sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:8004920 loss:CoSENTLoss en arxiv:1908.10084 base_model:sentence-transformers/all-MiniLM-L6-v2 base_model:finetune:sentence-transformers/all-MiniLM-L6-v2 license:apache-2.0 autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: sentence-transformers/all-MiniLM-L6-v2
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8004920
- loss:CoSENTLoss
widget:
- source_sentence: ulker marie biscuits
  sentences:
  - mozzarella cracker
  - four pockets slacks
  - luminous sun cream primer
- source_sentence: Owl Diaries 4 Eva and the New book
  sentences:
  - gibna Tortilla
  - Advanced swimmer tankini
  - flat waistband swim stretch pants
- source_sentence: compartment for footwear racket luggage
  sentences:
  - hair ties
  - designer shoulder satchel
  - Dishwasher safe Platter
- source_sentence: pearch purse
  sentences:
  - riding boots
  - Creme lipstick
  - dior tote
- source_sentence: momax q led desk lamp charging base
  sentences:
  - JUTE tote
  - walking trousers
  - sephora selftanning body mist
---

# all-MiniLM-L6-v3-pair_score

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 fa97f6e7cb1a59073dff9e6b13e2715cf7475ac9 -->
- **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 = [
    'momax q led desk lamp charging base',
    'JUTE tote',
    'sephora selftanning body mist',
]
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.*
-->

<!--
## 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`: 64
- `per_device_eval_batch_size`: 64
- `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`: 64
- `per_device_eval_batch_size`: 64
- `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   |
|:------:|:------:|:-------------:|:------:|
| 0.0040 | 500    | 11.0937       | -      |
| 0.0080 | 1000   | 10.4099       | -      |
| 0.0120 | 1500   | 9.5203        | -      |
| 0.0160 | 2000   | 8.5096        | -      |
| 0.0200 | 2500   | 7.7136        | -      |
| 0.0240 | 3000   | 7.0622        | -      |
| 0.0280 | 3500   | 6.656         | -      |
| 0.0320 | 4000   | 6.4973        | -      |
| 0.0360 | 4500   | 6.4192        | -      |
| 0.0400 | 5000   | 6.3691        | -      |
| 0.0440 | 5500   | 6.3185        | -      |
| 0.0480 | 6000   | 6.3008        | -      |
| 0.0520 | 6500   | 6.2712        | -      |
| 0.0560 | 7000   | 6.1812        | -      |
| 0.0600 | 7500   | 6.1804        | -      |
| 0.0640 | 8000   | 6.2158        | -      |
| 0.0680 | 8500   | 6.1458        | -      |
| 0.0720 | 9000   | 6.1272        | -      |
| 0.0760 | 9500   | 6.1191        | -      |
| 0.0800 | 10000  | 6.1326        | -      |
| 0.0839 | 10500  | 6.1149        | -      |
| 0.0879 | 11000  | 6.113         | -      |
| 0.0919 | 11500  | 6.0837        | -      |
| 0.0959 | 12000  | 6.0694        | -      |
| 0.0999 | 12500  | 6.0823        | -      |
| 0.1039 | 13000  | 6.0237        | -      |
| 0.1079 | 13500  | 6.0599        | -      |
| 0.1119 | 14000  | 6.0395        | -      |
| 0.1159 | 14500  | 6.0278        | -      |
| 0.1199 | 15000  | 6.0407        | -      |
| 0.1239 | 15500  | 5.9927        | -      |
| 0.1279 | 16000  | 6.007         | -      |
| 0.1319 | 16500  | 5.9956        | -      |
| 0.1359 | 17000  | 5.9771        | -      |
| 0.1399 | 17500  | 5.9549        | -      |
| 0.1439 | 18000  | 6.0142        | -      |
| 0.1479 | 18500  | 5.9834        | -      |
| 0.1519 | 19000  | 5.9187        | -      |
| 0.1559 | 19500  | 5.9599        | -      |
| 0.1599 | 20000  | 5.9338        | -      |
| 0.1639 | 20500  | 5.9025        | -      |
| 0.1679 | 21000  | 5.9289        | -      |
| 0.1719 | 21500  | 5.9003        | -      |
| 0.1759 | 22000  | 5.9284        | -      |
| 0.1799 | 22500  | 5.9084        | -      |
| 0.1839 | 23000  | 5.9171        | -      |
| 0.1879 | 23500  | 5.9341        | -      |
| 0.1919 | 24000  | 5.9336        | -      |
| 0.1959 | 24500  | 5.8839        | -      |
| 0.1999 | 25000  | 5.9089        | -      |
| 0.2039 | 25500  | 5.8855        | -      |
| 0.2079 | 26000  | 5.8739        | -      |
| 0.2119 | 26500  | 5.8558        | -      |
| 0.2159 | 27000  | 5.8715        | -      |
| 0.2199 | 27500  | 5.8257        | -      |
| 0.2239 | 28000  | 5.8951        | -      |
| 0.2279 | 28500  | 5.8489        | -      |
| 0.2319 | 29000  | 5.853         | -      |
| 0.2359 | 29500  | 5.865         | -      |
| 0.2399 | 30000  | 5.8399        | -      |
| 0.2438 | 30500  | 5.8356        | -      |
| 0.2478 | 31000  | 5.828         | -      |
| 0.2518 | 31500  | 5.8467        | -      |
| 0.2558 | 32000  | 5.856         | -      |
| 0.2598 | 32500  | 5.8059        | -      |
| 0.2638 | 33000  | 5.8476        | -      |
| 0.2678 | 33500  | 5.7928        | -      |
| 0.2718 | 34000  | 5.8001        | -      |
| 0.2758 | 34500  | 5.8136        | -      |
| 0.2798 | 35000  | 5.8163        | -      |
| 0.2838 | 35500  | 5.7882        | -      |
| 0.2878 | 36000  | 5.7785        | -      |
| 0.2918 | 36500  | 5.7778        | -      |
| 0.2958 | 37000  | 5.7418        | -      |
| 0.2998 | 37500  | 5.8014        | -      |
| 0.3038 | 38000  | 5.8088        | -      |
| 0.3078 | 38500  | 5.7458        | -      |
| 0.3118 | 39000  | 5.7981        | -      |
| 0.3158 | 39500  | 5.7693        | -      |
| 0.3198 | 40000  | 5.7612        | -      |
| 0.3238 | 40500  | 5.7782        | -      |
| 0.3278 | 41000  | 5.7576        | -      |
| 0.3318 | 41500  | 5.7524        | -      |
| 0.3358 | 42000  | 5.73          | -      |
| 0.3398 | 42500  | 5.7805        | -      |
| 0.3438 | 43000  | 5.7296        | -      |
| 0.3478 | 43500  | 5.7579        | -      |
| 0.3518 | 44000  | 5.7592        | -      |
| 0.3558 | 44500  | 5.7467        | -      |
| 0.3598 | 45000  | 5.7158        | -      |
| 0.3638 | 45500  | 5.731         | -      |
| 0.3678 | 46000  | 5.7482        | -      |
