---
base_model: sentence-transformers/all-MiniLM-L6-v2
language:
- en
library_name: sentence-transformers
license: apache-2.0
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:1363306
- loss:CoSENTLoss
widget:
- source_sentence: labneh
sentences:
- iftar
- bathing suit
- coffee cup
- source_sentence: Velvet flock Veil
sentences:
- mermaid purse
- veil
- mobile bag
- source_sentence: Red lipstick
sentences:
- chemise dress
- tote
- rouge
- source_sentence: Unisex Travel bag
sentences:
- spf
- basic vega ring
- travel backpack
- source_sentence: jeremy hush book
sentences:
- chinese jumper
- perfume
- home automation device
model-index:
- name: all-MiniLM-L6-v5-pair_score-syn-fr
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: 0.45976967432661087
name: Pearson Cosine
- type: spearman_cosine
value: 0.44063948938599923
name: Spearman Cosine
- type: pearson_manhattan
value: 0.41341637785801416
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.4372479132617008
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.4145493812051541
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.44063932299328573
name: Spearman Euclidean
- type: pearson_dot
value: 0.45976967600824187
name: Pearson Dot
- type: spearman_dot
value: 0.44063967285735406
name: Spearman Dot
- type: pearson_max
value: 0.45976967600824187
name: Pearson Max
- type: spearman_max
value: 0.44063967285735406
name: Spearman Max
---
# all-MiniLM-L6-v5-pair_score-syn-fr
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:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Language:** en
- **License:** apache-2.0
### 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': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'jeremy hush book',
'chinese jumper',
'perfume',
]
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 | 0.4598 |
| **spearman_cosine** | **0.4406** |
| pearson_manhattan | 0.4134 |
| spearman_manhattan | 0.4372 |
| pearson_euclidean | 0.4145 |
| spearman_euclidean | 0.4406 |
| pearson_dot | 0.4598 |
| spearman_dot | 0.4406 |
| pearson_max | 0.4598 |
| spearman_max | 0.4406 |
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: 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`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 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`: 4
- `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
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
Click to expand
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:-----------------------:|
| 0 | 0 | - | - | 0.4406 |
