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---
base_model: sentence-transformers/all-MiniLM-L6-v2
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1087179
- loss:TripletLoss
widget:
- source_sentence: hyperactive impulsive adhd
sentences:
- Claw Clip
- egyptian postage
- mug
- source_sentence: Work of Madness Hoodie
sentences:
- t-shirt
- towel
- men hoodie
- source_sentence: E7Lam Hoodie
sentences:
- Al Mady Hoodie
- waterfall cup
- hoodie
- source_sentence: Tote bag
sentences:
- Waterfall Mug
- hoodie
- linen tote bag
- source_sentence: Kimono
sentences:
- mug
- fringe kaftan
- shoes
model-index:
- name: all-MiniLM-L6-v2-triplet-loss
results:
- task:
type: triplet
name: Triplet
dataset:
name: all nli dev
type: all-nli-dev
metrics:
- type: cosine_accuracy
value: 0.9168454165823506
name: Cosine Accuracy
- type: dot_accuracy
value: 0.08315458341764934
name: Dot Accuracy
- type: manhattan_accuracy
value: 0.9135451351202193
name: Manhattan Accuracy
- type: euclidean_accuracy
value: 0.9168454165823506
name: Euclidean Accuracy
- type: max_accuracy
value: 0.9168454165823506
name: Max Accuracy
---
# all-MiniLM-L6-v2-triplet-loss
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) <!-- at revision ea78891063587eb050ed4166b20062eaf978037c -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **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 = [
'Kimono',
'fringe kaftan',
'mug',
]
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]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Dataset: `all-nli-dev`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | Value |
|:-------------------|:-----------|
| cosine_accuracy | 0.9168 |
| dot_accuracy | 0.0832 |
| manhattan_accuracy | 0.9135 |
| euclidean_accuracy | 0.9168 |
| **max_accuracy** | **0.9168** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training 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`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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
- `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
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | all-nli-dev_max_accuracy |
|:------:|:------:|:-------------:|:------:|:------------------------:|
| 0 | 0 | - | - | 0.9168 |
| 0.0029 | 100 | 4.7115 | - | - |
| 0.0059 | 200 | 4.6948 | - | - |
| 0.0088 | 300 | 4.6548 | - | - |
| 0.0118 | 400 | 4.6055 | - | - |
| 0.0147 | 500 | 4.5234 | 4.3878 | - |
| 0.0177 | 600 | 4.4338 | - | - |
| 0.0206 | 700 | 4.2938 | - | - |
| 0.0235 | 800 | 4.1176 | - | - |
| 0.0265 | 900 | 3.9373 | - | - |
| 0.0294 | 1000 | 3.7241 | 3.4721 | - |
| 0.0324 | 1100 | 3.5965 | - | - |
| 0.0353 | 1200 | 3.4949 | - | - |
| 0.0383 | 1300 | 3.4542 | - | - |
| 0.0412 | 1400 | 3.4345 | - | - |
| 0.0442 | 1500 | 3.3955 | 3.2453 | - |
| 0.0471 | 1600 | 3.3818 | - | - |
| 0.0500 | 1700 | 3.3608 | - | - |
| 0.0530 | 1800 | 3.3377 | - | - |
| 0.0559 | 1900 | 3.326 | - | - |
| 0.0589 | 2000 | 3.3061 | 3.1692 | - |
| 0.0618 | 2100 | 3.308 | - | - |
| 0.0648 | 2200 | 3.2887 | - | - |
| 0.0677 | 2300 | 3.2963 | - | - |
| 0.0706 | 2400 | 3.2744 | - | - |
| 0.0736 | 2500 | 3.2601 | 3.1416 | - |
| 0.0765 | 2600 | 3.271 | - | - |
| 0.0795 | 2700 | 3.2501 | - | - |
| 0.0824 | 2800 | 3.2536 | - | - |
| 0.0854 | 2900 | 3.2689 | - | - |
| 0.0883 | 3000 | 3.2362 | 3.1196 | - |
| 0.0912 | 3100 | 3.2281 | - | - |
| 0.0942 | 3200 | 3.2351 | - | - |
| 0.0971 | 3300 | 3.2173 | - | - |
| 0.1001 | 3400 | 3.2055 | - | - |
| 0.1030 | 3500 | 3.2198 | 3.1081 | - |
| 0.1060 | 3600 | 3.2116 | - | - |
| 0.1089 | 3700 | 3.2088 | - | - |
| 0.1118 | 3800 | 3.2043 | - | - |
| 0.1148 | 3900 | 3.1943 | - | - |
| 0.1177 | 4000 | 3.1897 | 3.1027 | - |
| 0.1207 | 4100 | 3.2131 | - | - |
| 0.1236 | 4200 | 3.198 | - | - |
| 0.1266 | 4300 | 3.1892 | - | - |
| 0.1295 | 4400 | 3.1753 | - | - |
| 0.1325 | 4500 | 3.1722 | 3.0840 | - |
| 0.1354 | 4600 | 3.1599 | - | - |
| 0.1383 | 4700 | 3.166 | - | - |
| 0.1413 | 4800 | 3.1585 | - | - |
| 0.1442 | 4900 | 3.1698 | - | - |
| 0.1472 | 5000 | 3.1766 | 3.0782 | - |
| 0.1501 | 5100 | 3.1515 | - | - |
| 0.1531 | 5200 | 3.1487 | - | - |
| 0.1560 | 5300 | 3.1579 | - | - |
| 0.1589 | 5400 | 3.1533 | - | - |
| 0.1619 | 5500 | 3.1433 | 3.0735 | - |
| 0.1648 | 5600 | 3.1454 | - | - |
| 0.1678 | 5700 | 3.1397 | - | - |
| 0.1707 | 5800 | 3.1422 | - | - |
| 0.1737 | 5900 | 3.1372 | - | - |
| 0.1766 | 6000 | 3.137 | 3.0710 | - |
| 0.1795 | 6100 | 3.1297 | - | - |
| 0.1825 | 6200 | 3.1202 | - | - |
| 0.1854 | 6300 | 3.1256 | - | - |
| 0.1884 | 6400 | 3.1185 | - | - |
| 0.1913 | 6500 | 3.1266 | 3.0667 | - |
| 0.1943 | 6600 | 3.1197 | - | - |
| 0.1972 | 6700 | 3.1286 | - | - |
| 0.2001 | 6800 | 3.1239 | - | - |
| 0.2031 | 6900 | 3.1166 | - | - |
| 0.2060 | 7000 | 3.1054 | 3.0664 | - |
| 0.2090 | 7100 | 3.1103 | - | - |
| 0.2119 | 7200 | 3.0929 | - | - |
| 0.2149 | 7300 | 3.1051 | - | - |
| 0.2178 | 7400 | 3.1023 | - | - |
| 0.2208 | 7500 | 3.0946 | 3.0636 | - |
| 0.2237 | 7600 | 3.0958 | - | - |
| 0.2266 | 7700 | 3.0907 | - | - |
| 0.2296 | 7800 | 3.1051 | - | - |
| 0.2325 | 7900 | 3.0965 | - | - |
| 0.2355 | 8000 | 3.0954 | 3.0617 | - |
| 0.2384 | 8100 | 3.0693 | - | - |
| 0.2414 | 8200 | 3.0906 | - | - |
| 0.2443 | 8300 | 3.0881 | - | - |
| 0.2472 | 8400 | 3.0867 | - | - |
| 0.2502 | 8500 | 3.0867 | 3.0610 | - |
| 0.2531 | 8600 | 3.0909 | - | - |
| 0.2561 | 8700 | 3.0877 | - | - |
| 0.2590 | 8800 | 3.0837 | - | - |
| 0.2620 | 8900 | 3.0865 | - | - |
| 0.2649 | 9000 | 3.0846 | 3.0607 | - |
| 0.2678 | 9100 | 3.0798 | - | - |
| 0.2708 | 9200 | 3.0928 | - | - |
| 0.2737 | 9300 | 3.0794 | - | - |
| 0.2767 | 9400 | 3.0797 | - | - |
| 0.2796 | 9500 | 3.0685 | 3.0623 | - |
| 0.2826 | 9600 | 3.0768 | - | - |
| 0.2855 | 9700 | 3.0657 | - | - |
| 0.2884 | 9800 | 3.0838 | - | - |
| 0.2914 | 9900 | 3.0775 | - | - |
| 0.2943 | 10000 | 3.0667 | 3.0587 | - |
| 0.2973 | 10100 | 3.088 | - | - |
| 0.3002 | 10200 | 3.0824 | - | - |
| 0.3032 | 10300 | 3.0754 | - | - |
| 0.3061 | 10400 | 3.064 | - | - |
| 0.3091 | 10500 | 3.0637 | 3.0578 | - |
| 0.3120 | 10600 | 3.0754 | - | - |
| 0.3149 | 10700 | 3.0703 | - | - |
| 0.3179 | 10800 | 3.0697 | - | - |
| 0.3208 | 10900 | 3.0635 | - | - |
| 0.3238 | 11000 | 3.0872 | 3.0573 | - |
| 0.3267 | 11100 | 3.0722 | - | - |
| 0.3297 | 11200 | 3.0633 | - | - |
| 0.3326 | 11300 | 3.058 | - | - |
| 0.3355 | 11400 | 3.0601 | - | - |