| 0.3718 | 46500  | 5.7176        | -      |
| 0.3758 | 47000  | 5.7273        | -      |
| 0.3798 | 47500  | 5.6963        | -      |
| 0.3838 | 48000  | 5.6946        | -      |
| 0.3878 | 48500  | 5.7576        | -      |
| 0.3918 | 49000  | 5.6921        | -      |
| 0.3958 | 49500  | 5.6965        | -      |
| 0.3998 | 50000  | 5.7335        | -      |
| 0.4038 | 50500  | 5.7064        | -      |
| 0.4077 | 51000  | 5.6945        | -      |
| 0.4117 | 51500  | 5.7319        | -      |
| 0.4157 | 52000  | 5.735         | -      |
| 0.4197 | 52500  | 5.6942        | -      |
| 0.4237 | 53000  | 5.7037        | -      |
| 0.4277 | 53500  | 5.6724        | -      |
| 0.4317 | 54000  | 5.6971        | -      |
| 0.4357 | 54500  | 5.7163        | -      |
| 0.4397 | 55000  | 5.6842        | -      |
| 0.4437 | 55500  | 5.6924        | -      |
| 0.4477 | 56000  | 5.6814        | -      |
| 0.4517 | 56500  | 5.671         | -      |
| 0.4557 | 57000  | 5.6563        | -      |
| 0.4597 | 57500  | 5.6385        | -      |
| 0.4637 | 58000  | 5.6595        | -      |
| 0.4677 | 58500  | 5.6744        | -      |
| 0.4717 | 59000  | 5.6285        | -      |
| 0.4757 | 59500  | 5.6202        | -      |
| 0.4797 | 60000  | 5.6484        | -      |
| 0.4837 | 60500  | 5.647         | -      |
| 0.4877 | 61000  | 5.6641        | -      |
| 0.4917 | 61500  | 5.6681        | -      |
| 0.4957 | 62000  | 5.6344        | -      |
| 0.4997 | 62500  | 5.6253        | -      |
| 0.5037 | 63000  | 5.6258        | -      |
| 0.5077 | 63500  | 5.6525        | -      |
| 0.5117 | 64000  | 5.5764        | -      |
| 0.5157 | 64500  | 5.6265        | -      |
| 0.5197 | 65000  | 5.6201        | -      |
| 0.5237 | 65500  | 5.6297        | -      |
| 0.5277 | 66000  | 5.6133        | -      |
| 0.5317 | 66500  | 5.5981        | -      |
| 0.5357 | 67000  | 5.6085        | -      |
| 0.5397 | 67500  | 5.6128        | -      |
| 0.5437 | 68000  | 5.6237        | -      |
| 0.5477 | 68500  | 5.6005        | -      |
| 0.5517 | 69000  | 5.6156        | -      |
| 0.5557 | 69500  | 5.5723        | -      |
| 0.5597 | 70000  | 5.5817        | -      |
| 0.5637 | 70500  | 5.6186        | -      |
| 0.5677 | 71000  | 5.588         | -      |
| 0.5716 | 71500  | 5.5219        | -      |
| 0.5756 | 72000  | 5.5718        | -      |
| 0.5796 | 72500  | 5.5878        | -      |
| 0.5836 | 73000  | 5.5702        | -      |
| 0.5876 | 73500  | 5.5274        | -      |
| 0.5916 | 74000  | 5.5608        | -      |
| 0.5956 | 74500  | 5.5328        | -      |
| 0.5996 | 75000  | 5.5848        | -      |
| 0.6036 | 75500  | 5.5713        | -      |
| 0.6076 | 76000  | 5.5529        | -      |
| 0.6116 | 76500  | 5.5224        | -      |
| 0.6156 | 77000  | 5.57          | -      |
| 0.6196 | 77500  | 5.5874        | -      |
| 0.6236 | 78000  | 5.5441        | -      |
| 0.6276 | 78500  | 5.5127        | -      |
| 0.6316 | 79000  | 5.5557        | -      |
| 0.6356 | 79500  | 5.5101        | -      |
| 0.6396 | 80000  | 5.5153        | -      |
| 0.6436 | 80500  | 5.4958        | -      |
| 0.6476 | 81000  | 5.518         | -      |
| 0.6516 | 81500  | 5.5022        | -      |
| 0.6556 | 82000  | 5.5013        | -      |
| 0.6596 | 82500  | 5.4832        | -      |
| 0.6636 | 83000  | 5.5174        | -      |
| 0.6676 | 83500  | 5.5052        | -      |
| 0.6716 | 84000  | 5.5315        | -      |
| 0.6756 | 84500  | 5.533         | -      |
| 0.6796 | 85000  | 5.4937        | -      |
| 0.6836 | 85500  | 5.4697        | -      |
| 0.6876 | 86000  | 5.5085        | -      |
| 0.6916 | 86500  | 5.4901        | -      |
| 0.6956 | 87000  | 5.4667        | -      |
| 0.6996 | 87500  | 5.5047        | -      |
| 0.7036 | 88000  | 5.495         | -      |
| 0.7076 | 88500  | 5.4677        | -      |
| 0.7116 | 89000  | 5.4779        | -      |
| 0.7156 | 89500  | 5.4467        | -      |
| 0.7196 | 90000  | 5.4772        | -      |
| 0.7236 | 90500  | 5.4988        | -      |
| 0.7276 | 91000  | 5.4832        | -      |
| 0.7315 | 91500  | 5.4669        | -      |
| 0.7355 | 92000  | 5.447         | -      |
| 0.7395 | 92500  | 5.4725        | -      |
| 0.7435 | 93000  | 5.458         | -      |
| 0.7475 | 93500  | 5.4872        | -      |
| 0.7515 | 94000  | 5.4491        | -      |
| 0.7555 | 94500  | 5.4729        | -      |
| 0.7595 | 95000  | 5.4506        | -      |
| 0.7635 | 95500  | 5.4585        | -      |
| 0.7675 | 96000  | 5.4173        | -      |
| 0.7715 | 96500  | 5.4371        | -      |
| 0.7755 | 97000  | 5.4433        | -      |
| 0.7795 | 97500  | 5.4664        | -      |
| 0.7835 | 98000  | 5.4302        | -      |
| 0.7875 | 98500  | 5.4389        | -      |
| 0.7915 | 99000  | 5.4451        | -      |
| 0.7955 | 99500  | 5.4432        | -      |
| 0.7995 | 100000 | 5.4322        | -      |
| 0.8035 | 100500 | 5.4166        | -      |
| 0.8075 | 101000 | 5.4405        | -      |
| 0.8115 | 101500 | 5.4114        | -      |
| 0.8155 | 102000 | 5.4646        | -      |
| 0.8195 | 102500 | 5.442         | -      |
| 0.8235 | 103000 | 5.4145        | -      |
| 0.8275 | 103500 | 5.432         | -      |
| 0.8315 | 104000 | 5.4458        | -      |
| 0.8355 | 104500 | 5.4044        | -      |
| 0.8395 | 105000 | 5.4376        | -      |
| 0.8435 | 105500 | 5.432         | -      |
| 0.8475 | 106000 | 5.4196        | -      |
| 0.8515 | 106500 | 5.4193        | -      |
| 0.8555 | 107000 | 5.4272        | -      |
| 0.8595 | 107500 | 5.4235        | -      |
| 0.8635 | 108000 | 5.4332        | -      |
| 0.8675 | 108500 | 5.4434        | -      |
| 0.8715 | 109000 | 5.3986        | -      |
| 0.8755 | 109500 | 5.3906        | -      |
| 0.8795 | 110000 | 5.3775        | -      |
| 0.8835 | 110500 | 5.3805        | -      |
| 0.8875 | 111000 | 5.404         | -      |
| 0.8915 | 111500 | 5.3914        | -      |