| 0.0094 | 100 | 17.0727 | - | - |
| 0.0188 | 200 | 16.8813 | - | - |
| 0.0282 | 300 | 16.5085 | - | - |
| 0.0376 | 400 | 15.5716 | - | - |
| 0.0469 | 500 | 14.5542 | - | - |
| 0.0563 | 600 | 13.1478 | - | - |
| 0.0657 | 700 | 11.3662 | - | - |
| 0.0751 | 800 | 9.5649 | - | - |
| 0.0845 | 900 | 8.536 | - | - |
| 0.0939 | 1000 | 8.2589 | - | - |
| 0.1033 | 1100 | 8.1649 | - | - |
| 0.1127 | 1200 | 8.134 | - | - |
| 0.1221 | 1300 | 8.1331 | - | - |
| 0.1314 | 1400 | 8.0893 | - | - |
| 0.1408 | 1500 | 8.0706 | - | - |
| 0.1502 | 1600 | 8.0786 | - | - |
| 0.1596 | 1700 | 8.058 | - | - |
| 0.1690 | 1800 | 8.0768 | - | - |
| 0.1784 | 1900 | 8.0834 | - | - |
| 0.1878 | 2000 | 8.0714 | - | - |
| 0.1972 | 2100 | 8.0671 | - | - |
| 0.2066 | 2200 | 8.051 | - | - |
| 0.2159 | 2300 | 8.0287 | - | - |
| 0.2253 | 2400 | 8.0445 | - | - |
| 0.2347 | 2500 | 8.0444 | - | - |
| 0.2441 | 2600 | 8.0679 | - | - |
| 0.2535 | 2700 | 8.0472 | - | - |
| 0.2629 | 2800 | 8.0151 | - | - |
| 0.2723 | 2900 | 8.0599 | - | - |
| 0.2817 | 3000 | 8.0304 | - | - |
| 0.2911 | 3100 | 8.0373 | - | - |
| 0.3004 | 3200 | 8.0382 | - | - |
| 0.3098 | 3300 | 8.0112 | - | - |
| 0.3192 | 3400 | 8.0209 | - | - |
| 0.3286 | 3500 | 8.0487 | - | - |
| 0.3380 | 3600 | 8.0138 | - | - |
| 0.3474 | 3700 | 8.046 | - | - |
| 0.3568 | 3800 | 7.9876 | - | - |
| 0.3662 | 3900 | 7.997 | - | - |
| 0.3756 | 4000 | 8.0462 | - | - |
| 0.3849 | 4100 | 7.9882 | - | - |
| 0.3943 | 4200 | 7.9949 | - | - |
| 0.4037 | 4300 | 7.9951 | - | - |
| 0.4131 | 4400 | 8.0202 | - | - |
| 0.4225 | 4500 | 8.0126 | - | - |
| 0.4319 | 4600 | 8.0351 | - | - |
| 0.4413 | 4700 | 8.0419 | - | - |
| 0.4507 | 4800 | 7.9959 | - | - |
| 0.4601 | 4900 | 8.0076 | - | - |
| 0.4694 | 5000 | 8.0022 | 8.0125 | - |
| 0.4788 | 5100 | 7.9819 | - | - |
| 0.4882 | 5200 | 7.9836 | - | - |
| 0.4976 | 5300 | 7.9996 | - | - |
| 0.5070 | 5400 | 8.0221 | - | - |
| 0.5164 | 5500 | 8.0854 | - | - |
| 0.5258 | 5600 | 8.0306 | - | - |
| 0.5352 | 5700 | 7.9924 | - | - |
| 0.5445 | 5800 | 7.9884 | - | - |
| 0.5539 | 5900 | 8.0253 | - | - |
| 0.5633 | 6000 | 7.9773 | - | - |
| 0.5727 | 6100 | 7.9878 | - | - |
| 0.5821 | 6200 | 8.0495 | - | - |
| 0.5915 | 6300 | 7.9908 | - | - |
| 0.6009 | 6400 | 7.9886 | - | - |
| 0.6103 | 6500 | 8.0232 | - | - |
| 0.6197 | 6600 | 7.9933 | - | - |
| 0.6290 | 6700 | 8.0143 | - | - |
| 0.6384 | 6800 | 7.9956 | - | - |
| 0.6478 | 6900 | 7.9755 | - | - |
| 0.6572 | 7000 | 7.9814 | - | - |
| 0.6666 | 7100 | 7.9849 | - | - |
| 0.6760 | 7200 | 8.0076 | - | - |