| 0.3385 | 11500 | 3.0732 | 3.0583 | - |
| 0.3414 | 11600 | 3.0565 | - | - |
| 0.3444 | 11700 | 3.0735 | - | - |
| 0.3473 | 11800 | 3.0656 | - | - |
| 0.3503 | 11900 | 3.0583 | - | - |
| 0.3532 | 12000 | 3.0714 | 3.0574 | - |
| 0.3561 | 12100 | 3.0647 | - | - |
| 0.3591 | 12200 | 3.0522 | - | - |
| 0.3620 | 12300 | 3.0668 | - | - |
| 0.3650 | 12400 | 3.071 | - | - |
| 0.3679 | 12500 | 3.0667 | 3.0556 | - |
| 0.3709 | 12600 | 3.0568 | - | - |
| 0.3738 | 12700 | 3.0642 | - | - |
| 0.3767 | 12800 | 3.0607 | - | - |
| 0.3797 | 12900 | 3.0679 | - | - |
| 0.3826 | 13000 | 3.0547 | 3.0547 | - |
| 0.3856 | 13100 | 3.0714 | - | - |
| 0.3885 | 13200 | 3.0692 | - | - |
| 0.3915 | 13300 | 3.0597 | - | - |
| 0.3944 | 13400 | 3.067 | - | - |
| 0.3974 | 13500 | 3.0626 | 3.0551 | - |
| 0.4003 | 13600 | 3.0708 | - | - |
| 0.4032 | 13700 | 3.065 | - | - |
| 0.4062 | 13800 | 3.0619 | - | - |
| 0.4091 | 13900 | 3.0556 | - | - |
| 0.4121 | 14000 | 3.0708 | 3.0524 | - |
| 0.4150 | 14100 | 3.0634 | - | - |
| 0.4180 | 14200 | 3.0605 | - | - |
| 0.4209 | 14300 | 3.0555 | - | - |
| 0.4238 | 14400 | 3.0624 | - | - |
| 0.4268 | 14500 | 3.0468 | 3.0510 | - |
| 0.4297 | 14600 | 3.0534 | - | - |
| 0.4327 | 14700 | 3.0671 | - | - |
| 0.4356 | 14800 | 3.0714 | - | - |
| 0.4386 | 14900 | 3.0493 | - | - |
| 0.4415 | 15000 | 3.0457 | 3.0467 | - |
| 0.4444 | 15100 | 3.0599 | - | - |
| 0.4474 | 15200 | 3.0554 | - | - |
| 0.4503 | 15300 | 3.0466 | - | - |
| 0.4533 | 15400 | 3.0471 | - | - |
| 0.4562 | 15500 | 3.0465 | 3.0500 | - |
| 0.4592 | 15600 | 3.0556 | - | - |
| 0.4621 | 15700 | 3.0444 | - | - |
| 0.4650 | 15800 | 3.0468 | - | - |
| 0.4680 | 15900 | 3.0554 | - | - |
| 0.4709 | 16000 | 3.0573 | 3.0469 | - |
| 0.4739 | 16100 | 3.049 | - | - |
| 0.4768 | 16200 | 3.0539 | - | - |
| 0.4798 | 16300 | 3.052 | - | - |
| 0.4827 | 16400 | 3.0538 | - | - |
| 0.4857 | 16500 | 3.045 | 3.0444 | - |
| 0.4886 | 16600 | 3.0381 | - | - |
| 0.4915 | 16700 | 3.0517 | - | - |
| 0.4945 | 16800 | 3.0598 | - | - |
| 0.4974 | 16900 | 3.046 | - | - |
| 0.5004 | 17000 | 3.0478 | 3.0447 | - |
| 0.5033 | 17100 | 3.054 | - | - |
| 0.5063 | 17200 | 3.0471 | - | - |
| 0.5092 | 17300 | 3.0383 | - | - |
| 0.5121 | 17400 | 3.0539 | - | - |
| 0.5151 | 17500 | 3.0457 | 3.0432 | - |
| 0.5180 | 17600 | 3.05 | - | - |
| 0.5210 | 17700 | 3.05 | - | - |
| 0.5239 | 17800 | 3.0512 | - | - |
| 0.5269 | 17900 | 3.0399 | - | - |
| 0.5298 | 18000 | 3.048 | 3.0431 | - |
| 0.5327 | 18100 | 3.0367 | - | - |
| 0.5357 | 18200 | 3.0442 | - | - |
| 0.5386 | 18300 | 3.0472 | - | - |
| 0.5416 | 18400 | 3.0335 | - | - |
| 0.5445 | 18500 | 3.0465 | 3.0459 | - |
| 0.5475 | 18600 | 3.054 | - | - |
| 0.5504 | 18700 | 3.0489 | - | - |
| 0.5533 | 18800 | 3.037 | - | - |
| 0.5563 | 18900 | 3.0432 | - | - |
| 0.5592 | 19000 | 3.0401 | 3.0426 | - |
| 0.5622 | 19100 | 3.0369 | - | - |
| 0.5651 | 19200 | 3.0561 | - | - |
| 0.5681 | 19300 | 3.0469 | - | - |
| 0.5710 | 19400 | 3.0468 | - | - |
| 0.5740 | 19500 | 3.0455 | 3.0433 | - |
| 0.5769 | 19600 | 3.0512 | - | - |
| 0.5798 | 19700 | 3.0474 | - | - |
| 0.5828 | 19800 | 3.043 | - | - |
| 0.5857 | 19900 | 3.0473 | - | - |
| 0.5887 | 20000 | 3.0448 | 3.0415 | - |
| 0.5916 | 20100 | 3.0441 | - | - |
| 0.5946 | 20200 | 3.0403 | - | - |
| 0.5975 | 20300 | 3.0516 | - | - |
| 0.6004 | 20400 | 3.0459 | - | - |
| 0.6034 | 20500 | 3.0415 | 3.0415 | - |
| 0.6063 | 20600 | 3.034 | - | - |
| 0.6093 | 20700 | 3.0483 | - | - |
| 0.6122 | 20800 | 3.0538 | - | - |
| 0.6152 | 20900 | 3.0458 | - | - |
| 0.6181 | 21000 | 3.0445 | 3.0372 | - |
| 0.6210 | 21100 | 3.0414 | - | - |
| 0.6240 | 21200 | 3.0476 | - | - |
| 0.6269 | 21300 | 3.0638 | - | - |
| 0.6299 | 21400 | 3.0375 | - | - |
| 0.6328 | 21500 | 3.0425 | 3.0397 | - |
| 0.6358 | 21600 | 3.0394 | - | - |
| 0.6387 | 21700 | 3.0443 | - | - |
| 0.6416 | 21800 | 3.0381 | - | - |
| 0.6446 | 21900 | 3.0387 | - | - |
| 0.6475 | 22000 | 3.0255 | 3.0381 | - |
| 0.6505 | 22100 | 3.0355 | - | - |
| 0.6534 | 22200 | 3.0411 | - | - |
| 0.6564 | 22300 | 3.0436 | - | - |
| 0.6593 | 22400 | 3.038 | - | - |
| 0.6623 | 22500 | 3.0336 | 3.0325 | - |
| 0.6652 | 22600 | 3.0404 | - | - |
| 0.6681 | 22700 | 3.0374 | - | - |
| 0.6711 | 22800 | 3.0342 | - | - |
| 0.6740 | 22900 | 3.0385 | - | - |
| 0.6770 | 23000 | 3.0329 | 3.0342 | - |
| 0.6799 | 23100 | 3.0391 | - | - |
| 0.6829 | 23200 | 3.0366 | - | - |
| 0.6858 | 23300 | 3.0284 | - | - |
| 0.6887 | 23400 | 3.0328 | - | - |
| 0.6917 | 23500 | 3.0322 | 3.0333 | - |
| 0.6946 | 23600 | 3.0353 | - | - |
| 0.6976 | 23700 | 3.0371 | - | - |
| 0.7005 | 23800 | 3.0321 | - | - |
| 0.7035 | 23900 | 3.0365 | - | - |
| 0.7064 | 24000 | 3.0302 | 3.0342 | - |
| 0.7093 | 24100 | 3.0352 | - | - |
| 0.7123 | 24200 | 3.0277 | - | - |
| 0.7152 | 24300 | 3.0402 | - | - |
| 0.7182 | 24400 | 3.0364 | - | - |
| 0.7211 | 24500 | 3.0439 | 3.0336 | - |
| 0.7241 | 24600 | 3.0396 | - | - |
| 0.7270 | 24700 | 3.0475 | - | - |
| 0.7299 | 24800 | 3.0258 | - | - |
| 0.7329 | 24900 | 3.0345 | - | - |
| 0.7358 | 25000 | 3.0326 | 3.0350 | - |
| 0.7388 | 25100 | 3.0357 | - | - |
| 0.7417 | 25200 | 3.0413 | - | - |
| 0.7447 | 25300 | 3.0326 | - | - |
| 0.7476 | 25400 | 3.0401 | - | - |
| 0.7506 | 25500 | 3.0313 | 3.0365 | - |
| 0.7535 | 25600 | 3.04 | - | - |
| 0.7564 | 25700 | 3.0382 | - | - |
| 0.7594 | 25800 | 3.0344 | - | - |
| 0.7623 | 25900 | 3.0325 | - | - |
| 0.7653 | 26000 | 3.0475 | 3.0340 | - |
| 0.7682 | 26100 | 3.0256 | - | - |
| 0.7712 | 26200 | 3.0331 | - | - |
| 0.7741 | 26300 | 3.0325 | - | - |
| 0.7770 | 26400 | 3.0431 | - | - |
| 0.7800 | 26500 | 3.04 | 3.0372 | - |
| 0.7829 | 26600 | 3.0393 | - | - |
| 0.7859 | 26700 | 3.0374 | - | - |
| 0.7888 | 26800 | 3.0406 | - | - |
| 0.7918 | 26900 | 3.0343 | - | - |
| 0.7947 | 27000 | 3.0374 | 3.0325 | - |
| 0.7976 | 27100 | 3.0262 | - | - |
| 0.8006 | 27200 | 3.0393 | - | - |
| 0.8035 | 27300 | 3.0255 | - | - |
| 0.8065 | 27400 | 3.0305 | - | - |
| 0.8094 | 27500 | 3.0324 | 3.0323 | - |
| 0.8124 | 27600 | 3.0317 | - | - |
| 0.8153 | 27700 | 3.0267 | - | - |
| 0.8182 | 27800 | 3.0299 | - | - |
| 0.8212 | 27900 | 3.0305 | - | - |
| 0.8241 | 28000 | 3.0336 | 3.0319 | - |
| 0.8271 | 28100 | 3.0373 | - | - |
| 0.8300 | 28200 | 3.0342 | - | - |
| 0.8330 | 28300 | 3.0436 | - | - |
| 0.8359 | 28400 | 3.0354 | - | - |
| 0.8389 | 28500 | 3.0373 | 3.0291 | - |
| 0.8418 | 28600 | 3.0292 | - | - |
| 0.8447 | 28700 | 3.0229 | - | - |