| 0.8954 | 112000 | 5.4238        | -      |
| 0.8994 | 112500 | 5.4133        | -      |
| 0.9034 | 113000 | 5.3882        | -      |
| 0.9074 | 113500 | 5.4108        | -      |
| 0.9114 | 114000 | 5.4203        | -      |
| 0.9154 | 114500 | 5.3607        | -      |
| 0.9194 | 115000 | 5.3691        | -      |
| 0.9234 | 115500 | 5.3354        | -      |
| 0.9274 | 116000 | 5.3859        | -      |
| 0.9314 | 116500 | 5.3877        | -      |
| 0.9354 | 117000 | 5.3874        | -      |
| 0.9394 | 117500 | 5.3595        | -      |
| 0.9434 | 118000 | 5.3769        | -      |
| 0.9474 | 118500 | 5.3635        | -      |
| 0.9514 | 119000 | 5.3546        | -      |
| 0.9554 | 119500 | 5.3652        | -      |
| 0.9594 | 120000 | 5.3204        | -      |
| 0.9634 | 120500 | 5.3674        | -      |
| 0.9674 | 121000 | 5.3512        | -      |
| 0.9714 | 121500 | 5.3539        | -      |
| 0.9754 | 122000 | 5.3259        | -      |
| 0.9794 | 122500 | 5.3189        | -      |
| 0.9834 | 123000 | 5.342         | -      |
| 0.9874 | 123500 | 5.3491        | -      |
| 0.9914 | 124000 | 5.3455        | -      |
| 0.9954 | 124500 | 5.3106        | -      |
| 0.9994 | 125000 | 5.2932        | -      |
| 1.0034 | 125500 | 5.3176        | -      |
| 1.0074 | 126000 | 5.3073        | -      |
| 1.0114 | 126500 | 5.3194        | -      |
| 1.0154 | 127000 | 5.2482        | -      |
| 1.0194 | 127500 | 5.2502        | -      |
| 1.0234 | 128000 | 5.3199        | -      |
| 1.0274 | 128500 | 5.2374        | -      |
| 1.0314 | 129000 | 5.2641        | -      |
| 1.0354 | 129500 | 5.2663        | -      |
| 1.0394 | 130000 | 5.2887        | -      |
| 1.0434 | 130500 | 5.2731        | -      |
| 1.0474 | 131000 | 5.2297        | -      |
| 1.0514 | 131500 | 5.2704        | -      |
| 1.0553 | 132000 | 5.2723        | -      |
| 1.0593 | 132500 | 5.2846        | -      |
| 1.0633 | 133000 | 5.2857        | -      |
| 1.0673 | 133500 | 5.2984        | -      |
| 1.0713 | 134000 | 5.2492        | -      |
| 1.0753 | 134500 | 5.2847        | -      |
| 1.0793 | 135000 | 5.2376        | -      |
| 1.0833 | 135500 | 5.2299        | -      |
| 1.0873 | 136000 | 5.2214        | -      |
| 1.0913 | 136500 | 5.2429        | -      |
| 1.0953 | 137000 | 5.2232        | -      |
| 1.0993 | 137500 | 5.261         | -      |
| 1.1033 | 138000 | 5.2394        | -      |
| 1.1073 | 138500 | 5.2877        | -      |
| 1.1113 | 139000 | 5.1936        | -      |
| 1.1153 | 139500 | 5.2483        | -      |
| 1.1193 | 140000 | 5.2412        | -      |
| 1.1233 | 140500 | 5.1841        | -      |
| 1.1273 | 141000 | 5.2741        | -      |
| 1.1313 | 141500 | 5.1711        | -      |
| 1.1353 | 142000 | 5.2154        | -      |
| 1.1393 | 142500 | 5.2667        | -      |
| 1.1433 | 143000 | 5.217         | -      |
| 1.1473 | 143500 | 5.261         | -      |
| 1.1513 | 144000 | 5.2169        | -      |
| 1.1553 | 144500 | 5.2471        | -      |
| 1.1593 | 145000 | 5.2486        | -      |
| 1.1633 | 145500 | 5.2252        | -      |
| 1.1673 | 146000 | 5.2488        | -      |
| 1.1713 | 146500 | 5.184         | -      |
| 1.1753 | 147000 | 5.2547        | -      |
| 1.1793 | 147500 | 5.207         | -      |
| 1.1833 | 148000 | 5.2087        | -      |
| 1.1873 | 148500 | 5.2478        | -      |
| 1.1913 | 149000 | 5.2409        | -      |
| 1.1953 | 149500 | 5.1968        | -      |
| 1.1993 | 150000 | 5.182         | -      |
| 1.2033 | 150500 | 5.1807        | -      |
| 1.2073 | 151000 | 5.1927        | -      |
| 1.2113 | 151500 | 5.1859        | -      |
| 1.2153 | 152000 | 5.1874        | -      |
| 1.2192 | 152500 | 5.2234        | -      |
| 1.2232 | 153000 | 5.1858        | -      |
| 1.2272 | 153500 | 5.2104        | -      |
| 1.2312 | 154000 | 5.2259        | -      |
| 1.2352 | 154500 | 5.2022        | -      |
| 1.2392 | 155000 | 5.2162        | -      |
| 1.2432 | 155500 | 5.1691        | -      |
| 1.2472 | 156000 | 5.1845        | -      |
| 1.2512 | 156500 | 5.1577        | -      |
| 1.2552 | 157000 | 5.1921        | -      |
| 1.2592 | 157500 | 5.2277        | -      |
| 1.2632 | 158000 | 5.2049        | -      |
| 1.2672 | 158500 | 5.1672        | -      |
| 1.2712 | 159000 | 5.2376        | -      |
| 1.2752 | 159500 | 5.1943        | -      |
| 1.2792 | 160000 | 5.1384        | -      |
| 1.2832 | 160500 | 5.1651        | -      |
| 1.2872 | 161000 | 5.1992        | -      |
| 1.2912 | 161500 | 5.1707        | -      |
| 1.2952 | 162000 | 5.1796        | -      |
| 1.2992 | 162500 | 5.0851        | -      |
| 1.3032 | 163000 | 5.202         | -      |
| 1.3072 | 163500 | 5.1546        | -      |
| 1.3112 | 164000 | 5.1962        | -      |
| 1.3152 | 164500 | 5.1498        | -      |
| 1.3192 | 165000 | 5.1587        | -      |
| 1.3232 | 165500 | 5.1674        | -      |
| 1.3272 | 166000 | 5.1375        | -      |
| 1.3312 | 166500 | 5.1558        | -      |
| 1.3352 | 167000 | 5.1298        | -      |
| 1.3392 | 167500 | 5.1469        | -      |
| 1.3432 | 168000 | 5.0647        | -      |
| 1.3472 | 168500 | 5.1455        | -      |
| 1.3512 | 169000 | 5.1312        | -      |
| 1.3552 | 169500 | 5.1067        | -      |
| 1.3592 | 170000 | 5.109         | -      |
| 1.3632 | 170500 | 5.1282        | -      |
| 1.3672 | 171000 | 5.1348        | -      |
| 1.3712 | 171500 | 5.1415        | -      |
| 1.3752 | 172000 | 5.0964        | -      |
| 1.3792 | 172500 | 5.1503        | -      |
| 1.3831 | 173000 | 5.1629        | -      |
| 1.3871 | 173500 | 5.105         | -      |
| 1.3911 | 174000 | 5.0606        | -      |
| 1.3951 | 174500 | 5.151         | -      |
| 1.3991 | 175000 | 5.1262        | -      |
| 1.4031 | 175500 | 5.1856        | -      |
| 1.4071 | 176000 | 5.1216        | -      |
| 1.4111 | 176500 | 5.1419        | -      |
| 1.4151 | 177000 | 5.121         | -      |