| 0.6854 | 7300 | 8.0071 | - | - |
| 0.6948 | 7400 | 8.003 | - | - |
| 0.7042 | 7500 | 7.9966 | - | - |
| 0.7135 | 7600 | 8.0052 | - | - |
| 0.7229 | 7700 | 8.0226 | - | - |
| 0.7323 | 7800 | 7.9809 | - | - |
| 0.7417 | 7900 | 7.9802 | - | - |
| 0.7511 | 8000 | 8.0008 | - | - |
| 0.7605 | 8100 | 7.9876 | - | - |
| 0.7699 | 8200 | 8.0295 | - | - |
| 0.7793 | 8300 | 7.9992 | - | - |
| 0.7887 | 8400 | 7.9942 | - | - |
| 0.7980 | 8500 | 7.9872 | - | - |
| 0.8074 | 8600 | 7.9757 | - | - |
| 0.8168 | 8700 | 7.9835 | - | - |
| 0.8262 | 8800 | 8.0555 | - | - |
| 0.8356 | 8900 | 8.0055 | - | - |
| 0.8450 | 9000 | 7.9817 | - | - |
| 0.8544 | 9100 | 7.9952 | - | - |
| 0.8638 | 9200 | 8.0083 | - | - |
| 0.8732 | 9300 | 7.984 | - | - |
| 0.8825 | 9400 | 7.9918 | - | - |
| 0.8919 | 9500 | 7.9816 | - | - |
| 0.9013 | 9600 | 8.0167 | - | - |
| 0.9107 | 9700 | 7.9747 | - | - |
| 0.9201 | 9800 | 7.9882 | - | - |
| 0.9295 | 9900 | 8.0003 | - | - |
| 0.9389 | 10000 | 8.0067 | 7.9823 | - |
| 0.9483 | 10100 | 8.017 | - | - |
| 0.9577 | 10200 | 7.9763 | - | - |
| 0.9670 | 10300 | 7.9553 | - | - |
| 0.9764 | 10400 | 7.9525 | - | - |
| 0.9858 | 10500 | 7.9987 | - | - |
| 0.9952 | 10600 | 7.9715 | - | - |
| 1.0046 | 10700 | 7.947 | - | - |
| 1.0140 | 10800 | 8.0298 | - | - |
| 1.0234 | 10900 | 7.9756 | - | - |
| 1.0328 | 11000 | 7.979 | - | - |
| 1.0422 | 11100 | 8.0417 | - | - |
| 1.0515 | 11200 | 7.9936 | - | - |
| 1.0609 | 11300 | 7.971 | - | - |
| 1.0703 | 11400 | 7.99 | - | - |
| 1.0797 | 11500 | 7.9562 | - | - |
| 1.0891 | 11600 | 7.9541 | - | - |
| 1.0985 | 11700 | 7.9788 | - | - |
| 1.1079 | 11800 | 7.9883 | - | - |
| 1.1173 | 11900 | 7.9643 | - | - |
| 1.1267 | 12000 | 7.9806 | - | - |
| 1.1360 | 12100 | 7.9543 | - | - |
| 1.1454 | 12200 | 7.9684 | - | - |
| 1.1548 | 12300 | 7.9492 | - | - |
| 1.1642 | 12400 | 7.984 | - | - |
| 1.1736 | 12500 | 7.9817 | - | - |
| 1.1830 | 12600 | 7.9621 | - | - |
| 1.1924 | 12700 | 7.9782 | - | - |
| 1.2018 | 12800 | 7.9748 | - | - |
| 1.2112 | 12900 | 7.9606 | - | - |
| 1.2205 | 13000 | 7.9654 | - | - |
| 1.2299 | 13100 | 7.9708 | - | - |
| 1.2393 | 13200 | 7.9832 | - | - |
| 1.2487 | 13300 | 7.9482 | - | - |
| 1.2581 | 13400 | 7.9717 | - | - |
| 1.2675 | 13500 | 7.9667 | - | - |
| 1.2769 | 13600 | 7.9653 | - | - |
| 1.2863 | 13700 | 7.969 | - | - |
| 1.2957 | 13800 | 7.9416 | - | - |
| 1.3050 | 13900 | 7.994 | - | - |
| 1.3144 | 14000 | 7.9821 | - | - |
| 1.3238 | 14100 | 7.9656 | - | - |
| 1.3332 | 14200 | 7.9763 | - | - |
| 1.3426 | 14300 | 7.9708 | - | - |
| 1.3520 | 14400 | 7.9713 | - | - |