| 0.8477 | 28800 | 3.0348 | - | - |
| 0.8506 | 28900 | 3.041 | - | - |
| 0.8536 | 29000 | 3.031 | 3.0324 | - |
| 0.8565 | 29100 | 3.0354 | - | - |
| 0.8595 | 29200 | 3.0242 | - | - |
| 0.8624 | 29300 | 3.026 | - | - |
| 0.8653 | 29400 | 3.0373 | - | - |
| 0.8683 | 29500 | 3.0298 | 3.0276 | - |
| 0.8712 | 29600 | 3.0341 | - | - |
| 0.8742 | 29700 | 3.0304 | - | - |
| 0.8771 | 29800 | 3.0241 | - | - |
| 0.8801 | 29900 | 3.0304 | - | - |
| 0.8830 | 30000 | 3.0279 | 3.0278 | - |
| 0.8859 | 30100 | 3.026 | - | - |
| 0.8889 | 30200 | 3.0272 | - | - |
| 0.8918 | 30300 | 3.0372 | - | - |
| 0.8948 | 30400 | 3.0241 | - | - |
| 0.8977 | 30500 | 3.0347 | 3.0276 | - |
| 0.9007 | 30600 | 3.0335 | - | - |
| 0.9036 | 30700 | 3.0316 | - | - |
| 0.9065 | 30800 | 3.0372 | - | - |
| 0.9095 | 30900 | 3.0234 | - | - |
| 0.9124 | 31000 | 3.0303 | 3.0278 | - |
| 0.9154 | 31100 | 3.0466 | - | - |
| 0.9183 | 31200 | 3.0391 | - | - |
| 0.9213 | 31300 | 3.0334 | - | - |
| 0.9242 | 31400 | 3.029 | - | - |
| 0.9272 | 31500 | 3.0322 | 3.0280 | - |
| 0.9301 | 31600 | 3.0272 | - | - |
| 0.9330 | 31700 | 3.0315 | - | - |
| 0.9360 | 31800 | 3.0297 | - | - |
| 0.9389 | 31900 | 3.0228 | - | - |
| 0.9419 | 32000 | 3.0246 | 3.0272 | - |
| 0.9448 | 32100 | 3.0215 | - | - |
| 0.9478 | 32200 | 3.0246 | - | - |
| 0.9507 | 32300 | 3.0333 | - | - |
| 0.9536 | 32400 | 3.0334 | - | - |
| 0.9566 | 32500 | 3.029 | 3.0271 | - |
| 0.9595 | 32600 | 3.0328 | - | - |
| 0.9625 | 32700 | 3.0284 | - | - |
| 0.9654 | 32800 | 3.0327 | - | - |
| 0.9684 | 32900 | 3.0228 | - | - |
| 0.9713 | 33000 | 3.0321 | 3.0267 | - |
| 0.9742 | 33100 | 3.0277 | - | - |
| 0.9772 | 33200 | 3.0309 | - | - |
| 0.9801 | 33300 | 3.0265 | - | - |
| 0.9831 | 33400 | 3.029 | - | - |
| 0.9860 | 33500 | 3.0315 | 3.0257 | - |
| 0.9890 | 33600 | 3.0233 | - | - |
| 0.9919 | 33700 | 3.0208 | - | - |
| 0.9948 | 33800 | 3.0296 | - | - |
| 0.9978 | 33900 | 3.0271 | - | - |
| 1.0007 | 34000 | 3.0258 | 3.0261 | - |
| 1.0037 | 34100 | 3.0233 | - | - |
| 1.0066 | 34200 | 3.0283 | - | - |
| 1.0096 | 34300 | 3.0277 | - | - |
| 1.0125 | 34400 | 3.0233 | - | - |
| 1.0155 | 34500 | 3.0296 | 3.0270 | - |
| 1.0184 | 34600 | 3.0321 | - | - |
| 1.0213 | 34700 | 3.0314 | - | - |
| 1.0243 | 34800 | 3.0458 | - | - |
| 1.0272 | 34900 | 3.0415 | - | - |
| 1.0302 | 35000 | 3.0271 | 3.0261 | - |
| 1.0331 | 35100 | 3.0252 | - | - |
| 1.0361 | 35200 | 3.0327 | - | - |
| 1.0390 | 35300 | 3.0302 | - | - |
| 1.0419 | 35400 | 3.0264 | - | - |
| 1.0449 | 35500 | 3.0314 | 3.0269 | - |
| 1.0478 | 35600 | 3.0252 | - | - |
| 1.0508 | 35700 | 3.0302 | - | - |
| 1.0537 | 35800 | 3.0339 | - | - |
| 1.0567 | 35900 | 3.0277 | - | - |
| 1.0596 | 36000 | 3.0314 | 3.0232 | - |
| 1.0625 | 36100 | 3.0339 | - | - |
| 1.0655 | 36200 | 3.0233 | - | - |
| 1.0684 | 36300 | 3.0264 | - | - |
| 1.0714 | 36400 | 3.0246 | - | - |
| 1.0743 | 36500 | 3.0252 | 3.0242 | - |
| 1.0773 | 36600 | 3.027 | - | - |
| 1.0802 | 36700 | 3.0202 | - | - |
| 1.0831 | 36800 | 3.0245 | - | - |
| 1.0861 | 36900 | 3.0239 | - | - |
| 1.0890 | 37000 | 3.022 | 3.0229 | - |
| 1.0920 | 37100 | 3.0164 | - | - |
| 1.0949 | 37200 | 3.0289 | - | - |
| 1.0979 | 37300 | 3.012 | - | - |
| 1.1008 | 37400 | 3.027 | - | - |
| 1.1038 | 37500 | 3.0283 | 3.0229 | - |
| 1.1067 | 37600 | 3.0289 | - | - |
| 1.1096 | 37700 | 3.0264 | - | - |
| 1.1126 | 37800 | 3.0295 | - | - |
| 1.1155 | 37900 | 3.0245 | - | - |
| 1.1185 | 38000 | 3.0301 | 3.0226 | - |
| 1.1214 | 38100 | 3.0276 | - | - |
| 1.1244 | 38200 | 3.0264 | - | - |
| 1.1273 | 38300 | 3.0264 | - | - |
| 1.1302 | 38400 | 3.022 | - | - |
| 1.1332 | 38500 | 3.0308 | 3.0243 | - |
| 1.1361 | 38600 | 3.022 | - | - |
| 1.1391 | 38700 | 3.027 | - | - |
| 1.1420 | 38800 | 3.0189 | - | - |
| 1.1450 | 38900 | 3.0282 | - | - |
| 1.1479 | 39000 | 3.0226 | 3.0228 | - |
| 1.1508 | 39100 | 3.0257 | - | - |
| 1.1538 | 39200 | 3.0201 | - | - |
| 1.1567 | 39300 | 3.0282 | - | - |
| 1.1597 | 39400 | 3.0395 | - | - |
| 1.1626 | 39500 | 3.042 | 3.0340 | - |
| 1.1656 | 39600 | 3.0432 | - | - |
| 1.1685 | 39700 | 3.0214 | - | - |
| 1.1714 | 39800 | 3.022 | - | - |
| 1.1744 | 39900 | 3.0245 | - | - |
| 1.1773 | 40000 | 3.032 | 3.0276 | - |
| 1.1803 | 40100 | 3.0389 | - | - |
| 1.1832 | 40200 | 3.0332 | - | - |
| 1.1862 | 40300 | 3.0689 | - | - |
| 1.1891 | 40400 | 3.0476 | - | - |
| 1.1921 | 40500 | 3.0626 | 3.0399 | - |
| 1.1950 | 40600 | 3.0357 | - | - |
| 1.1979 | 40700 | 3.0282 | - | - |
| 1.2009 | 40800 | 3.0276 | - | - |
| 1.2038 | 40900 | 3.032 | - | - |
| 1.2068 | 41000 | 3.0189 | 3.0256 | - |
| 1.2097 | 41100 | 3.0276 | - | - |
| 1.2127 | 41200 | 3.0276 | - | - |
| 1.2156 | 41300 | 3.0276 | - | - |
| 1.2185 | 41400 | 3.0301 | - | - |
| 1.2215 | 41500 | 3.0238 | 3.0262 | - |
| 1.2244 | 41600 | 3.0326 | - | - |
| 1.2274 | 41700 | 3.0295 | - | - |
| 1.2303 | 41800 | 3.0307 | - | - |
| 1.2333 | 41900 | 3.0351 | - | - |
| 1.2362 | 42000 | 3.0301 | 3.0242 | - |
| 1.2391 | 42100 | 3.0238 | - | - |
| 1.2421 | 42200 | 3.0232 | - | - |
| 1.2450 | 42300 | 3.0301 | - | - |
| 1.2480 | 42400 | 3.0201 | - | - |
| 1.2509 | 42500 | 3.0295 | 3.0242 | - |
| 1.2539 | 42600 | 3.0326 | - | - |
| 1.2568 | 42700 | 3.0232 | - | - |
| 1.2597 | 42800 | 3.0213 | - | - |
| 1.2627 | 42900 | 3.0263 | - | - |
| 1.2656 | 43000 | 3.0351 | 3.0236 | - |
| 1.2686 | 43100 | 3.0295 | - | - |
| 1.2715 | 43200 | 3.0232 | - | - |
| 1.2745 | 43300 | 3.0207 | - | - |
| 1.2774 | 43400 | 3.027 | - | - |
| 1.2804 | 43500 | 3.0276 | 3.0234 | - |
| 1.2833 | 43600 | 3.0257 | - | - |
| 1.2862 | 43700 | 3.0263 | - | - |
| 1.2892 | 43800 | 3.0163 | - | - |
| 1.2921 | 43900 | 3.0282 | - | - |
| 1.2951 | 44000 | 3.0276 | 3.0270 | - |
| 1.2980 | 44100 | 3.032 | - | - |
| 1.3010 | 44200 | 3.0326 | - | - |
| 1.3039 | 44300 | 3.0288 | - | - |
| 1.3068 | 44400 | 3.0263 | - | - |
| 1.3098 | 44500 | 3.0251 | 3.0231 | - |
| 1.3127 | 44600 | 3.0188 | - | - |
| 1.3157 | 44700 | 3.0213 | - | - |
| 1.3186 | 44800 | 3.0157 | - | - |
| 1.3216 | 44900 | 3.0238 | - | - |
| 1.3245 | 45000 | 3.0263 | 3.0214 | - |
| 1.3274 | 45100 | 3.0194 | - | - |
| 1.3304 | 45200 | 3.0301 | - | - |
| 1.3333 | 45300 | 3.0232 | - | - |
| 1.3363 | 45400 | 3.0163 | - | - |
| 1.3392 | 45500 | 3.0157 | 3.0214 | - |
| 1.3422 | 45600 | 3.0219 | - | - |
| 1.3451 | 45700 | 3.0169 | - | - |
| 1.3481 | 45800 | 3.0232 | - | - |
| 1.3510 | 45900 | 3.0344 | - | - |
| 1.3539 | 46000 | 3.0219 | 3.0209 | - |