| 1.4191 | 177500 | 5.1393        | -      |
| 1.4231 | 178000 | 5.1029        | -      |
| 1.4271 | 178500 | 5.0734        | -      |
| 1.4311 | 179000 | 5.1087        | -      |
| 1.4351 | 179500 | 5.1404        | -      |
| 1.4391 | 180000 | 5.1152        | -      |
| 1.4431 | 180500 | 5.1041        | -      |
| 1.4471 | 181000 | 5.0889        | -      |
| 1.4511 | 181500 | 5.1602        | -      |
| 1.4551 | 182000 | 5.1193        | -      |
| 1.4591 | 182500 | 5.092         | -      |
| 1.4631 | 183000 | 5.0901        | -      |
| 1.4671 | 183500 | 5.0899        | -      |
| 1.4711 | 184000 | 5.1426        | -      |
| 1.4751 | 184500 | 5.0812        | -      |
| 1.4791 | 185000 | 5.0964        | -      |
| 1.4831 | 185500 | 5.0828        | -      |
| 1.4871 | 186000 | 5.116         | -      |
| 1.4911 | 186500 | 5.1069        | -      |
| 1.4951 | 187000 | 5.0598        | -      |
| 1.4991 | 187500 | 5.0734        | -      |
| 1.5031 | 188000 | 5.0516        | -      |
| 1.5071 | 188500 | 5.1049        | -      |
| 1.5111 | 189000 | 5.0636        | -      |
| 1.5151 | 189500 | 5.0715        | -      |
| 1.5191 | 190000 | 5.0757        | -      |
| 1.5231 | 190500 | 5.0947        | -      |
| 1.5271 | 191000 | 5.0433        | -      |
| 1.5311 | 191500 | 5.1079        | -      |
| 1.5351 | 192000 | 5.0872        | -      |
| 1.5391 | 192500 | 5.016         | -      |
| 1.5430 | 193000 | 5.0627        | -      |
| 1.5470 | 193500 | 5.0841        | -      |
| 1.5510 | 194000 | 5.1012        | -      |
| 1.5550 | 194500 | 5.0415        | -      |
| 1.5590 | 195000 | 5.0871        | -      |
| 1.5630 | 195500 | 5.0678        | -      |
| 1.5670 | 196000 | 5.0399        | -      |
| 1.5710 | 196500 | 5.0794        | -      |
| 1.5750 | 197000 | 5.0639        | -      |
| 1.5790 | 197500 | 5.0335        | -      |
| 1.5830 | 198000 | 5.0606        | -      |
| 1.5870 | 198500 | 5.1059        | -      |
| 1.5910 | 199000 | 5.0426        | -      |
| 1.5950 | 199500 | 5.0185        | -      |
| 1.5990 | 200000 | 5.0194        | -      |
| 1.6030 | 200500 | 5.0887        | -      |
| 1.6070 | 201000 | 5.004         | -      |
| 1.6110 | 201500 | 5.0834        | -      |
| 1.6150 | 202000 | 5.0363        | -      |
| 1.6190 | 202500 | 5.0819        | -      |
| 1.6230 | 203000 | 5.0344        | -      |
| 1.6270 | 203500 | 5.107         | -      |
| 1.6310 | 204000 | 5.0201        | -      |
| 1.6350 | 204500 | 5.0305        | -      |
| 1.6390 | 205000 | 5.0074        | -      |
| 1.6430 | 205500 | 5.0507        | -      |
| 1.6470 | 206000 | 5.0419        | -      |
| 1.6510 | 206500 | 5.0099        | -      |
| 1.6550 | 207000 | 5.0673        | -      |
| 1.6590 | 207500 | 5.0449        | -      |
| 1.6630 | 208000 | 4.9631        | -      |
| 1.6670 | 208500 | 5.0013        | -      |
| 1.6710 | 209000 | 5.02          | -      |
| 1.6750 | 209500 | 5.032         | -      |
| 1.6790 | 210000 | 4.9984        | -      |
| 1.6830 | 210500 | 4.987         | -      |
| 1.6870 | 211000 | 5.0095        | -      |
| 1.6910 | 211500 | 5.0801        | -      |
| 1.6950 | 212000 | 5.0061        | -      |
| 1.6990 | 212500 | 5.0193        | -      |
| 1.7030 | 213000 | 5.0453        | -      |
| 1.7069 | 213500 | 4.991         | -      |
| 1.7109 | 214000 | 5.0149        | -      |
| 1.7149 | 214500 | 5.0181        | -      |
| 1.7189 | 215000 | 5.0341        | -      |
| 1.7229 | 215500 | 4.9987        | -      |
| 1.7269 | 216000 | 4.9864        | -      |
| 1.7309 | 216500 | 4.993         | -      |
| 1.7349 | 217000 | 4.9888        | -      |
| 1.7389 | 217500 | 5.0125        | -      |
| 1.7429 | 218000 | 5.0023        | -      |
| 1.7469 | 218500 | 5.0205        | -      |
| 1.7509 | 219000 | 5.0141        | -      |
| 1.7549 | 219500 | 5.0071        | -      |
| 1.7589 | 220000 | 4.9684        | -      |
| 1.7629 | 220500 | 4.9898        | -      |
| 1.7669 | 221000 | 4.9889        | -      |
| 1.7709 | 221500 | 4.9894        | -      |
| 1.7749 | 222000 | 4.989         | -      |
| 1.7789 | 222500 | 4.9179        | -      |
| 1.7829 | 223000 | 4.9818        | -      |
| 1.7869 | 223500 | 5.0056        | -      |
| 1.7909 | 224000 | 4.9475        | -      |
| 1.7949 | 224500 | 5.0104        | -      |
| 1.7989 | 225000 | 4.9827        | -      |
| 1.8029 | 225500 | 4.9716        | -      |
| 1.8069 | 226000 | 4.9924        | -      |
| 1.8109 | 226500 | 5.0384        | -      |
| 1.8149 | 227000 | 4.9853        | -      |
| 1.8189 | 227500 | 4.9858        | -      |
| 1.8229 | 228000 | 4.9423        | -      |
| 1.8269 | 228500 | 4.9476        | -      |
| 1.8309 | 229000 | 4.9631        | -      |
| 1.8349 | 229500 | 4.9819        | -      |
| 1.8389 | 230000 | 4.9464        | -      |
| 1.8429 | 230500 | 4.9688        | -      |
| 1.8469 | 231000 | 4.9569        | -      |
| 1.8509 | 231500 | 4.9515        | -      |
| 1.8549 | 232000 | 4.9447        | -      |
| 1.8589 | 232500 | 4.9845        | -      |
| 1.8629 | 233000 | 4.9834        | -      |
| 1.8669 | 233500 | 4.9545        | -      |
| 1.8708 | 234000 | 4.9452        | -      |
| 1.8748 | 234500 | 4.9623        | -      |
| 1.8788 | 235000 | 4.9869        | -      |
| 1.8828 | 235500 | 4.9603        | -      |
| 1.8868 | 236000 | 4.9504        | -      |
| 1.8908 | 236500 | 4.9537        | -      |
| 1.8948 | 237000 | 5.014         | -      |
| 1.8988 | 237500 | 4.9226        | -      |
| 1.9028 | 238000 | 4.9528        | -      |
| 1.9068 | 238500 | 4.9213        | -      |
| 1.9108 | 239000 | 4.8836        | -      |
| 1.9148 | 239500 | 4.9354        | -      |
| 1.9188 | 240000 | 4.8704        | -      |
| 1.9228 | 240500 | 4.9336        | -      |
| 1.9268 | 241000 | 4.8654        | -      |
| 1.9308 | 241500 | 4.9376        | -      |
| 1.9348 | 242000 | 4.9164        | -      |