| 1.3614 | 14500 | 8.0128 | - | - |
| 1.3708 | 14600 | 7.9914 | - | - |
| 1.3802 | 14700 | 7.9839 | - | - |
| 1.3895 | 14800 | 7.9485 | - | - |
| 1.3989 | 14900 | 7.9564 | - | - |
| 1.4083 | 15000 | 7.9646 | 7.9795 | - |
| 1.4177 | 15100 | 7.9443 | - | - |
| 1.4271 | 15200 | 8.002 | - | - |
| 1.4365 | 15300 | 7.9493 | - | - |
| 1.4459 | 15400 | 7.9561 | - | - |
| 1.4553 | 15500 | 7.9571 | - | - |
| 1.4647 | 15600 | 7.9634 | - | - |
| 1.4740 | 15700 | 7.9348 | - | - |
| 1.4834 | 15800 | 7.9476 | - | - |
| 1.4928 | 15900 | 7.9373 | - | - |
| 1.5022 | 16000 | 7.9985 | - | - |
| 1.5116 | 16100 | 7.9518 | - | - |
| 1.5210 | 16200 | 7.9751 | - | - |
| 1.5304 | 16300 | 7.9677 | - | - |
| 1.5398 | 16400 | 7.9538 | - | - |
| 1.5492 | 16500 | 7.9894 | - | - |
| 1.5585 | 16600 | 7.9832 | - | - |
| 1.5679 | 16700 | 7.9582 | - | - |
| 1.5773 | 16800 | 7.975 | - | - |
| 1.5867 | 16900 | 7.9379 | - | - |
| 1.5961 | 17000 | 7.9434 | - | - |
| 1.6055 | 17100 | 7.9805 | - | - |
| 1.6149 | 17200 | 7.946 | - | - |
| 1.6243 | 17300 | 7.9613 | - | - |
| 1.6336 | 17400 | 7.9687 | - | - |
| 1.6430 | 17500 | 7.9612 | - | - |
| 1.6524 | 17600 | 7.9614 | - | - |
| 1.6618 | 17700 | 7.95 | - | - |
| 1.6712 | 17800 | 7.9874 | - | - |
| 1.6806 | 17900 | 7.9665 | - | - |
| 1.6900 | 18000 | 7.9562 | - | - |
| 1.6994 | 18100 | 7.9777 | - | - |
| 1.7088 | 18200 | 7.9771 | - | - |
| 1.7181 | 18300 | 7.9405 | - | - |
| 1.7275 | 18400 | 7.9516 | - | - |
| 1.7369 | 18500 | 8.0012 | - | - |
| 1.7463 | 18600 | 7.9464 | - | - |
| 1.7557 | 18700 | 7.9623 | - | - |
| 1.7651 | 18800 | 7.9478 | - | - |
| 1.7745 | 18900 | 7.9528 | - | - |
| 1.7839 | 19000 | 7.9617 | - | - |
| 1.7933 | 19100 | 7.966 | - | - |
| 1.8026 | 19200 | 7.9718 | - | - |
| 1.8120 | 19300 | 7.9679 | - | - |
| 1.8214 | 19400 | 7.9448 | - | - |
| 1.8308 | 19500 | 7.9299 | - | - |
| 1.8402 | 19600 | 7.967 | - | - |
| 1.8496 | 19700 | 7.9327 | - | - |
| 1.8590 | 19800 | 7.9602 | - | - |
| 1.8684 | 19900 | 7.9515 | - | - |
| 1.8778 | 20000 | 7.9447 | 7.9457 | - |
| 1.8871 | 20100 | 7.9487 | - | - |
| 1.8965 | 20200 | 7.9438 | - | - |
| 1.9059 | 20300 | 7.9821 | - | - |
| 1.9153 | 20400 | 7.9485 | - | - |
| 1.9247 | 20500 | 7.9251 | - | - |
| 1.9341 | 20600 | 7.982 | - | - |
| 1.9435 | 20700 | 7.9508 | - | - |
| 1.9529 | 20800 | 7.9511 | - | - |
| 1.9623 | 20900 | 7.9747 | - | - |
| 1.9716 | 21000 | 7.9365 | - | - |
| 1.9810 | 21100 | 7.9845 | - | - |
| 1.9904 | 21200 | 8.0186 | - | - |
| 1.9998 | 21300 | 8.0228 | - | - |
| 2.0092 | 21400 | 7.949 | - | - |