| 1.3569 | 46100 | 3.0183 | - | - |
| 1.3598 | 46200 | 3.0207 | - | - |
| 1.3628 | 46300 | 3.0351 | - | - |
| 1.3657 | 46400 | 3.0244 | - | - |
| 1.3687 | 46500 | 3.0194 | 3.0208 | - |
| 1.3716 | 46600 | 3.0176 | - | - |
| 1.3745 | 46700 | 3.0244 | - | - |
| 1.3775 | 46800 | 3.0263 | - | - |
| 1.3804 | 46900 | 3.0151 | - | - |
| 1.3834 | 47000 | 3.0226 | 3.0208 | - |
| 1.3863 | 47100 | 3.0213 | - | - |
| 1.3893 | 47200 | 3.0307 | - | - |
| 1.3922 | 47300 | 3.0244 | - | - |
| 1.3951 | 47400 | 3.0238 | - | - |
| 1.3981 | 47500 | 3.0276 | 3.0207 | - |
| 1.4010 | 47600 | 3.0282 | - | - |
| 1.4040 | 47700 | 3.0201 | - | - |
| 1.4069 | 47800 | 3.0226 | - | - |
| 1.4099 | 47900 | 3.0263 | - | - |
| 1.4128 | 48000 | 3.0213 | 3.0208 | - |
| 1.4157 | 48100 | 3.0201 | - | - |
| 1.4187 | 48200 | 3.0207 | - | - |
| 1.4216 | 48300 | 3.0288 | - | - |
| 1.4246 | 48400 | 3.0182 | - | - |
| 1.4275 | 48500 | 3.0263 | 3.0200 | - |
| 1.4305 | 48600 | 3.0207 | - | - |
| 1.4334 | 48700 | 3.0332 | - | - |
| 1.4364 | 48800 | 3.0201 | - | - |
| 1.4393 | 48900 | 3.0182 | - | - |
| 1.4422 | 49000 | 3.0188 | 3.0200 | - |
| 1.4452 | 49100 | 3.0213 | - | - |
| 1.4481 | 49200 | 3.0144 | - | - |
| 1.4511 | 49300 | 3.0257 | - | - |
| 1.4540 | 49400 | 3.0201 | - | - |
| 1.4570 | 49500 | 3.0238 | 3.0191 | - |
| 1.4599 | 49600 | 3.0294 | - | - |
| 1.4628 | 49700 | 3.0226 | - | - |
| 1.4658 | 49800 | 3.0194 | - | - |
| 1.4687 | 49900 | 3.0169 | - | - |
| 1.4717 | 50000 | 3.0207 | 3.0189 | - |
| 1.4746 | 50100 | 3.0219 | - | - |
| 1.4776 | 50200 | 3.0194 | - | - |
| 1.4805 | 50300 | 3.0126 | - | - |
| 1.4834 | 50400 | 3.0194 | - | - |
| 1.4864 | 50500 | 3.0163 | 3.0208 | - |
| 1.4893 | 50600 | 3.0182 | - | - |
| 1.4923 | 50700 | 3.0169 | - | - |
| 1.4952 | 50800 | 3.0188 | - | - |
| 1.4982 | 50900 | 3.0219 | - | - |
| 1.5011 | 51000 | 3.0169 | 3.0200 | - |
| 1.5040 | 51100 | 3.0294 | - | - |
| 1.5070 | 51200 | 3.0207 | - | - |
| 1.5099 | 51300 | 3.02 | - | - |
| 1.5129 | 51400 | 3.0207 | - | - |
| 1.5158 | 51500 | 3.0175 | 3.0196 | - |
| 1.5188 | 51600 | 3.0225 | - | - |
| 1.5217 | 51700 | 3.0213 | - | - |
| 1.5247 | 51800 | 3.02 | - | - |
| 1.5276 | 51900 | 3.0232 | - | - |
| 1.5305 | 52000 | 3.0275 | 3.0188 | - |
| 1.5335 | 52100 | 3.0169 | - | - |
| 1.5364 | 52200 | 3.02 | - | - |
| 1.5394 | 52300 | 3.0232 | - | - |
| 1.5423 | 52400 | 3.0125 | - | - |
| 1.5453 | 52500 | 3.0163 | 3.0188 | - |
| 1.5482 | 52600 | 3.0163 | - | - |
| 1.5511 | 52700 | 3.0269 | - | - |
| 1.5541 | 52800 | 3.0194 | - | - |
| 1.5570 | 52900 | 3.0238 | - | - |
| 1.5600 | 53000 | 3.02 | 3.0183 | - |
| 1.5629 | 53100 | 3.0175 | - | - |
| 1.5659 | 53200 | 3.0157 | - | - |
| 1.5688 | 53300 | 3.0157 | - | - |
| 1.5717 | 53400 | 3.0232 | - | - |
| 1.5747 | 53500 | 3.0238 | 3.0182 | - |
| 1.5776 | 53600 | 3.0207 | - | - |
| 1.5806 | 53700 | 3.0182 | - | - |
| 1.5835 | 53800 | 3.0213 | - | - |
| 1.5865 | 53900 | 3.0213 | - | - |
| 1.5894 | 54000 | 3.0125 | 3.0181 | - |
| 1.5923 | 54100 | 3.0119 | - | - |
| 1.5953 | 54200 | 3.0194 | - | - |
| 1.5982 | 54300 | 3.0125 | - | - |
| 1.6012 | 54400 | 3.0257 | - | - |
| 1.6041 | 54500 | 3.02 | 3.0181 | - |
| 1.6071 | 54600 | 3.0232 | - | - |
| 1.6100 | 54700 | 3.025 | - | - |
| 1.6130 | 54800 | 3.0263 | - | - |
| 1.6159 | 54900 | 3.0144 | - | - |
| 1.6188 | 55000 | 3.0138 | 3.0177 | - |
| 1.6218 | 55100 | 3.0207 | - | - |
| 1.6247 | 55200 | 3.015 | - | - |
| 1.6277 | 55300 | 3.0175 | - | - |
| 1.6306 | 55400 | 3.0163 | - | - |
| 1.6336 | 55500 | 3.0157 | 3.0172 | - |
| 1.6365 | 55600 | 3.01 | - | - |
| 1.6394 | 55700 | 3.0132 | - | - |
| 1.6424 | 55800 | 3.0232 | - | - |
| 1.6453 | 55900 | 3.02 | - | - |
| 1.6483 | 56000 | 3.0163 | 3.0145 | - |
| 1.6512 | 56100 | 3.0132 | - | - |
| 1.6542 | 56200 | 3.0219 | - | - |
| 1.6571 | 56300 | 3.0188 | - | - |
| 1.6600 | 56400 | 3.015 | - | - |
| 1.6630 | 56500 | 3.0157 | 3.0146 | - |
| 1.6659 | 56600 | 3.0188 | - | - |
| 1.6689 | 56700 | 3.0225 | - | - |
| 1.6718 | 56800 | 3.0094 | - | - |
| 1.6748 | 56900 | 3.0163 | - | - |
| 1.6777 | 57000 | 3.0244 | 3.0158 | - |
| 1.6806 | 57100 | 3.0157 | - | - |
| 1.6836 | 57200 | 3.0157 | - | - |
| 1.6865 | 57300 | 3.015 | - | - |
| 1.6895 | 57400 | 3.0125 | - | - |
| 1.6924 | 57500 | 3.0169 | 3.0151 | - |
| 1.6954 | 57600 | 3.02 | - | - |
| 1.6983 | 57700 | 3.0138 | - | - |
| 1.7013 | 57800 | 3.0163 | - | - |
| 1.7042 | 57900 | 3.0169 | - | - |
| 1.7071 | 58000 | 3.0169 | 3.0153 | - |
| 1.7101 | 58100 | 3.0119 | - | - |
| 1.7130 | 58200 | 3.0132 | - | - |
| 1.7160 | 58300 | 3.0138 | - | - |
| 1.7189 | 58400 | 3.0225 | - | - |
| 1.7219 | 58500 | 3.02 | 3.0148 | - |
| 1.7248 | 58600 | 3.015 | - | - |
| 1.7277 | 58700 | 3.0188 | - | - |
| 1.7307 | 58800 | 3.015 | - | - |
| 1.7336 | 58900 | 3.015 | - | - |
| 1.7366 | 59000 | 3.0082 | 3.0148 | - |
| 1.7395 | 59100 | 3.0213 | - | - |
| 1.7425 | 59200 | 3.0094 | - | - |
| 1.7454 | 59300 | 3.0188 | - | - |
| 1.7483 | 59400 | 3.0138 | - | - |
| 1.7513 | 59500 | 3.0138 | 3.0148 | - |
| 1.7542 | 59600 | 3.0188 | - | - |
| 1.7572 | 59700 | 3.0107 | - | - |
| 1.7601 | 59800 | 3.0119 | - | - |
| 1.7631 | 59900 | 3.015 | - | - |
| 1.7660 | 60000 | 3.0194 | 3.0147 | - |
| 1.7689 | 60100 | 3.0144 | - | - |
| 1.7719 | 60200 | 3.0182 | - | - |
| 1.7748 | 60300 | 3.0213 | - | - |
| 1.7778 | 60400 | 3.0144 | - | - |
| 1.7807 | 60500 | 3.0157 | 3.0147 | - |
| 1.7837 | 60600 | 3.0132 | - | - |
| 1.7866 | 60700 | 3.0163 | - | - |
| 1.7896 | 60800 | 3.0182 | - | - |
| 1.7925 | 60900 | 3.015 | - | - |
| 1.7954 | 61000 | 3.0088 | 3.0148 | - |
| 1.7984 | 61100 | 3.015 | - | - |
| 1.8013 | 61200 | 3.0144 | - | - |
| 1.8043 | 61300 | 3.0113 | - | - |
| 1.8072 | 61400 | 3.0182 | - | - |
| 1.8102 | 61500 | 3.0194 | 3.0147 | - |
| 1.8131 | 61600 | 3.02 | - | - |
| 1.8160 | 61700 | 3.0125 | - | - |
| 1.8190 | 61800 | 3.015 | - | - |
| 1.8219 | 61900 | 3.0175 | - | - |
| 1.8249 | 62000 | 3.0119 | 3.0146 | - |
| 1.8278 | 62100 | 3.0169 | - | - |
| 1.8308 | 62200 | 3.0225 | - | - |
| 1.8337 | 62300 | 3.0207 | - | - |
| 1.8366 | 62400 | 3.0169 | - | - |
| 1.8396 | 62500 | 3.0125 | 3.0170 | - |
| 1.8425 | 62600 | 3.0188 | - | - |
| 1.8455 | 62700 | 3.0157 | - | - |
| 1.8484 | 62800 | 3.0182 | - | - |
| 1.8514 | 62900 | 3.01 | - | - |
| 1.8543 | 63000 | 3.0138 | 3.0148 | - |
| 1.8572 | 63100 | 3.0094 | - | - |
| 1.8602 | 63200 | 3.0157 | - | - |
| 1.8631 | 63300 | 3.02 | - | - |