| 1.9388 | 242500 | 4.9584        | -      |
| 1.9428 | 243000 | 4.8759        | -      |
| 1.9468 | 243500 | 4.9324        | -      |
| 1.9508 | 244000 | 4.8457        | -      |
| 1.9548 | 244500 | 4.9048        | -      |
| 1.9588 | 245000 | 4.9234        | -      |
| 1.9628 | 245500 | 4.8536        | -      |
| 1.9668 | 246000 | 4.9681        | -      |
| 1.9708 | 246500 | 4.9161        | -      |
| 1.9748 | 247000 | 4.9786        | -      |
| 1.9788 | 247500 | 4.8784        | -      |
| 1.9828 | 248000 | 4.9222        | -      |
| 1.9868 | 248500 | 4.9061        | -      |
| 1.9908 | 249000 | 4.9281        | -      |
| 1.9948 | 249500 | 4.9007        | -      |
| 1.9988 | 250000 | 4.9466        | -      |
| 2.0028 | 250500 | 4.8483        | -      |
| 2.0068 | 251000 | 4.8103        | -      |
| 2.0108 | 251500 | 4.8288        | -      |
| 2.0148 | 252000 | 4.8127        | -      |
| 2.0188 | 252500 | 4.8619        | -      |
| 2.0228 | 253000 | 4.7683        | -      |
| 2.0268 | 253500 | 4.8246        | -      |
| 2.0307 | 254000 | 4.7784        | -      |
| 2.0347 | 254500 | 4.8416        | -      |
| 2.0387 | 255000 | 4.7759        | -      |
| 2.0427 | 255500 | 4.8185        | -      |
| 2.0467 | 256000 | 4.7993        | -      |
| 2.0507 | 256500 | 4.8505        | -      |
| 2.0547 | 257000 | 4.7538        | -      |
| 2.0587 | 257500 | 4.8128        | -      |
| 2.0627 | 258000 | 4.7945        | -      |
| 2.0667 | 258500 | 4.8436        | -      |
| 2.0707 | 259000 | 4.8455        | -      |
| 2.0747 | 259500 | 4.8235        | -      |
| 2.0787 | 260000 | 4.7401        | -      |
| 2.0827 | 260500 | 4.8347        | -      |
| 2.0867 | 261000 | 4.7746        | -      |
| 2.0907 | 261500 | 4.8129        | -      |
| 2.0947 | 262000 | 4.8213        | -      |
| 2.0987 | 262500 | 4.7762        | -      |
| 2.1027 | 263000 | 4.8996        | -      |
| 2.1067 | 263500 | 4.7671        | -      |
| 2.1107 | 264000 | 4.7543        | -      |
| 2.1147 | 264500 | 4.8171        | -      |
| 2.1187 | 265000 | 4.8018        | -      |
| 2.1227 | 265500 | 4.8622        | -      |
| 2.1267 | 266000 | 4.7957        | -      |
| 2.1307 | 266500 | 4.8256        | -      |
| 2.1347 | 267000 | 4.7887        | -      |
| 2.1387 | 267500 | 4.8366        | -      |
| 2.1427 | 268000 | 4.7425        | -      |
| 2.1467 | 268500 | 4.7731        | -      |
| 2.1507 | 269000 | 4.7837        | -      |
| 2.1547 | 269500 | 4.8199        | -      |
| 2.1587 | 270000 | 4.8294        | -      |
| 2.1627 | 270500 | 4.7913        | -      |
| 2.1667 | 271000 | 4.7904        | -      |
| 2.1707 | 271500 | 4.8006        | -      |
| 2.1747 | 272000 | 4.7291        | -      |
| 2.1787 | 272500 | 4.7825        | -      |
| 2.1827 | 273000 | 4.7188        | -      |
| 2.1867 | 273500 | 4.8076        | -      |
| 2.1907 | 274000 | 4.8095        | -      |
| 2.1946 | 274500 | 4.8016        | -      |
| 2.1986 | 275000 | 4.7654        | -      |
| 2.2026 | 275500 | 4.8253        | -      |
| 2.2066 | 276000 | 4.7669        | -      |
| 2.2106 | 276500 | 4.7752        | -      |
| 2.2146 | 277000 | 4.8365        | -      |
| 2.2186 | 277500 | 4.736         | -      |
| 2.2226 | 278000 | 4.7251        | -      |
| 2.2266 | 278500 | 4.7384        | -      |
| 2.2306 | 279000 | 4.7001        | -      |
| 2.2346 | 279500 | 4.8238        | -      |
| 2.2386 | 280000 | 4.7455        | -      |
| 2.2426 | 280500 | 4.7353        | -      |
| 2.2466 | 281000 | 4.7557        | -      |
| 2.2506 | 281500 | 4.7732        | -      |
| 2.2546 | 282000 | 4.7795        | -      |
| 2.2586 | 282500 | 4.8051        | -      |
| 2.2626 | 283000 | 4.7726        | -      |
| 2.2666 | 283500 | 4.8173        | -      |
| 2.2706 | 284000 | 4.7607        | -      |
| 2.2746 | 284500 | 4.7855        | -      |
| 2.2786 | 285000 | 4.8191        | -      |
| 2.2826 | 285500 | 4.7365        | -      |
| 2.2866 | 286000 | 4.7416        | -      |
| 2.2906 | 286500 | 4.7495        | -      |
| 2.2946 | 287000 | 4.7597        | -      |
| 2.2986 | 287500 | 4.7924        | -      |
| 2.3026 | 288000 | 4.7603        | -      |
| 2.3066 | 288500 | 4.7443        | -      |
| 2.3106 | 289000 | 4.7826        | -      |
| 2.3146 | 289500 | 4.7053        | -      |
| 2.3186 | 290000 | 4.7274        | -      |
| 2.3226 | 290500 | 4.7756        | -      |
| 2.3266 | 291000 | 4.7881        | -      |
| 2.3306 | 291500 | 4.7875        | -      |
| 2.3346 | 292000 | 4.7254        | -      |
| 2.3386 | 292500 | 4.7193        | -      |
| 2.3426 | 293000 | 4.7639        | -      |
| 2.3466 | 293500 | 4.7244        | -      |
| 2.3506 | 294000 | 4.7996        | -      |
| 2.3545 | 294500 | 4.7699        | -      |
| 2.3585 | 295000 | 4.7504        | -      |
| 2.3625 | 295500 | 4.6969        | -      |
| 2.3665 | 296000 | 4.7523        | -      |
| 2.3705 | 296500 | 4.6984        | -      |
| 2.3745 | 297000 | 4.7297        | -      |
| 2.3785 | 297500 | 4.7366        | -      |
| 2.3825 | 298000 | 4.7177        | -      |
| 2.3865 | 298500 | 4.7276        | -      |
| 2.3905 | 299000 | 4.7916        | -      |
| 2.3945 | 299500 | 4.7726        | -      |
| 2.3985 | 300000 | 4.6847        | 4.6182 |
| 2.4025 | 300500 | 4.7426        | -      |
| 2.4065 | 301000 | 4.7426        | -      |
| 2.4105 | 301500 | 4.7588        | -      |
| 2.4145 | 302000 | 4.7516        | -      |
| 2.4185 | 302500 | 4.7318        | -      |
| 2.4225 | 303000 | 4.7366        | -      |
| 2.4265 | 303500 | 4.7328        | -      |
| 2.4305 | 304000 | 4.687         | -      |
| 2.4345 | 304500 | 4.6934        | -      |
| 2.4385 | 305000 | 4.8018        | -      |
| 2.4425 | 305500 | 4.709         | -      |
| 2.4465 | 306000 | 4.6837        | -      |
| 2.4505 | 306500 | 4.7268        | -      |
| 2.4545 | 307000 | 4.7508        | -      |
| 2.4585 | 307500 | 4.6809        | -      |