| 2.0186 | 21500 | 7.9371 | - | - |
| 2.0280 | 21600 | 7.9355 | - | - |
| 2.0374 | 21700 | 7.9528 | - | - |
| 2.0468 | 21800 | 7.9246 | - | - |
| 2.0561 | 21900 | 7.9721 | - | - |
| 2.0655 | 22000 | 7.9438 | - | - |
| 2.0749 | 22100 | 7.9349 | - | - |
| 2.0843 | 22200 | 7.9315 | - | - |
| 2.0937 | 22300 | 7.9398 | - | - |
| 2.1031 | 22400 | 7.9232 | - | - |
| 2.1125 | 22500 | 7.9189 | - | - |
| 2.1219 | 22600 | 7.9296 | - | - |
| 2.1313 | 22700 | 7.9658 | - | - |
| 2.1406 | 22800 | 7.922 | - | - |
| 2.1500 | 22900 | 7.9247 | - | - |
| 2.1594 | 23000 | 7.9748 | - | - |
| 2.1688 | 23100 | 7.9632 | - | - |
| 2.1782 | 23200 | 7.9416 | - | - |
| 2.1876 | 23300 | 8.0063 | - | - |
| 2.1970 | 23400 | 7.9347 | - | - |
| 2.2064 | 23500 | 7.9242 | - | - |
| 2.2158 | 23600 | 7.9537 | - | - |
| 2.2251 | 23700 | 7.9281 | - | - |
| 2.2345 | 23800 | 7.9417 | - | - |
| 2.2439 | 23900 | 7.9699 | - | - |
| 2.2533 | 24000 | 7.9919 | - | - |
| 2.2627 | 24100 | 7.9322 | - | - |
| 2.2721 | 24200 | 7.9702 | - | - |
| 2.2815 | 24300 | 7.9421 | - | - |
| 2.2909 | 24400 | 7.9453 | - | - |
| 2.3003 | 24500 | 7.9485 | - | - |
| 2.3096 | 24600 | 7.9491 | - | - |
| 2.3190 | 24700 | 7.9575 | - | - |
| 2.3284 | 24800 | 7.9481 | - | - |
| 2.3378 | 24900 | 7.9261 | - | - |
| 2.3472 | 25000 | 7.9347 | 7.9455 | - |
| 2.3566 | 25100 | 7.9434 | - | - |
| 2.3660 | 25200 | 7.9627 | - | - |
| 2.3754 | 25300 | 7.9303 | - | - |
| 2.3848 | 25400 | 7.9455 | - | - |
| 2.3941 | 25500 | 7.9228 | - | - |
| 2.4035 | 25600 | 7.9492 | - | - |
| 2.4129 | 25700 | 7.9384 | - | - |
| 2.4223 | 25800 | 7.9408 | - | - |
| 2.4317 | 25900 | 7.9497 | - | - |
| 2.4411 | 26000 | 7.9159 | - | - |
| 2.4505 | 26100 | 7.941 | - | - |
| 2.4599 | 26200 | 7.937 | - | - |
| 2.4693 | 26300 | 7.9484 | - | - |
| 2.4786 | 26400 | 7.9238 | - | - |
| 2.4880 | 26500 | 7.9329 | - | - |
| 2.4974 | 26600 | 7.9506 | - | - |
| 2.5068 | 26700 | 7.9568 | - | - |
| 2.5162 | 26800 | 7.9548 | - | - |
| 2.5256 | 26900 | 7.9097 | - | - |
| 2.5350 | 27000 | 7.9085 | - | - |
| 2.5444 | 27100 | 7.9368 | - | - |
| 2.5538 | 27200 | 7.9546 | - | - |
| 2.5631 | 27300 | 7.9255 | - | - |
| 2.5725 | 27400 | 7.9536 | - | - |
| 2.5819 | 27500 | 7.919 | - | - |
| 2.5913 | 27600 | 7.917 | - | - |
| 2.6007 | 27700 | 7.937 | - | - |
| 2.6101 | 27800 | 7.9159 | - | - |
| 2.6195 | 27900 | 7.9306 | - | - |
| 2.6289 | 28000 | 7.9592 | - | - |
| 2.6382 | 28100 | 7.9375 | - | - |
| 2.6476 | 28200 | 7.9225 | - | - |
| 2.6570 | 28300 | 7.958 | - | - |
| 2.6664 | 28400 | 7.9059 | - | - |
| 2.6758 | 28500 | 7.936 | - | - |