| 1.8661 | 63400 | 3.0094 | - | - |
| 1.8690 | 63500 | 3.0182 | 3.0145 | - |
| 1.8720 | 63600 | 3.0157 | - | - |
| 1.8749 | 63700 | 3.0138 | - | - |
| 1.8779 | 63800 | 3.0125 | - | - |
| 1.8808 | 63900 | 3.015 | - | - |
| 1.8837 | 64000 | 3.0075 | 3.0144 | - |
| 1.8867 | 64100 | 3.0157 | - | - |
| 1.8896 | 64200 | 3.0088 | - | - |
| 1.8926 | 64300 | 3.0225 | - | - |
| 1.8955 | 64400 | 3.0175 | - | - |
| 1.8985 | 64500 | 3.0232 | 3.0179 | - |
| 1.9014 | 64600 | 3.0257 | - | - |
| 1.9043 | 64700 | 3.0175 | - | - |
| 1.9073 | 64800 | 3.0188 | - | - |
| 1.9102 | 64900 | 3.0125 | - | - |
| 1.9132 | 65000 | 3.0225 | 3.0170 | - |
| 1.9161 | 65100 | 3.02 | - | - |
| 1.9191 | 65200 | 3.0213 | - | - |
| 1.9220 | 65300 | 3.0113 | - | - |
| 1.9249 | 65400 | 3.0182 | - | - |
| 1.9279 | 65500 | 3.0232 | 3.0169 | - |
| 1.9308 | 65600 | 3.0225 | - | - |
| 1.9338 | 65700 | 3.0181 | - | - |
| 1.9367 | 65800 | 3.0181 | - | - |
| 1.9397 | 65900 | 3.0194 | - | - |
| 1.9426 | 66000 | 3.0175 | 3.0168 | - |
| 1.9455 | 66100 | 3.0181 | - | - |
| 1.9485 | 66200 | 3.0157 | - | - |
| 1.9514 | 66300 | 3.0169 | - | - |
| 1.9544 | 66400 | 3.0181 | - | - |
| 1.9573 | 66500 | 3.0138 | 3.0152 | - |
| 1.9603 | 66600 | 3.0175 | - | - |
| 1.9632 | 66700 | 3.0156 | - | - |
| 1.9662 | 66800 | 3.0106 | - | - |
| 1.9691 | 66900 | 3.01 | - | - |
| 1.9720 | 67000 | 3.0175 | 3.0141 | - |
| 1.9750 | 67100 | 3.0144 | - | - |
| 1.9779 | 67200 | 3.0131 | - | - |
| 1.9809 | 67300 | 3.0113 | - | - |
| 1.9838 | 67400 | 3.0113 | - | - |
| 1.9868 | 67500 | 3.0125 | 3.0140 | - |
| 1.9897 | 67600 | 3.0119 | - | - |
| 1.9926 | 67700 | 3.02 | - | - |
| 1.9956 | 67800 | 3.0125 | - | - |
| 1.9985 | 67900 | 3.01 | - | - |
| 2.0015 | 68000 | 3.0156 | 3.0139 | - |
| 2.0044 | 68100 | 3.0131 | - | - |
| 2.0074 | 68200 | 3.015 | - | - |
| 2.0103 | 68300 | 3.0169 | - | - |
| 2.0132 | 68400 | 3.0169 | - | - |
| 2.0162 | 68500 | 3.0119 | 3.0139 | - |
| 2.0191 | 68600 | 3.0138 | - | - |
| 2.0221 | 68700 | 3.0138 | - | - |
| 2.0250 | 68800 | 3.0163 | - | - |
| 2.0280 | 68900 | 3.0188 | - | - |
| 2.0309 | 69000 | 3.0188 | 3.0139 | - |
| 2.0338 | 69100 | 3.01 | - | - |
| 2.0368 | 69200 | 3.015 | - | - |
| 2.0397 | 69300 | 3.0175 | - | - |
| 2.0427 | 69400 | 3.0144 | - | - |
| 2.0456 | 69500 | 3.0188 | 3.0139 | - |
| 2.0486 | 69600 | 3.0119 | - | - |
| 2.0515 | 69700 | 3.0131 | - | - |
| 2.0545 | 69800 | 3.0131 | - | - |
| 2.0574 | 69900 | 3.0144 | - | - |
| 2.0603 | 70000 | 3.0144 | 3.0139 | - |
| 2.0633 | 70100 | 3.0163 | - | - |
| 2.0662 | 70200 | 3.0069 | - | - |
| 2.0692 | 70300 | 3.0213 | - | - |
| 2.0721 | 70400 | 3.0188 | - | - |
| 2.0751 | 70500 | 3.0131 | 3.0108 | - |
| 2.0780 | 70600 | 3.0131 | - | - |
| 2.0809 | 70700 | 3.0094 | - | - |
| 2.0839 | 70800 | 3.0131 | - | - |
| 2.0868 | 70900 | 3.0119 | - | - |
| 2.0898 | 71000 | 3.0106 | 3.0117 | - |
| 2.0927 | 71100 | 3.015 | - | - |
| 2.0957 | 71200 | 3.0106 | - | - |
| 2.0986 | 71300 | 3.0106 | - | - |
| 2.1015 | 71400 | 3.0113 | - | - |
| 2.1045 | 71500 | 3.01 | 3.0117 | - |
| 2.1074 | 71600 | 3.01 | - | - |
| 2.1104 | 71700 | 3.0138 | - | - |
| 2.1133 | 71800 | 3.0088 | - | - |
| 2.1163 | 71900 | 3.0106 | - | - |
| 2.1192 | 72000 | 3.0069 | 3.0111 | - |
| 2.1221 | 72100 | 3.0056 | - | - |
| 2.1251 | 72200 | 3.0156 | - | - |
| 2.1280 | 72300 | 3.0094 | - | - |
| 2.1310 | 72400 | 3.0081 | - | - |
| 2.1339 | 72500 | 3.0125 | 3.0112 | - |
| 2.1369 | 72600 | 3.0125 | - | - |
| 2.1398 | 72700 | 3.0144 | - | - |
| 2.1428 | 72800 | 3.0156 | - | - |
| 2.1457 | 72900 | 3.0094 | - | - |
| 2.1486 | 73000 | 3.0075 | 3.0112 | - |
| 2.1516 | 73100 | 3.0119 | - | - |
| 2.1545 | 73200 | 3.0088 | - | - |
| 2.1575 | 73300 | 3.0119 | - | - |
| 2.1604 | 73400 | 3.0131 | - | - |
| 2.1634 | 73500 | 3.0094 | 3.0110 | - |
| 2.1663 | 73600 | 3.0063 | - | - |
| 2.1692 | 73700 | 3.0138 | - | - |
| 2.1722 | 73800 | 3.0094 | - | - |
| 2.1751 | 73900 | 3.0144 | - | - |
| 2.1781 | 74000 | 3.0081 | 3.0109 | - |
| 2.1810 | 74100 | 3.0138 | - | - |
| 2.1840 | 74200 | 3.0144 | - | - |
| 2.1869 | 74300 | 3.0094 | - | - |
| 2.1898 | 74400 | 3.0106 | - | - |
| 2.1928 | 74500 | 3.01 | 3.0110 | - |
| 2.1957 | 74600 | 3.0088 | - | - |
| 2.1987 | 74700 | 3.0081 | - | - |
| 2.2016 | 74800 | 3.0094 | - | - |
| 2.2046 | 74900 | 3.01 | - | - |
| 2.2075 | 75000 | 3.0181 | 3.0108 | - |
| 2.2104 | 75100 | 3.0088 | - | - |
| 2.2134 | 75200 | 3.0144 | - | - |
| 2.2163 | 75300 | 3.0131 | - | - |
| 2.2193 | 75400 | 3.01 | - | - |
| 2.2222 | 75500 | 3.0125 | 3.0112 | - |
| 2.2252 | 75600 | 3.0131 | - | - |
| 2.2281 | 75700 | 3.0125 | - | - |
| 2.2311 | 75800 | 3.01 | - | - |
| 2.2340 | 75900 | 3.01 | - | - |
| 2.2369 | 76000 | 3.0175 | 3.0112 | - |
| 2.2399 | 76100 | 3.0094 | - | - |
| 2.2428 | 76200 | 3.015 | - | - |
| 2.2458 | 76300 | 3.0075 | - | - |
| 2.2487 | 76400 | 3.0125 | - | - |
| 2.2517 | 76500 | 3.0131 | 3.0109 | - |
| 2.2546 | 76600 | 3.0175 | - | - |
| 2.2575 | 76700 | 3.0063 | - | - |
| 2.2605 | 76800 | 3.0113 | - | - |
| 2.2634 | 76900 | 3.0106 | - | - |
| 2.2664 | 77000 | 3.0106 | 3.0109 | - |
| 2.2693 | 77100 | 3.0125 | - | - |
| 2.2723 | 77200 | 3.0163 | - | - |
| 2.2752 | 77300 | 3.0081 | - | - |
| 2.2781 | 77400 | 3.0131 | - | - |
| 2.2811 | 77500 | 3.0119 | 3.0107 | - |
| 2.2840 | 77600 | 3.015 | - | - |
| 2.2870 | 77700 | 3.0125 | - | - |
| 2.2899 | 77800 | 3.0094 | - | - |
| 2.2929 | 77900 | 3.01 | - | - |
| 2.2958 | 78000 | 3.0125 | 3.0107 | - |
| 2.2987 | 78100 | 3.0113 | - | - |
| 2.3017 | 78200 | 3.01 | - | - |
| 2.3046 | 78300 | 3.0119 | - | - |
| 2.3076 | 78400 | 3.0131 | - | - |
| 2.3105 | 78500 | 3.0106 | 3.0109 | - |
| 2.3135 | 78600 | 3.0063 | - | - |
| 2.3164 | 78700 | 3.0113 | - | - |
| 2.3194 | 78800 | 3.01 | - | - |
| 2.3223 | 78900 | 3.0131 | - | - |
| 2.3252 | 79000 | 3.0088 | 3.0118 | - |
| 2.3282 | 79100 | 3.0088 | - | - |
| 2.3311 | 79200 | 3.0106 | - | - |
| 2.3341 | 79300 | 3.0081 | - | - |
| 2.3370 | 79400 | 3.0144 | - | - |
| 2.3400 | 79500 | 3.0138 | 3.0107 | - |
| 2.3429 | 79600 | 3.01 | - | - |
| 2.3458 | 79700 | 3.01 | - | - |
| 2.3488 | 79800 | 3.0144 | - | - |
| 2.3517 | 79900 | 3.01 | - | - |
| 2.3547 | 80000 | 3.0125 | 3.0104 | - |
| 2.3576 | 80100 | 3.005 | - | - |
| 2.3606 | 80200 | 3.0106 | - | - |
| 2.3635 | 80300 | 3.0094 | - | - |
| 2.3664 | 80400 | 3.0131 | - | - |
| 2.3694 | 80500 | 3.0125 | 3.0104 | - |
| 2.3723 | 80600 | 3.0106 | - | - |
