392 | - | - |
| 0.8815 | 49400 | 4.4343 | - | - |
| 0.8833 | 49500 | 4.4822 | - | - |
| 0.8850 | 49600 | 4.3977 | - | - |
| 0.8868 | 49700 | 4.5944 | - | - |
| 0.8886 | 49800 | 4.4176 | - | - |
| 0.8904 | 49900 | 4.5269 | - | - |
| 0.8922 | 50000 | 4.4267 | 1.8965 | 0.8206 |
| 0.8940 | 50100 | 4.5109 | - | - |
| 0.8957 | 50200 | 4.1775 | - | - |
| 0.8975 | 50300 | 4.3453 | - | - |
| 0.8993 | 50400 | 4.5443 | - | - |
| 0.9011 | 50500 | 4.226 | - | - |
| 0.9029 | 50600 | 4.3296 | - | - |
| 0.9047 | 50700 | 4.1968 | - | - |
| 0.9064 | 50800 | 4.2206 | - | - |
| 0.9082 | 50900 | 4.2299 | - | - |
| 0.9100 | 51000 | 4.0471 | 2.0479 | 0.8146 |
| 0.9118 | 51100 | 4.0832 | - | - |
| 0.9136 | 51200 | 3.7516 | - | - |
| 0.9154 | 51300 | 4.0545 | - | - |
| 0.9172 | 51400 | 4.1281 | - | - |
| 0.9189 | 51500 | 4.2336 | - | - |
| 0.9207 | 51600 | 4.2511 | - | - |
| 0.9225 | 51700 | 4.2588 | - | - |
| 0.9243 | 51800 | 4.0719 | - | - |
| 0.9261 | 51900 | 4.1847 | - | - |
| 0.9279 | 52000 | 4.1445 | 2.1419 | 0.8128 |
| 0.9296 | 52100 | 3.9735 | - | - |
| 0.9314 | 52200 | 3.8635 | - | - |
| 0.9332 | 52300 | 4.1738 | - | - |
| 0.9350 | 52400 | 4.07 | - | - |
| 0.9368 | 52500 | 4.1008 | - | - |
| 0.9386 | 52600 | 3.9628 | - | - |
| 0.9403 | 52700 | 4.2895 | - | - |
| 0.9421 | 52800 | 4.3393 | - | - |
| 0.9439 | 52900 | 2.8535 | - | - |
| 0.9457 | 53000 | 2.5506 | 2.1743 | 0.8116 |
| 0.9475 | 53100 | 2.1566 | - | - |
| 0.9493 | 53200 | 2.0386 | - | - |
| 0.9511 | 53300 | 1.8535 | - | - |
| 0.9528 | 53400 | 1.8561 | - | - |
| 0.9546 | 53500 | 1.3213 | - | - |
| 0.9564 | 53600 | 1.0904 | - | - |
| 0.9582 | 53700 | 1.2266 | - | - |
| 0.9600 | 53800 | 0.9386 | - | - |
| 0.9618 | 53900 | 0.8379 | - | - |
| 0.9635 | 54000 | 0.9314 | 2.3331 | 0.8071 |
| 0.9653 | 54100 | 1.1145 | - | - |
| 0.9671 | 54200 | 1.4435 | - | - |
| 0.9689 | 54300 | 1.3226 | - | - |
| 0.9707 | 54400 | 0.6677 | - | - |
| 0.9725 | 54500 | 0.7357 | - | - |
| 0.9743 | 54600 | 0.6854 | - | - |
| 0.9760 | 54700 | 0.8408 | - | - |
| 0.9778 | 54800 | 0.6291 | - | - |
| 0.9796 | 54900 | 0.8203 | - | - |
| 0.9814 | 55000 | 1.6263 | 2.4720 | 0.8104 |
| 0.9832 | 55100 | 0.95 | - | - |
| 0.9850 | 55200 | 0.6462 | - | - |
| 0.9867 | 55300 | 1.2467 | - | - |
| 0.9885 | 55400 | 1.4926 | - | - |
| 0.9903 | 55500 | 1.9608 | - | - |
| 0.9921 | 55600 | 1.6415 | - | - |
| 0.9939 | 55700 | 1.3258 | - | - |
| 0.9957 | 55800 | 1.2157 | - | - |
| 0.9974 | 55900 | 1.2391 | - | - |
| 0.9992 | 56000 | 1.3474 | 2.5008 | 0.8111 |