| 2.4625 | 308000 | 4.7127        | -      |
| 2.4665 | 308500 | 4.7319        | -      |
| 2.4705 | 309000 | 4.6616        | -      |
| 2.4745 | 309500 | 4.6795        | -      |
| 2.4785 | 310000 | 4.6834        | -      |
| 2.4825 | 310500 | 4.7565        | -      |
| 2.4865 | 311000 | 4.71          | -      |
| 2.4905 | 311500 | 4.7456        | -      |
| 2.4945 | 312000 | 4.7009        | -      |
| 2.4985 | 312500 | 4.7716        | -      |
| 2.5025 | 313000 | 4.6919        | -      |
| 2.5065 | 313500 | 4.7159        | -      |
| 2.5105 | 314000 | 4.7297        | -      |
| 2.5145 | 314500 | 4.7487        | -      |
| 2.5184 | 315000 | 4.7104        | -      |
| 2.5224 | 315500 | 4.6836        | -      |
| 2.5264 | 316000 | 4.6765        | -      |
| 2.5304 | 316500 | 4.7597        | -      |
| 2.5344 | 317000 | 4.6589        | -      |
| 2.5384 | 317500 | 4.6776        | -      |
| 2.5424 | 318000 | 4.757         | -      |
| 2.5464 | 318500 | 4.7112        | -      |
| 2.5504 | 319000 | 4.7098        | -      |
| 2.5544 | 319500 | 4.7395        | -      |
| 2.5584 | 320000 | 4.7164        | -      |
| 2.5624 | 320500 | 4.759         | -      |
| 2.5664 | 321000 | 4.7274        | -      |
| 2.5704 | 321500 | 4.6674        | -      |
| 2.5744 | 322000 | 4.6751        | -      |
| 2.5784 | 322500 | 4.613         | -      |
| 2.5824 | 323000 | 4.6354        | -      |
| 2.5864 | 323500 | 4.6394        | -      |
| 2.5904 | 324000 | 4.6935        | -      |
| 2.5944 | 324500 | 4.7022        | -      |
| 2.5984 | 325000 | 4.6831        | -      |
| 2.6024 | 325500 | 4.7033        | -      |
| 2.6064 | 326000 | 4.7113        | -      |
| 2.6104 | 326500 | 4.6515        | -      |
| 2.6144 | 327000 | 4.5857        | -      |
| 2.6184 | 327500 | 4.6675        | -      |
| 2.6224 | 328000 | 4.7054        | -      |
| 2.6264 | 328500 | 4.6774        | -      |
| 2.6304 | 329000 | 4.6585        | -      |
| 2.6344 | 329500 | 4.7232        | -      |
| 2.6384 | 330000 | 4.6977        | -      |
| 2.6424 | 330500 | 4.6576        | -      |
| 2.6464 | 331000 | 4.6725        | -      |
| 2.6504 | 331500 | 4.6805        | -      |
| 2.6544 | 332000 | 4.6577        | -      |
| 2.6584 | 332500 | 4.7071        | -      |
| 2.6624 | 333000 | 4.6369        | -      |
| 2.6664 | 333500 | 4.6759        | -      |
| 2.6704 | 334000 | 4.6337        | -      |
| 2.6744 | 334500 | 4.6379        | -      |
| 2.6784 | 335000 | 4.6706        | -      |
| 2.6823 | 335500 | 4.6853        | -      |
| 2.6863 | 336000 | 4.7038        | -      |
| 2.6903 | 336500 | 4.6554        | -      |
| 2.6943 | 337000 | 4.6431        | -      |
| 2.6983 | 337500 | 4.6991        | -      |
| 2.7023 | 338000 | 4.6217        | -      |
| 2.7063 | 338500 | 4.668         | -      |
| 2.7103 | 339000 | 4.6611        | -      |
| 2.7143 | 339500 | 4.6793        | -      |
| 2.7183 | 340000 | 4.6465        | -      |
| 2.7223 | 340500 | 4.6846        | -      |
| 2.7263 | 341000 | 4.6407        | -      |
| 2.7303 | 341500 | 4.7138        | -      |
| 2.7343 | 342000 | 4.659         | -      |
| 2.7383 | 342500 | 4.6315        | -      |
| 2.7423 | 343000 | 4.6272        | -      |
| 2.7463 | 343500 | 4.6833        | -      |
| 2.7503 | 344000 | 4.6754        | -      |
| 2.7543 | 344500 | 4.653         | -      |
| 2.7583 | 345000 | 4.6996        | -      |
| 2.7623 | 345500 | 4.679         | -      |
| 2.7663 | 346000 | 4.6452        | -      |
| 2.7703 | 346500 | 4.6275        | -      |
| 2.7743 | 347000 | 4.6215        | -      |
| 2.7783 | 347500 | 4.654         | -      |
| 2.7823 | 348000 | 4.5852        | -      |
| 2.7863 | 348500 | 4.5764        | -      |
| 2.7903 | 349000 | 4.641         | -      |
| 2.7943 | 349500 | 4.6139        | -      |
| 2.7983 | 350000 | 4.6775        | -      |
| 2.8023 | 350500 | 4.6022        | -      |
| 2.8063 | 351000 | 4.6272        | -      |
| 2.8103 | 351500 | 4.6111        | -      |
| 2.8143 | 352000 | 4.58          | -      |
| 2.8183 | 352500 | 4.6763        | -      |
| 2.8223 | 353000 | 4.6233        | -      |
| 2.8263 | 353500 | 4.5973        | -      |
| 2.8303 | 354000 | 4.6608        | -      |
| 2.8343 | 354500 | 4.592         | -      |
| 2.8383 | 355000 | 4.6801        | -      |
| 2.8422 | 355500 | 4.6838        | -      |
| 2.8462 | 356000 | 4.596         | -      |
| 2.8502 | 356500 | 4.59          | -      |
| 2.8542 | 357000 | 4.5696        | -      |
| 2.8582 | 357500 | 4.5852        | -      |
| 2.8622 | 358000 | 4.6176        | -      |
| 2.8662 | 358500 | 4.7878        | -      |
| 2.8702 | 359000 | 4.5917        | -      |
| 2.8742 | 359500 | 4.659         | -      |
| 2.8782 | 360000 | 4.6217        | -      |
| 2.8822 | 360500 | 4.5605        | -      |
| 2.8862 | 361000 | 4.5948        | -      |
| 2.8902 | 361500 | 4.6097        | -      |
| 2.8942 | 362000 | 4.6381        | -      |
| 2.8982 | 362500 | 4.5962        | -      |
| 2.9022 | 363000 | 4.6115        | -      |
| 2.9062 | 363500 | 4.6171        | -      |
| 2.9102 | 364000 | 4.6593        | -      |
| 2.9142 | 364500 | 4.6264        | -      |
| 2.9182 | 365000 | 4.6625        | -      |
| 2.9222 | 365500 | 4.6538        | -      |
| 2.9262 | 366000 | 4.6148        | -      |
| 2.9302 | 366500 | 4.6189        | -      |
| 2.9342 | 367000 | 4.6181        | -      |
| 2.9382 | 367500 | 4.6206        | -      |
| 2.9422 | 368000 | 4.6196        | -      |
| 2.9462 | 368500 | 4.6006        | -      |
| 2.9502 | 369000 | 4.6136        | -      |
| 2.9542 | 369500 | 4.6126        | -      |
| 2.9582 | 370000 | 4.5722        | -      |
| 2.9622 | 370500 | 4.6372        | -      |
| 2.9662 | 371000 | 4.5645        | -      |
| 2.9702 | 371500 | 4.6004        | -      |
| 2.9742 | 372000 | 4.6302        | -      |
| 2.9782 | 372500 | 4.596         | -      |
| 2.9822 | 373000 | 4.5669        | -      |