| 2.3753 | 80700 | 3.01 | - | - |
| 2.3782 | 80800 | 3.0119 | - | - |
| 2.3812 | 80900 | 3.0088 | - | - |
| 2.3841 | 81000 | 3.0113 | 3.0103 | - |
| 2.3870 | 81100 | 3.0094 | - | - |
| 2.3900 | 81200 | 3.0094 | - | - |
| 2.3929 | 81300 | 3.0119 | - | - |
| 2.3959 | 81400 | 3.0094 | - | - |
| 2.3988 | 81500 | 3.0088 | 3.0103 | - |
| 2.4018 | 81600 | 3.0106 | - | - |
| 2.4047 | 81700 | 3.0088 | - | - |
| 2.4077 | 81800 | 3.005 | - | - |
| 2.4106 | 81900 | 3.0113 | - | - |
| 2.4135 | 82000 | 3.0138 | 3.0103 | - |
| 2.4165 | 82100 | 3.0106 | - | - |
| 2.4194 | 82200 | 3.0094 | - | - |
| 2.4224 | 82300 | 3.0069 | - | - |
| 2.4253 | 82400 | 3.0106 | - | - |
| 2.4283 | 82500 | 3.0106 | 3.0104 | - |
| 2.4312 | 82600 | 3.0156 | - | - |
| 2.4341 | 82700 | 3.0138 | - | - |
| 2.4371 | 82800 | 3.0113 | - | - |
| 2.4400 | 82900 | 3.01 | - | - |
| 2.4430 | 83000 | 3.0138 | 3.0104 | - |
| 2.4459 | 83100 | 3.0194 | - | - |
| 2.4489 | 83200 | 3.0075 | - | - |
| 2.4518 | 83300 | 3.0088 | - | - |
| 2.4547 | 83400 | 3.0081 | - | - |
| 2.4577 | 83500 | 3.0138 | 3.0104 | - |
| 2.4606 | 83600 | 3.0081 | - | - |
| 2.4636 | 83700 | 3.0163 | - | - |
| 2.4665 | 83800 | 3.0113 | - | - |
| 2.4695 | 83900 | 3.0063 | - | - |
| 2.4724 | 84000 | 3.0144 | 3.0103 | - |
| 2.4753 | 84100 | 3.0088 | - | - |
| 2.4783 | 84200 | 3.0144 | - | - |
| 2.4812 | 84300 | 3.0131 | - | - |
| 2.4842 | 84400 | 3.0094 | - | - |
| 2.4871 | 84500 | 3.015 | 3.0103 | - |
| 2.4901 | 84600 | 3.0106 | - | - |
| 2.4930 | 84700 | 3.0119 | - | - |
| 2.4960 | 84800 | 3.0125 | - | - |
| 2.4989 | 84900 | 3.0125 | - | - |
| 2.5018 | 85000 | 3.015 | 3.0113 | - |
| 2.5048 | 85100 | 3.0156 | - | - |
| 2.5077 | 85200 | 3.0194 | - | - |
| 2.5107 | 85300 | 3.0119 | - | - |
| 2.5136 | 85400 | 3.0075 | - | - |
| 2.5166 | 85500 | 3.0156 | 3.0103 | - |
| 2.5195 | 85600 | 3.0131 | - | - |
| 2.5224 | 85700 | 3.0044 | - | - |
| 2.5254 | 85800 | 3.0075 | - | - |
| 2.5283 | 85900 | 3.0113 | - | - |
| 2.5313 | 86000 | 3.0144 | 3.0103 | - |
| 2.5342 | 86100 | 3.0144 | - | - |
| 2.5372 | 86200 | 3.0113 | - | - |
| 2.5401 | 86300 | 3.0163 | - | - |
| 2.5430 | 86400 | 3.0169 | - | - |
| 2.5460 | 86500 | 3.01 | 3.0101 | - |
| 2.5489 | 86600 | 3.01 | - | - |
| 2.5519 | 86700 | 3.0113 | - | - |
| 2.5548 | 86800 | 3.0138 | - | - |
| 2.5578 | 86900 | 3.0113 | - | - |
| 2.5607 | 87000 | 3.0113 | 3.0101 | - |
| 2.5636 | 87100 | 3.0081 | - | - |
| 2.5666 | 87200 | 3.0069 | - | - |
| 2.5695 | 87300 | 3.0069 | - | - |
| 2.5725 | 87400 | 3.0088 | - | - |
| 2.5754 | 87500 | 3.0094 | 3.0101 | - |
| 2.5784 | 87600 | 3.0088 | - | - |
| 2.5813 | 87700 | 3.0119 | - | - |
| 2.5843 | 87800 | 3.01 | - | - |
| 2.5872 | 87900 | 3.0119 | - | - |
| 2.5901 | 88000 | 3.0125 | 3.0101 | - |
| 2.5931 | 88100 | 3.0088 | - | - |
| 2.5960 | 88200 | 3.0138 | - | - |
| 2.5990 | 88300 | 3.01 | - | - |
| 2.6019 | 88400 | 3.0119 | - | - |
| 2.6049 | 88500 | 3.0119 | 3.0102 | - |
| 2.6078 | 88600 | 3.0063 | - | - |
| 2.6107 | 88700 | 3.01 | - | - |
| 2.6137 | 88800 | 3.0125 | - | - |
| 2.6166 | 88900 | 3.0175 | - | - |
| 2.6196 | 89000 | 3.0113 | 3.0118 | - |
| 2.6225 | 89100 | 3.02 | - | - |
| 2.6255 | 89200 | 3.0194 | - | - |
| 2.6284 | 89300 | 3.0088 | - | - |
| 2.6313 | 89400 | 3.0144 | - | - |
| 2.6343 | 89500 | 3.0125 | 3.0105 | - |
| 2.6372 | 89600 | 3.0144 | - | - |
| 2.6402 | 89700 | 3.0163 | - | - |
| 2.6431 | 89800 | 3.0106 | - | - |
| 2.6461 | 89900 | 3.0131 | - | - |
| 2.6490 | 90000 | 3.0119 | 3.0101 | - |
| 2.6519 | 90100 | 3.0175 | - | - |
| 2.6549 | 90200 | 3.0106 | - | - |
| 2.6578 | 90300 | 3.0138 | - | - |
| 2.6608 | 90400 | 3.0069 | - | - |
| 2.6637 | 90500 | 3.0138 | 3.0100 | - |
| 2.6667 | 90600 | 3.0044 | - | - |
| 2.6696 | 90700 | 3.0131 | - | - |
| 2.6726 | 90800 | 3.01 | - | - |
| 2.6755 | 90900 | 3.0094 | - | - |
| 2.6784 | 91000 | 3.0094 | 3.0100 | - |
| 2.6814 | 91100 | 3.0156 | - | - |
| 2.6843 | 91200 | 3.01 | - | - |
| 2.6873 | 91300 | 3.01 | - | - |
| 2.6902 | 91400 | 3.01 | - | - |
| 2.6932 | 91500 | 3.0075 | 3.0098 | - |
| 2.6961 | 91600 | 3.0125 | - | - |
| 2.6990 | 91700 | 3.01 | - | - |
| 2.7020 | 91800 | 3.0081 | - | - |
| 2.7049 | 91900 | 3.01 | - | - |
| 2.7079 | 92000 | 3.0169 | 3.0097 | - |
| 2.7108 | 92100 | 3.01 | - | - |
| 2.7138 | 92200 | 3.0125 | - | - |
| 2.7167 | 92300 | 3.0131 | - | - |
| 2.7196 | 92400 | 3.0138 | - | - |
| 2.7226 | 92500 | 3.0156 | 3.0099 | - |
| 2.7255 | 92600 | 3.0113 | - | - |
| 2.7285 | 92700 | 3.0106 | - | - |
| 2.7314 | 92800 | 3.0125 | - | - |
| 2.7344 | 92900 | 3.0038 | - | - |
| 2.7373 | 93000 | 3.0088 | 3.0100 | - |
| 2.7403 | 93100 | 3.0081 | - | - |
| 2.7432 | 93200 | 3.0119 | - | - |
| 2.7461 | 93300 | 3.0138 | - | - |
| 2.7491 | 93400 | 3.0131 | - | - |
| 2.7520 | 93500 | 3.0106 | 3.0100 | - |
| 2.7550 | 93600 | 3.0081 | - | - |
| 2.7579 | 93700 | 3.0056 | - | - |
| 2.7609 | 93800 | 3.0106 | - | - |
| 2.7638 | 93900 | 3.0119 | - | - |
| 2.7667 | 94000 | 3.0075 | 3.0099 | - |
| 2.7697 | 94100 | 3.0119 | - | - |
| 2.7726 | 94200 | 3.0075 | - | - |
| 2.7756 | 94300 | 3.0094 | - | - |
| 2.7785 | 94400 | 3.0119 | - | - |
| 2.7815 | 94500 | 3.01 | 3.0099 | - |
| 2.7844 | 94600 | 3.0106 | - | - |
| 2.7873 | 94700 | 3.0131 | - | - |
| 2.7903 | 94800 | 3.0094 | - | - |
| 2.7932 | 94900 | 3.0075 | - | - |
| 2.7962 | 95000 | 3.0119 | 3.0098 | - |
| 2.7991 | 95100 | 3.0094 | - | - |
| 2.8021 | 95200 | 3.0138 | - | - |
| 2.8050 | 95300 | 3.0094 | - | - |
| 2.8079 | 95400 | 3.0125 | - | - |
| 2.8109 | 95500 | 3.0081 | 3.0100 | - |
| 2.8138 | 95600 | 3.0081 | - | - |
| 2.8168 | 95700 | 3.0088 | - | - |
| 2.8197 | 95800 | 3.0113 | - | - |
| 2.8227 | 95900 | 3.0075 | - | - |
| 2.8256 | 96000 | 3.0138 | 3.0097 | - |
| 2.8286 | 96100 | 3.0106 | - | - |
| 2.8315 | 96200 | 3.01 | - | - |
| 2.8344 | 96300 | 3.0119 | - | - |
| 2.8374 | 96400 | 3.0144 | - | - |
| 2.8403 | 96500 | 3.0106 | 3.0099 | - |
| 2.8433 | 96600 | 3.0094 | - | - |
| 2.8462 | 96700 | 3.0131 | - | - |
| 2.8492 | 96800 | 3.0088 | - | - |
| 2.8521 | 96900 | 3.005 | - | - |
| 2.8550 | 97000 | 3.0156 | 3.0099 | - |
| 2.8580 | 97100 | 3.0094 | - | - |
| 2.8609 | 97200 | 3.0081 | - | - |
| 2.8639 | 97300 | 3.0113 | - | - |
| 2.8668 | 97400 | 3.0138 | - | - |
| 2.8698 | 97500 | 3.0119 | 3.0096 | - |
| 2.8727 | 97600 | 3.0125 | - | - |
| 2.8756 | 97700 | 3.0094 | - | - |
| 2.8786 | 97800 | 3.0119 | - | - |
| 2.8815 | 97900 | 3.0081 | - | - |