| 2.6852 | 28600 | 7.9138 | - | - |
| 2.6946 | 28700 | 7.9565 | - | - |
| 2.7040 | 28800 | 7.926 | - | - |
| 2.7134 | 28900 | 7.9365 | - | - |
| 2.7227 | 29000 | 7.9122 | - | - |
| 2.7321 | 29100 | 7.9196 | - | - |
| 2.7415 | 29200 | 7.9533 | - | - |
| 2.7509 | 29300 | 7.925 | - | - |
| 2.7603 | 29400 | 7.9594 | - | - |
| 2.7697 | 29500 | 7.9115 | - | - |
| 2.7791 | 29600 | 7.956 | - | - |
| 2.7885 | 29700 | 7.9394 | - | - |
| 2.7979 | 29800 | 7.9165 | - | - |
| 2.8072 | 29900 | 7.9471 | - | - |
| 2.8166 | 30000 | 7.9724 | 7.9237 | - |
| 2.8260 | 30100 | 7.9205 | - | - |
| 2.8354 | 30200 | 7.9513 | - | - |
| 2.8448 | 30300 | 7.9101 | - | - |
| 2.8542 | 30400 | 7.9237 | - | - |
| 2.8636 | 30500 | 7.9428 | - | - |
| 2.8730 | 30600 | 7.9408 | - | - |
| 2.8824 | 30700 | 7.956 | - | - |
| 2.8917 | 30800 | 7.9196 | - | - |
| 2.9011 | 30900 | 7.9262 | - | - |
| 2.9105 | 31000 | 7.9516 | - | - |
| 2.9199 | 31100 | 7.9086 | - | - |
| 2.9293 | 31200 | 7.9339 | - | - |
| 2.9387 | 31300 | 7.9334 | - | - |
| 2.9481 | 31400 | 7.9308 | - | - |
| 2.9575 | 31500 | 7.9569 | - | - |
| 2.9669 | 31600 | 7.9256 | - | - |
| 2.9762 | 31700 | 7.9108 | - | - |
| 2.9856 | 31800 | 7.9409 | - | - |
| 2.9950 | 31900 | 7.9159 | - | - |
| 3.0044 | 32000 | 7.8975 | - | - |
| 3.0138 | 32100 | 7.9583 | - | - |
| 3.0232 | 32200 | 7.9031 | - | - |
| 3.0326 | 32300 | 7.9448 | - | - |
| 3.0420 | 32400 | 7.9438 | - | - |
| 3.0514 | 32500 | 7.9284 | - | - |
| 3.0607 | 32600 | 7.9124 | - | - |
| 3.0701 | 32700 | 7.9153 | - | - |
| 3.0795 | 32800 | 7.9188 | - | - |
| 3.0889 | 32900 | 7.9358 | - | - |
| 3.0983 | 33000 | 7.9436 | - | - |
| 3.1077 | 33100 | 7.9492 | - | - |
| 3.1171 | 33200 | 7.9032 | - | - |
| 3.1265 | 33300 | 7.922 | - | - |
| 3.1359 | 33400 | 7.9677 | - | - |
| 3.1452 | 33500 | 7.9127 | - | - |
| 3.1546 | 33600 | 7.9381 | - | - |
| 3.1640 | 33700 | 7.9198 | - | - |
| 3.1734 | 33800 | 7.9183 | - | - |
| 3.1828 | 33900 | 7.9182 | - | - |
| 3.1922 | 34000 | 7.9261 | - | - |
| 3.2016 | 34100 | 7.9091 | - | - |
| 3.2110 | 34200 | 7.941 | - | - |
| 3.2204 | 34300 | 7.9239 | - | - |
| 3.2297 | 34400 | 7.9208 | - | - |
| 3.2391 | 34500 | 7.9499 | - | - |
| 3.2485 | 34600 | 7.9251 | - | - |
| 3.2579 | 34700 | 7.9219 | - | - |
| 3.2673 | 34800 | 7.9344 | - | - |
| 3.2767 | 34900 | 7.9496 | - | - |
| 3.2861 | 35000 | 7.9184 | 7.9239 | - |
| 3.2955 | 35100 | 7.9053 | - | - |
| 3.3049 | 35200 | 7.931 | - | - |
| 3.3142 | 35300 | 7.9347 | - | - |
| 3.3236 | 35400 | 7.9575 | - | - |
| 3.3330 | 35500 | 7.9259 | - | - |