| 2.9862 | 373500 | 4.602         | -      |
| 2.9902 | 374000 | 4.5663        | -      |
| 2.9942 | 374500 | 4.642         | -      |
| 2.9982 | 375000 | 4.6571        | -      |
| 3.0022 | 375500 | 4.5416        | -      |
| 3.0061 | 376000 | 4.401         | -      |
| 3.0101 | 376500 | 4.5286        | -      |
| 3.0141 | 377000 | 4.5782        | -      |
| 3.0181 | 377500 | 4.4813        | -      |
| 3.0221 | 378000 | 4.6178        | -      |
| 3.0261 | 378500 | 4.465         | -      |
| 3.0301 | 379000 | 4.54          | -      |
| 3.0341 | 379500 | 4.4754        | -      |
| 3.0381 | 380000 | 4.4952        | -      |
| 3.0421 | 380500 | 4.4559        | -      |
| 3.0461 | 381000 | 4.4628        | -      |
| 3.0501 | 381500 | 4.4921        | -      |
| 3.0541 | 382000 | 4.5431        | -      |
| 3.0581 | 382500 | 4.458         | -      |
| 3.0621 | 383000 | 4.4635        | -      |
| 3.0661 | 383500 | 4.5708        | -      |
| 3.0701 | 384000 | 4.527         | -      |
| 3.0741 | 384500 | 4.4934        | -      |
| 3.0781 | 385000 | 4.5327        | -      |
| 3.0821 | 385500 | 4.4403        | -      |
| 3.0861 | 386000 | 4.5091        | -      |
| 3.0901 | 386500 | 4.5706        | -      |
| 3.0941 | 387000 | 4.5111        | -      |
| 3.0981 | 387500 | 4.4952        | -      |
| 3.1021 | 388000 | 4.5416        | -      |
| 3.1061 | 388500 | 4.4591        | -      |
| 3.1101 | 389000 | 4.4738        | -      |
| 3.1141 | 389500 | 4.514         | -      |
| 3.1181 | 390000 | 4.5517        | -      |
| 3.1221 | 390500 | 4.5412        | -      |
| 3.1261 | 391000 | 4.4587        | -      |
| 3.1301 | 391500 | 4.4099        | -      |
| 3.1341 | 392000 | 4.5022        | -      |
| 3.1381 | 392500 | 4.4698        | -      |
| 3.1421 | 393000 | 4.4923        | -      |
| 3.1461 | 393500 | 4.4601        | -      |
| 3.1501 | 394000 | 4.5446        | -      |
| 3.1541 | 394500 | 4.4247        | -      |
| 3.1581 | 395000 | 4.4242        | -      |
| 3.1621 | 395500 | 4.4761        | -      |
| 3.1660 | 396000 | 4.4489        | -      |
| 3.1700 | 396500 | 4.4729        | -      |
| 3.1740 | 397000 | 4.4916        | -      |
| 3.1780 | 397500 | 4.4595        | -      |
| 3.1820 | 398000 | 4.4726        | -      |
| 3.1860 | 398500 | 4.4582        | -      |
| 3.1900 | 399000 | 4.4528        | -      |
| 3.1940 | 399500 | 4.4559        | -      |
| 3.1980 | 400000 | 4.4422        | -      |
| 3.2020 | 400500 | 4.4876        | -      |
| 3.2060 | 401000 | 4.4733        | -      |
| 3.2100 | 401500 | 4.4214        | -      |
| 3.2140 | 402000 | 4.4644        | -      |
| 3.2180 | 402500 | 4.4732        | -      |
| 3.2220 | 403000 | 4.4603        | -      |
| 3.2260 | 403500 | 4.4993        | -      |
| 3.2300 | 404000 | 4.4994        | -      |
| 3.2340 | 404500 | 4.4778        | -      |
| 3.2380 | 405000 | 4.5121        | -      |
| 3.2420 | 405500 | 4.4108        | -      |
| 3.2460 | 406000 | 4.3834        | -      |
| 3.2500 | 406500 | 4.4434        | -      |
| 3.2540 | 407000 | 4.4464        | -      |
| 3.2580 | 407500 | 4.4645        | -      |
| 3.2620 | 408000 | 4.5341        | -      |
| 3.2660 | 408500 | 4.5013        | -      |
| 3.2700 | 409000 | 4.4671        | -      |
| 3.2740 | 409500 | 4.4962        | -      |
| 3.2780 | 410000 | 4.444         | -      |
| 3.2820 | 410500 | 4.5596        | -      |
| 3.2860 | 411000 | 4.5458        | -      |
| 3.2900 | 411500 | 4.4768        | -      |
| 3.2940 | 412000 | 4.5219        | -      |
| 3.2980 | 412500 | 4.4747        | -      |
| 3.3020 | 413000 | 4.5522        | -      |
| 3.3060 | 413500 | 4.4709        | -      |
| 3.3100 | 414000 | 4.4982        | -      |
| 3.3140 | 414500 | 4.4459        | -      |
| 3.3180 | 415000 | 4.4523        | -      |
| 3.3220 | 415500 | 4.4214        | -      |
| 3.3260 | 416000 | 4.3863        | -      |
| 3.3299 | 416500 | 4.4348        | -      |
| 3.3339 | 417000 | 4.4873        | -      |
| 3.3379 | 417500 | 4.5004        | -      |
| 3.3419 | 418000 | 4.5359        | -      |
| 3.3459 | 418500 | 4.458         | -      |
| 3.3499 | 419000 | 4.4721        | -      |
| 3.3539 | 419500 | 4.5148        | -      |
| 3.3579 | 420000 | 4.4239        | -      |
| 3.3619 | 420500 | 4.423         | -      |
| 3.3659 | 421000 | 4.4774        | -      |
| 3.3699 | 421500 | 4.4258        | -      |
| 3.3739 | 422000 | 4.5019        | -      |
| 3.3779 | 422500 | 4.4487        | -      |
| 3.3819 | 423000 | 4.4691        | -      |
| 3.3859 | 423500 | 4.5267        | -      |
| 3.3899 | 424000 | 4.4422        | -      |
| 3.3939 | 424500 | 4.4965        | -      |
| 3.3979 | 425000 | 4.407         | -      |
| 3.4019 | 425500 | 4.4443        | -      |
| 3.4059 | 426000 | 4.5078        | -      |
| 3.4099 | 426500 | 4.4561        | -      |
| 3.4139 | 427000 | 4.4057        | -      |
| 3.4179 | 427500 | 4.4829        | -      |
| 3.4219 | 428000 | 4.4281        | -      |
| 3.4259 | 428500 | 4.4486        | -      |
| 3.4299 | 429000 | 4.4626        | -      |
| 3.4339 | 429500 | 4.4792        | -      |
| 3.4379 | 430000 | 4.4109        | -      |
| 3.4419 | 430500 | 4.531         | -      |
| 3.4459 | 431000 | 4.4599        | -      |
| 3.4499 | 431500 | 4.376         | -      |
| 3.4539 | 432000 | 4.4899        | -      |
| 3.4579 | 432500 | 4.4339        | -      |
| 3.4619 | 433000 | 4.3908        | -      |
| 3.4659 | 433500 | 4.3601        | -      |
| 3.4699 | 434000 | 4.4492        | -      |
| 3.4739 | 434500 | 4.4114        | -      |
| 3.4779 | 435000 | 4.3885        | -      |
| 3.4819 | 435500 | 4.4452        | -      |
| 3.4859 | 436000 | 4.4125        | -      |
| 3.4899 | 436500 | 4.4369        | -      |
| 3.4938 | 437000 | 4.4511        | -      |
| 3.4978 | 437500 | 4.4088        | -      |
| 3.5018 | 438000 | 4.4583        | -      |