| 2.8845 | 98000 | 3.0106 | 3.0096 | - |
| 2.8874 | 98100 | 3.0081 | - | - |
| 2.8904 | 98200 | 3.0125 | - | - |
| 2.8933 | 98300 | 3.0075 | - | - |
| 2.8962 | 98400 | 3.0119 | - | - |
| 2.8992 | 98500 | 3.0106 | 3.0096 | - |
| 2.9021 | 98600 | 3.0081 | - | - |
| 2.9051 | 98700 | 3.0094 | - | - |
| 2.9080 | 98800 | 3.0081 | - | - |
| 2.9110 | 98900 | 3.0144 | - | - |
| 2.9139 | 99000 | 3.0094 | 3.0091 | - |
| 2.9169 | 99100 | 3.0094 | - | - |
| 2.9198 | 99200 | 3.0094 | - | - |
| 2.9227 | 99300 | 3.0106 | - | - |
| 2.9257 | 99400 | 3.01 | - | - |
| 2.9286 | 99500 | 3.0113 | 3.0091 | - |
| 2.9316 | 99600 | 3.0106 | - | - |
| 2.9345 | 99700 | 3.0106 | - | - |
| 2.9375 | 99800 | 3.0094 | - | - |
| 2.9404 | 99900 | 3.0081 | - | - |
| 2.9433 | 100000 | 3.01 | 3.0091 | - |
| 2.9463 | 100100 | 3.0119 | - | - |
| 2.9492 | 100200 | 3.0106 | - | - |
| 2.9522 | 100300 | 3.0113 | - | - |
| 2.9551 | 100400 | 3.0075 | - | - |
| 2.9581 | 100500 | 3.0094 | 3.0098 | - |
| 2.9610 | 100600 | 3.0119 | - | - |
| 2.9639 | 100700 | 3.0106 | - | - |
| 2.9669 | 100800 | 3.0088 | - | - |
| 2.9698 | 100900 | 3.015 | - | - |
| 2.9728 | 101000 | 3.0106 | 3.0096 | - |
| 2.9757 | 101100 | 3.0075 | - | - |
| 2.9787 | 101200 | 3.0188 | - | - |
| 2.9816 | 101300 | 3.0088 | - | - |
| 2.9845 | 101400 | 3.0081 | - | - |
| 2.9875 | 101500 | 3.0075 | 3.0097 | - |
| 2.9904 | 101600 | 3.0119 | - | - |
| 2.9934 | 101700 | 3.01 | - | - |
| 2.9963 | 101800 | 3.0075 | - | - |
| 2.9993 | 101900 | 3.0094 | - | - |
| 3.0022 | 102000 | 3.0119 | 3.0097 | - |
| 3.0052 | 102100 | 3.0113 | - | - |
| 3.0081 | 102200 | 3.0088 | - | - |
| 3.0110 | 102300 | 3.0106 | - | - |
| 3.0140 | 102400 | 3.0113 | - | - |
| 3.0169 | 102500 | 3.015 | 3.0097 | - |
| 3.0199 | 102600 | 3.0088 | - | - |
| 3.0228 | 102700 | 3.0088 | - | - |
| 3.0258 | 102800 | 3.0106 | - | - |
| 3.0287 | 102900 | 3.0113 | - | - |
| 3.0316 | 103000 | 3.01 | 3.0094 | - |
| 3.0346 | 103100 | 3.0113 | - | - |
| 3.0375 | 103200 | 3.0125 | - | - |
| 3.0405 | 103300 | 3.0056 | - | - |
| 3.0434 | 103400 | 3.01 | - | - |
| 3.0464 | 103500 | 3.01 | 3.0094 | - |
| 3.0493 | 103600 | 3.01 | - | - |
| 3.0522 | 103700 | 3.01 | - | - |
| 3.0552 | 103800 | 3.0075 | - | - |
| 3.0581 | 103900 | 3.0063 | - | - |
| 3.0611 | 104000 | 3.015 | 3.0096 | - |
| 3.0640 | 104100 | 3.0063 | - | - |
| 3.0670 | 104200 | 3.0119 | - | - |
| 3.0699 | 104300 | 3.0088 | - | - |
| 3.0728 | 104400 | 3.0113 | - | - |
| 3.0758 | 104500 | 3.01 | 3.0095 | - |
| 3.0787 | 104600 | 3.0081 | - | - |
| 3.0817 | 104700 | 3.0094 | - | - |
| 3.0846 | 104800 | 3.0075 | - | - |
| 3.0876 | 104900 | 3.0113 | - | - |
| 3.0905 | 105000 | 3.0131 | 3.0095 | - |
| 3.0935 | 105100 | 3.0131 | - | - |
| 3.0964 | 105200 | 3.0131 | - | - |
| 3.0993 | 105300 | 3.0075 | - | - |
| 3.1023 | 105400 | 3.0119 | - | - |
| 3.1052 | 105500 | 3.0094 | 3.0092 | - |
| 3.1082 | 105600 | 3.0069 | - | - |
| 3.1111 | 105700 | 3.0063 | - | - |
| 3.1141 | 105800 | 3.0094 | - | - |
| 3.1170 | 105900 | 3.01 | - | - |
| 3.1199 | 106000 | 3.0113 | 3.0097 | - |
| 3.1229 | 106100 | 3.0056 | - | - |
| 3.1258 | 106200 | 3.01 | - | - |
| 3.1288 | 106300 | 3.0081 | - | - |
| 3.1317 | 106400 | 3.0106 | - | - |
| 3.1347 | 106500 | 3.01 | 3.0096 | - |
| 3.1376 | 106600 | 3.0069 | - | - |
| 3.1405 | 106700 | 3.0119 | - | - |
| 3.1435 | 106800 | 3.0081 | - | - |
| 3.1464 | 106900 | 3.0075 | - | - |
| 3.1494 | 107000 | 3.0081 | 3.0097 | - |
| 3.1523 | 107100 | 3.0075 | - | - |
| 3.1553 | 107200 | 3.0081 | - | - |
| 3.1582 | 107300 | 3.0125 | - | - |
| 3.1611 | 107400 | 3.0094 | - | - |
| 3.1641 | 107500 | 3.0094 | 3.0092 | - |
| 3.1670 | 107600 | 3.0175 | - | - |
| 3.1700 | 107700 | 3.01 | - | - |
| 3.1729 | 107800 | 3.0113 | - | - |
| 3.1759 | 107900 | 3.0094 | - | - |
| 3.1788 | 108000 | 3.0125 | 3.0091 | - |
| 3.1818 | 108100 | 3.0069 | - | - |
| 3.1847 | 108200 | 3.0119 | - | - |
| 3.1876 | 108300 | 3.0144 | - | - |
| 3.1906 | 108400 | 3.0075 | - | - |
| 3.1935 | 108500 | 3.0094 | 3.0097 | - |
| 3.1965 | 108600 | 3.0106 | - | - |
| 3.1994 | 108700 | 3.0144 | - | - |
| 3.2024 | 108800 | 3.0075 | - | - |
| 3.2053 | 108900 | 3.0156 | - | - |
| 3.2082 | 109000 | 3.0044 | 3.0095 | - |
| 3.2112 | 109100 | 3.01 | - | - |
| 3.2141 | 109200 | 3.0106 | - | - |
| 3.2171 | 109300 | 3.0081 | - | - |
| 3.2200 | 109400 | 3.0069 | - | - |
| 3.2230 | 109500 | 3.01 | 3.0096 | - |
| 3.2259 | 109600 | 3.01 | - | - |
| 3.2288 | 109700 | 3.0125 | - | - |
| 3.2318 | 109800 | 3.0069 | - | - |
| 3.2347 | 109900 | 3.0081 | - | - |
| 3.2377 | 110000 | 3.0088 | 3.0097 | - |
| 3.2406 | 110100 | 3.0119 | - | - |
| 3.2436 | 110200 | 3.0131 | - | - |
| 3.2465 | 110300 | 3.0119 | - | - |
| 3.2494 | 110400 | 3.0094 | - | - |
| 3.2524 | 110500 | 3.0094 | 3.0096 | - |
| 3.2553 | 110600 | 3.0144 | - | - |
| 3.2583 | 110700 | 3.0069 | - | - |
| 3.2612 | 110800 | 3.0131 | - | - |
| 3.2642 | 110900 | 3.0081 | - | - |
| 3.2671 | 111000 | 3.01 | 3.0096 | - |
| 3.2701 | 111100 | 3.01 | - | - |
| 3.2730 | 111200 | 3.01 | - | - |
| 3.2759 | 111300 | 3.0125 | - | - |
| 3.2789 | 111400 | 3.0113 | - | - |
| 3.2818 | 111500 | 3.0088 | 3.0095 | - |
| 3.2848 | 111600 | 3.0131 | - | - |
| 3.2877 | 111700 | 3.0125 | - | - |
| 3.2907 | 111800 | 3.01 | - | - |
| 3.2936 | 111900 | 3.0113 | - | - |
| 3.2965 | 112000 | 3.0044 | 3.0095 | - |
| 3.2995 | 112100 | 3.0144 | - | - |
| 3.3024 | 112200 | 3.0081 | - | - |
| 3.3054 | 112300 | 3.0106 | - | - |
| 3.3083 | 112400 | 3.0094 | - | - |
| 3.3113 | 112500 | 3.005 | 3.0095 | - |
| 3.3142 | 112600 | 3.0131 | - | - |
| 3.3171 | 112700 | 3.0081 | - | - |
| 3.3201 | 112800 | 3.0094 | - | - |
| 3.3230 | 112900 | 3.0075 | - | - |
| 3.3260 | 113000 | 3.0113 | 3.0095 | - |
| 3.3289 | 113100 | 3.0081 | - | - |
| 3.3319 | 113200 | 3.0094 | - | - |
| 3.3348 | 113300 | 3.0081 | - | - |
| 3.3377 | 113400 | 3.0106 | - | - |
| 3.3407 | 113500 | 3.0169 | 3.0095 | - |
| 3.3436 | 113600 | 3.0056 | - | - |
| 3.3466 | 113700 | 3.0081 | - | - |
| 3.3495 | 113800 | 3.0069 | - | - |
| 3.3525 | 113900 | 3.0094 | - | - |
| 3.3554 | 114000 | 3.0031 | 3.0095 | - |
| 3.3584 | 114100 | 3.0069 | - | - |
| 3.3613 | 114200 | 3.0075 | - | - |
| 3.3642 | 114300 | 3.015 | - | - |
| 3.3672 | 114400 | 3.0081 | - | - |
| 3.3701 | 114500 | 3.0094 | 3.0095 | - |
| 3.3731 | 114600 | 3.0056 | - | - |
| 3.3760 | 114700 | 3.0081 | - | - |
| 3.3790 | 114800 | 3.0119 | - | - |
| 3.3819 | 114900 | 3.0075 | - | - |