| 3.3424 | 35600 | 7.9262 | - | - |
| 3.3518 | 35700 | 7.9206 | - | - |
| 3.3612 | 35800 | 7.9445 | - | - |
| 3.3706 | 35900 | 7.9043 | - | - |
| 3.3800 | 36000 | 7.9164 | - | - |
| 3.3894 | 36100 | 7.9199 | - | - |
| 3.3987 | 36200 | 7.9132 | - | - |
| 3.4081 | 36300 | 7.9163 | - | - |
| 3.4175 | 36400 | 7.9203 | - | - |
| 3.4269 | 36500 | 7.9491 | - | - |
| 3.4363 | 36600 | 7.9093 | - | - |
| 3.4457 | 36700 | 7.9271 | - | - |
| 3.4551 | 36800 | 7.9202 | - | - |
| 3.4645 | 36900 | 7.9193 | - | - |
| 3.4739 | 37000 | 7.9041 | - | - |
| 3.4832 | 37100 | 7.9284 | - | - |
| 3.4926 | 37200 | 7.9633 | - | - |
| 3.5020 | 37300 | 7.9078 | - | - |
| 3.5114 | 37400 | 7.9144 | - | - |
| 3.5208 | 37500 | 7.9011 | - | - |
| 3.5302 | 37600 | 7.9101 | - | - |
| 3.5396 | 37700 | 7.9331 | - | - |
| 3.5490 | 37800 | 7.9349 | - | - |
| 3.5584 | 37900 | 7.9272 | - | - |
| 3.5677 | 38000 | 7.9033 | - | - |
| 3.5771 | 38100 | 7.895 | - | - |
| 3.5865 | 38200 | 7.9082 | - | - |
| 3.5959 | 38300 | 7.9544 | - | - |
| 3.6053 | 38400 | 7.9063 | - | - |
| 3.6147 | 38500 | 7.9249 | - | - |
| 3.6241 | 38600 | 7.9124 | - | - |
| 3.6335 | 38700 | 7.9174 | - | - |
| 3.6429 | 38800 | 7.9275 | - | - |
| 3.6522 | 38900 | 7.9045 | - | - |
| 3.6616 | 39000 | 7.9327 | - | - |
| 3.6710 | 39100 | 7.9383 | - | - |
| 3.6804 | 39200 | 7.9134 | - | - |
| 3.6898 | 39300 | 7.925 | - | - |
| 3.6992 | 39400 | 7.9214 | - | - |
| 3.7086 | 39500 | 7.9207 | - | - |
| 3.7180 | 39600 | 7.9192 | - | - |
| 3.7273 | 39700 | 7.9194 | - | - |
| 3.7367 | 39800 | 7.9242 | - | - |
| 3.7461 | 39900 | 7.905 | - | - |
| 3.7555 | 40000 | 7.9278 | 7.9185 | - |
| 3.7649 | 40100 | 7.9147 | - | - |
| 3.7743 | 40200 | 7.9194 | - | - |
| 3.7837 | 40300 | 7.9004 | - | - |
| 3.7931 | 40400 | 7.9549 | - | - |
| 3.8025 | 40500 | 7.9326 | - | - |
| 3.8118 | 40600 | 7.9124 | - | - |
| 3.8212 | 40700 | 7.9355 | - | - |
| 3.8306 | 40800 | 7.926 | - | - |
| 3.8400 | 40900 | 7.9491 | - | - |
| 3.8494 | 41000 | 7.9163 | - | - |
| 3.8588 | 41100 | 7.9554 | - | - |
| 3.8682 | 41200 | 7.9162 | - | - |
| 3.8776 | 41300 | 7.8916 | - | - |
| 3.8870 | 41400 | 7.8969 | - | - |
| 3.8963 | 41500 | 7.9131 | - | - |
| 3.9057 | 41600 | 7.9272 | - | - |
| 3.9151 | 41700 | 7.9482 | - | - |
| 3.9245 | 41800 | 7.9168 | - | - |
| 3.9339 | 41900 | 7.9062 | - | - |
| 3.9433 | 42000 | 7.9238 | - | - |
| 3.9527 | 42100 | 7.9407 | - | - |
| 3.9621 | 42200 | 7.9482 | - | - |
| 3.9715 | 42300 | 7.9221 | - | - |
| 3.9808 | 42400 | 7.9221 | - | - |
| 3.9902 | 42500 | 7.9313 | - | - |
| 3.9996 | 42600 | 7.9441 | - | - |