| 3.5058 | 438500 | 4.4259        | -      |
| 3.5098 | 439000 | 4.4397        | -      |
| 3.5138 | 439500 | 4.3635        | -      |
| 3.5178 | 440000 | 4.4461        | -      |
| 3.5218 | 440500 | 4.4595        | -      |
| 3.5258 | 441000 | 4.5471        | -      |
| 3.5298 | 441500 | 4.4739        | -      |
| 3.5338 | 442000 | 4.4534        | -      |
| 3.5378 | 442500 | 4.369         | -      |
| 3.5418 | 443000 | 4.4104        | -      |
| 3.5458 | 443500 | 4.4219        | -      |
| 3.5498 | 444000 | 4.3791        | -      |
| 3.5538 | 444500 | 4.4981        | -      |
| 3.5578 | 445000 | 4.3899        | -      |
| 3.5618 | 445500 | 4.4454        | -      |
| 3.5658 | 446000 | 4.3621        | -      |
| 3.5698 | 446500 | 4.4437        | -      |
| 3.5738 | 447000 | 4.4827        | -      |
| 3.5778 | 447500 | 4.4761        | -      |
| 3.5818 | 448000 | 4.4698        | -      |
| 3.5858 | 448500 | 4.4386        | -      |
| 3.5898 | 449000 | 4.4147        | -      |
| 3.5938 | 449500 | 4.4644        | -      |
| 3.5978 | 450000 | 4.3913        | -      |
| 3.6018 | 450500 | 4.4642        | -      |
| 3.6058 | 451000 | 4.4529        | -      |
| 3.6098 | 451500 | 4.4912        | -      |
| 3.6138 | 452000 | 4.3711        | -      |
| 3.6178 | 452500 | 4.5121        | -      |
| 3.6218 | 453000 | 4.4718        | -      |
| 3.6258 | 453500 | 4.4593        | -      |
| 3.6298 | 454000 | 4.3954        | -      |
| 3.6338 | 454500 | 4.3387        | -      |
| 3.6378 | 455000 | 4.3933        | -      |
| 3.6418 | 455500 | 4.4689        | -      |
| 3.6458 | 456000 | 4.4221        | -      |
| 3.6498 | 456500 | 4.4561        | -      |
| 3.6537 | 457000 | 4.3731        | -      |
| 3.6577 | 457500 | 4.4143        | -      |
| 3.6617 | 458000 | 4.4378        | -      |
| 3.6657 | 458500 | 4.3778        | -      |
| 3.6697 | 459000 | 4.4467        | -      |
| 3.6737 | 459500 | 4.4633        | -      |
| 3.6777 | 460000 | 4.438         | -      |
| 3.6817 | 460500 | 4.4633        | -      |
| 3.6857 | 461000 | 4.4209        | -      |
| 3.6897 | 461500 | 4.4838        | -      |
| 3.6937 | 462000 | 4.3811        | -      |
| 3.6977 | 462500 | 4.4388        | -      |
| 3.7017 | 463000 | 4.462         | -      |
| 3.7057 | 463500 | 4.3969        | -      |
| 3.7097 | 464000 | 4.4324        | -      |
| 3.7137 | 464500 | 4.3826        | -      |
| 3.7177 | 465000 | 4.4277        | -      |
| 3.7217 | 465500 | 4.4819        | -      |
| 3.7257 | 466000 | 4.4314        | -      |
| 3.7297 | 466500 | 4.4364        | -      |
| 3.7337 | 467000 | 4.4119        | -      |
| 3.7377 | 467500 | 4.4413        | -      |
| 3.7417 | 468000 | 4.434         | -      |
| 3.7457 | 468500 | 4.4263        | -      |
| 3.7497 | 469000 | 4.4241        | -      |
| 3.7537 | 469500 | 4.4055        | -      |
| 3.7577 | 470000 | 4.4456        | -      |
| 3.7617 | 470500 | 4.4056        | -      |
| 3.7657 | 471000 | 4.4655        | -      |
| 3.7697 | 471500 | 4.4211        | -      |
| 3.7737 | 472000 | 4.4314        | -      |
| 3.7777 | 472500 | 4.4651        | -      |
| 3.7817 | 473000 | 4.3825        | -      |
| 3.7857 | 473500 | 4.4665        | -      |
| 3.7897 | 474000 | 4.4425        | -      |
| 3.7937 | 474500 | 4.3956        | -      |
| 3.7977 | 475000 | 4.4302        | -      |
| 3.8017 | 475500 | 4.4057        | -      |
| 3.8057 | 476000 | 4.337         | -      |
| 3.8097 | 476500 | 4.4776        | -      |
| 3.8137 | 477000 | 4.4289        | -      |
| 3.8176 | 477500 | 4.4508        | -      |
| 3.8216 | 478000 | 4.4438        | -      |
| 3.8256 | 478500 | 4.3681        | -      |
| 3.8296 | 479000 | 4.4492        | -      |
| 3.8336 | 479500 | 4.3427        | -      |
| 3.8376 | 480000 | 4.4412        | -      |
| 3.8416 | 480500 | 4.385         | -      |
| 3.8456 | 481000 | 4.401         | -      |
| 3.8496 | 481500 | 4.3606        | -      |
| 3.8536 | 482000 | 4.3404        | -      |
| 3.8576 | 482500 | 4.4591        | -      |
| 3.8616 | 483000 | 4.3984        | -      |
| 3.8656 | 483500 | 4.4195        | -      |
| 3.8696 | 484000 | 4.4414        | -      |
| 3.8736 | 484500 | 4.4028        | -      |
| 3.8776 | 485000 | 4.3685        | -      |
| 3.8816 | 485500 | 4.3658        | -      |
| 3.8856 | 486000 | 4.3967        | -      |
| 3.8896 | 486500 | 4.5247        | -      |
| 3.8936 | 487000 | 4.4359        | -      |
| 3.8976 | 487500 | 4.4493        | -      |
| 3.9016 | 488000 | 4.4604        | -      |
| 3.9056 | 488500 | 4.3784        | -      |
| 3.9096 | 489000 | 4.4631        | -      |
| 3.9136 | 489500 | 4.4359        | -      |
| 3.9176 | 490000 | 4.3923        | -      |
| 3.9216 | 490500 | 4.3844        | -      |
| 3.9256 | 491000 | 4.41          | -      |
| 3.9296 | 491500 | 4.4547        | -      |
| 3.9336 | 492000 | 4.3895        | -      |
| 3.9376 | 492500 | 4.3827        | -      |
| 3.9416 | 493000 | 4.3615        | -      |
| 3.9456 | 493500 | 4.4237        | -      |
| 3.9496 | 494000 | 4.4513        | -      |
| 3.9536 | 494500 | 4.4038        | -      |
| 3.9576 | 495000 | 4.4093        | -      |
| 3.9616 | 495500 | 4.3791        | -      |
| 3.9656 | 496000 | 4.2971        | -      |
| 3.9696 | 496500 | 4.4166        | -      |
| 3.9736 | 497000 | 4.454         | -      |
| 3.9775 | 497500 | 4.4146        | -      |
| 3.9815 | 498000 | 4.3621        | -      |
| 3.9855 | 498500 | 4.3588        | -      |
| 3.9895 | 499000 | 4.4758        | -      |
| 3.9935 | 499500 | 4.4936        | -      |
| 3.9975 | 500000 | 4.4108        | -      |

</details>

### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.2
- PyTorch: 2.4.1+cu118
- Accelerate: 1.0.1
- Datasets: 3.0.1
- Tokenizers: 0.20.3

## 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",
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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