| 3.3848 | 115000 | 3.0063 | 3.0098 | - |
| 3.3878 | 115100 | 3.0144 | - | - |
| 3.3907 | 115200 | 3.0138 | - | - |
| 3.3937 | 115300 | 3.0081 | - | - |
| 3.3966 | 115400 | 3.0113 | - | - |
| 3.3996 | 115500 | 3.0138 | 3.0098 | - |
| 3.4025 | 115600 | 3.0081 | - | - |
| 3.4054 | 115700 | 3.0106 | - | - |
| 3.4084 | 115800 | 3.0088 | - | - |
| 3.4113 | 115900 | 3.0106 | - | - |
| 3.4143 | 116000 | 3.0156 | 3.0095 | - |
| 3.4172 | 116100 | 3.0119 | - | - |
| 3.4202 | 116200 | 3.01 | - | - |
| 3.4231 | 116300 | 3.0144 | - | - |
| 3.4260 | 116400 | 3.0131 | - | - |
| 3.4290 | 116500 | 3.0131 | 3.0097 | - |
| 3.4319 | 116600 | 3.0088 | - | - |
| 3.4349 | 116700 | 3.0113 | - | - |
| 3.4378 | 116800 | 3.0044 | - | - |
| 3.4408 | 116900 | 3.01 | - | - |
| 3.4437 | 117000 | 3.0069 | 3.0094 | - |
| 3.4467 | 117100 | 3.0081 | - | - |
| 3.4496 | 117200 | 3.0125 | - | - |
| 3.4525 | 117300 | 3.0069 | - | - |
| 3.4555 | 117400 | 3.0063 | - | - |
| 3.4584 | 117500 | 3.0044 | 3.0095 | - |
| 3.4614 | 117600 | 3.0119 | - | - |
| 3.4643 | 117700 | 3.0081 | - | - |
| 3.4673 | 117800 | 3.0081 | - | - |
| 3.4702 | 117900 | 3.0106 | - | - |
| 3.4731 | 118000 | 3.0125 | 3.0095 | - |
| 3.4761 | 118100 | 3.0138 | - | - |
| 3.4790 | 118200 | 3.0106 | - | - |
| 3.4820 | 118300 | 3.0144 | - | - |
| 3.4849 | 118400 | 3.0081 | - | - |
| 3.4879 | 118500 | 3.01 | 3.0095 | - |
| 3.4908 | 118600 | 3.0075 | - | - |
| 3.4937 | 118700 | 3.0056 | - | - |
| 3.4967 | 118800 | 3.0069 | - | - |
| 3.4996 | 118900 | 3.0094 | - | - |
| 3.5026 | 119000 | 3.0119 | 3.0095 | - |
| 3.5055 | 119100 | 3.0038 | - | - |
| 3.5085 | 119200 | 3.025 | - | - |
| 3.5114 | 119300 | 3.0081 | - | - |
| 3.5143 | 119400 | 3.0119 | - | - |
| 3.5173 | 119500 | 3.005 | 3.0095 | - |
| 3.5202 | 119600 | 3.01 | - | - |
| 3.5232 | 119700 | 3.0025 | - | - |
| 3.5261 | 119800 | 3.0088 | - | - |
| 3.5291 | 119900 | 3.0106 | - | - |
| 3.5320 | 120000 | 3.0138 | 3.0095 | - |
| 3.5350 | 120100 | 3.0056 | - | - |
| 3.5379 | 120200 | 3.0088 | - | - |
| 3.5408 | 120300 | 3.0125 | - | - |
| 3.5438 | 120400 | 3.0125 | - | - |
| 3.5467 | 120500 | 3.0056 | 3.0095 | - |
| 3.5497 | 120600 | 3.0131 | - | - |
| 3.5526 | 120700 | 3.0119 | - | - |
| 3.5556 | 120800 | 3.0094 | - | - |
| 3.5585 | 120900 | 3.0106 | - | - |
| 3.5614 | 121000 | 3.0113 | 3.0095 | - |
| 3.5644 | 121100 | 3.0106 | - | - |
| 3.5673 | 121200 | 3.0156 | - | - |
| 3.5703 | 121300 | 3.0069 | - | - |
| 3.5732 | 121400 | 3.0125 | - | - |
| 3.5762 | 121500 | 3.0069 | 3.0095 | - |
| 3.5791 | 121600 | 3.01 | - | - |
| 3.5820 | 121700 | 3.0119 | - | - |
| 3.5850 | 121800 | 3.0088 | - | - |
| 3.5879 | 121900 | 3.0119 | - | - |
| 3.5909 | 122000 | 3.0069 | 3.0095 | - |
| 3.5938 | 122100 | 3.0069 | - | - |
| 3.5968 | 122200 | 3.0138 | - | - |
| 3.5997 | 122300 | 3.01 | - | - |
| 3.6026 | 122400 | 3.0106 | - | - |
| 3.6056 | 122500 | 3.0113 | 3.0095 | - |
| 3.6085 | 122600 | 3.01 | - | - |
| 3.6115 | 122700 | 3.005 | - | - |
| 3.6144 | 122800 | 3.0069 | - | - |
| 3.6174 | 122900 | 3.0094 | - | - |
| 3.6203 | 123000 | 3.0119 | 3.0095 | - |
| 3.6233 | 123100 | 3.0056 | - | - |
| 3.6262 | 123200 | 3.0075 | - | - |
| 3.6291 | 123300 | 3.0106 | - | - |
| 3.6321 | 123400 | 3.005 | - | - |
| 3.6350 | 123500 | 3.0081 | 3.0095 | - |
| 3.6380 | 123600 | 3.02 | - | - |
| 3.6409 | 123700 | 3.0094 | - | - |
| 3.6439 | 123800 | 3.0119 | - | - |
| 3.6468 | 123900 | 3.0106 | - | - |
| 3.6497 | 124000 | 3.0125 | 3.0095 | - |
| 3.6527 | 124100 | 3.0125 | - | - |
| 3.6556 | 124200 | 3.0188 | - | - |
| 3.6586 | 124300 | 3.01 | - | - |
| 3.6615 | 124400 | 3.0088 | - | - |
| 3.6645 | 124500 | 3.0169 | 3.0095 | - |
| 3.6674 | 124600 | 3.0113 | - | - |
| 3.6703 | 124700 | 3.0063 | - | - |
| 3.6733 | 124800 | 3.0094 | - | - |
| 3.6762 | 124900 | 3.0038 | - | - |
| 3.6792 | 125000 | 3.0106 | 3.0091 | - |
| 3.6821 | 125100 | 3.005 | - | - |
| 3.6851 | 125200 | 3.0081 | - | - |
| 3.6880 | 125300 | 3.0075 | - | - |
| 3.6909 | 125400 | 3.0131 | - | - |
| 3.6939 | 125500 | 3.0075 | 3.0091 | - |
| 3.6968 | 125600 | 3.0131 | - | - |
| 3.6998 | 125700 | 3.01 | - | - |
| 3.7027 | 125800 | 3.0075 | - | - |
| 3.7057 | 125900 | 3.0113 | - | - |
| 3.7086 | 126000 | 3.0094 | 3.0091 | - |
| 3.7116 | 126100 | 3.0081 | - | - |
| 3.7145 | 126200 | 3.0119 | - | - |
| 3.7174 | 126300 | 3.0088 | - | - |
| 3.7204 | 126400 | 3.0063 | - | - |
| 3.7233 | 126500 | 3.0081 | 3.0091 | - |
| 3.7263 | 126600 | 3.0125 | - | - |
| 3.7292 | 126700 | 3.0125 | - | - |
| 3.7322 | 126800 | 3.0131 | - | - |
| 3.7351 | 126900 | 3.0106 | - | - |
| 3.7380 | 127000 | 3.0088 | 3.0091 | - |
| 3.7410 | 127100 | 3.0113 | - | - |
| 3.7439 | 127200 | 3.0125 | - | - |
| 3.7469 | 127300 | 3.0094 | - | - |
| 3.7498 | 127400 | 3.0069 | - | - |
| 3.7528 | 127500 | 3.0088 | 3.0091 | - |
| 3.7557 | 127600 | 3.0163 | - | - |
| 3.7586 | 127700 | 3.0094 | - | - |
| 3.7616 | 127800 | 3.0069 | - | - |
| 3.7645 | 127900 | 3.0063 | - | - |
| 3.7675 | 128000 | 3.0094 | 3.0091 | - |
| 3.7704 | 128100 | 3.01 | - | - |
| 3.7734 | 128200 | 3.015 | - | - |
| 3.7763 | 128300 | 3.0163 | - | - |
| 3.7792 | 128400 | 3.0106 | - | - |
| 3.7822 | 128500 | 3.0113 | 3.0091 | - |
| 3.7851 | 128600 | 3.0069 | - | - |
| 3.7881 | 128700 | 3.0113 | - | - |
| 3.7910 | 128800 | 3.0063 | - | - |
| 3.7940 | 128900 | 3.0088 | - | - |
| 3.7969 | 129000 | 3.0019 | 3.0091 | - |
| 3.7999 | 129100 | 3.0094 | - | - |
| 3.8028 | 129200 | 3.0038 | - | - |
| 3.8057 | 129300 | 3.0044 | - | - |
| 3.8087 | 129400 | 3.0088 | - | - |
| 3.8116 | 129500 | 3.0113 | 3.0091 | - |
| 3.8146 | 129600 | 3.0094 | - | - |
| 3.8175 | 129700 | 3.0088 | - | - |
| 3.8205 | 129800 | 3.0113 | - | - |
| 3.8234 | 129900 | 3.0094 | - | - |
| 3.8263 | 130000 | 3.0069 | 3.0091 | - |
| 3.8293 | 130100 | 3.0113 | - | - |
| 3.8322 | 130200 | 3.0081 | - | - |
| 3.8352 | 130300 | 3.0125 | - | - |
| 3.8381 | 130400 | 3.0156 | - | - |
| 3.8411 | 130500 | 3.0069 | 3.0091 | - |
| 3.8440 | 130600 | 3.0131 | - | - |
| 3.8469 | 130700 | 3.0131 | - | - |
| 3.8499 | 130800 | 3.005 | - | - |
| 3.8528 | 130900 | 3.0106 | - | - |
| 3.8558 | 131000 | 3.0119 | 3.0089 | - |
| 3.8587 | 131100 | 3.0081 | - | - |
| 3.8617 | 131200 | 3.0088 | - | - |
| 3.8646 | 131300 | 3.0075 | - | - |
| 3.8675 | 131400 | 3.0056 | - | - |
</details>
### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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