---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:12683
- loss:CustomBatchAllTripletLoss
widget:
- source_sentence: 科目：コンクリート。名称：配管ﾋﾟｯﾄ土間ｺﾝｸﾘｰﾄ。摘要：FC21 S18AE減水剤。備考：コンクリー 4。
  sentences:
  - 科目：ユニット及びその他。名称：地下#階ｼｬﾜｰ室他ﾕﾆｯﾄｼｬﾜｰ。
  - 科目：ユニット及びその他。名称：#階議長室他造作棚。
  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ（嵩上げ）。
- source_sentence: 科目：ユニット及びその他。名称：La-#自動扉注意喚起ｻｲﾝ。
  sentences:
  - 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。
  - 科目：タイル。名称：手洗い水周りﾀｲﾙ。
  - 科目：コンクリート。名称：高流動ｺﾝｸﾘｰﾄ。摘要：FC36N/mm2 ｽﾗﾝﾌﾟﾌﾛｰ55～65高性能AE減水剤。備考：代価表    0058。
- source_sentence: 科目：ユニット及びその他。名称：病棟ｽﾀｯﾌｽﾃｰｼｮﾝｶｳﾝﾀｰ。
  sentences:
  - 科目：ユニット及びその他。名称：利用規定ｻｲﾝ。
  - 科目：タイル。名称：ﾋﾟﾛﾃｨ柱壁ﾀｲﾙ。
  - 科目：ユニット及びその他。名称：屋外階段階数表示。
- source_sentence: 科目：ユニット及びその他。名称：ｶｳﾝﾀｰ案内ｻｲﾝ。
  sentences:
  - 科目：ユニット及びその他。名称：EX-#ｲﾝﾀｰﾎﾝｻｲﾝB。
  - 科目：ユニット及びその他。名称：Sc-#誘導ｻｲﾝ（壁付ﾀｲﾌﾟ）。
  - 科目：ユニット及びその他。名称：#aｴﾝﾄﾗﾝｽ･衝突防止ﾊﾟﾀｰﾝｼｰﾄ。
- source_sentence: 科目：ユニット及びその他。名称：ﾃﾗｽ床再生木ﾃﾞｯｷ。
  sentences:
  - 科目：ユニット及びその他。名称：ﾌﾗｯﾄﾃﾗｽ床ｳｯﾄﾞﾃﾞｯｷ。
  - 科目：ユニット及びその他。名称：駐車ｿﾞｰﾝｻｲﾝ。
  - 科目：ユニット及びその他。名称：#階 MWC､WWC他姿見鏡。
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer

This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-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:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_6")
# Run inference
sentences = [
    '科目：ユニット及びその他。名称：ﾃﾗｽ床再生木ﾃﾞｯｷ。',
    '科目：ユニット及びその他。名称：駐車ｿﾞｰﾝｻｲﾝ。',
    '科目：ユニット及びその他。名称：#階 MWC､WWC他姿見鏡。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# 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 Dataset

#### Unnamed Dataset

* Size: 12,683 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                           | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | type    | string                                                                             | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              |
  | details | <ul><li>min: 11 tokens</li><li>mean: 18.16 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.30%</li><li>5: ~0.30%</li><li>6: ~0.30%</li><li>7: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.30%</li><li>11: ~0.30%</li><li>12: ~1.10%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.30%</li><li>16: ~0.30%</li><li>17: ~0.30%</li><li>18: ~0.30%</li><li>19: ~0.30%</li><li>20: ~0.30%</li><li>21: ~0.30%</li><li>22: ~0.30%</li><li>23: ~0.40%</li><li>24: ~0.30%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.90%</li><li>28: ~0.30%</li><li>29: ~0.40%</li><li>30: ~0.30%</li><li>31: ~1.10%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.30%</li><li>41: ~0.30%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.30%</li><li>45: ~0.30%</li><li>46: ~0.30%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.40%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.60%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.30%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.30%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.30%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.30%</li><li>70: ~0.30%</li><li>71: ~0.30%</li><li>72: ~0.50%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.30%</li><li>78: ~0.30%</li><li>79: ~0.30%</li><li>80: ~0.30%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.30%</li><li>84: ~0.30%</li><li>85: ~0.30%</li><li>86: ~0.30%</li><li>87: ~0.30%</li><li>88: ~0.80%</li><li>89: ~0.30%</li><li>90: ~0.30%</li><li>91: ~0.30%</li><li>92: ~0.30%</li><li>93: ~0.30%</li><li>94: ~0.30%</li><li>95: ~0.30%</li><li>96: ~0.30%</li><li>97: ~0.50%</li><li>98: ~0.30%</li><li>99: ~0.30%</li><li>100: ~0.30%</li><li>101: ~0.30%</li><li>102: ~0.80%</li><li>103: ~0.60%</li><li>104: ~0.50%</li><li>105: ~0.30%</li><li>106: ~0.30%</li><li>107: ~16.50%</li><li>108: ~0.30%</li><li>109: ~0.30%</li><li>110: ~0.30%</li><li>111: ~0.30%</li><li>112: ~0.30%</li><li>113: ~0.30%</li><li>114: ~0.30%</li><li>115: ~0.30%</li><li>116: ~0.50%</li><li>117: ~0.30%</li><li>118: ~0.30%</li><li>119: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.30%</li><li>122: ~0.30%</li><li>123: ~0.30%</li><li>124: ~0.30%</li><li>125: ~0.70%</li><li>126: ~0.30%</li><li>127: ~0.30%</li><li>128: ~0.30%</li><li>129: ~0.40%</li><li>130: ~2.10%</li><li>131: ~2.10%</li><li>132: ~0.30%</li><li>133: ~0.30%</li><li>134: ~0.50%</li><li>135: ~0.50%</li><li>136: ~0.50%</li><li>137: ~0.40%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.30%</li><li>144: ~0.30%</li><li>145: ~0.30%</li><li>146: ~0.30%</li><li>147: ~0.30%</li><li>148: ~0.30%</li><li>149: ~0.30%</li><li>150: ~0.30%</li><li>151: ~0.30%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~0.50%</li><li>155: ~0.30%</li><li>156: ~0.40%</li><li>157: ~0.30%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~0.30%</li><li>161: ~0.30%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.30%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~0.30%</li><li>169: ~0.40%</li><li>170: ~0.30%</li><li>171: ~0.30%</li><li>172: ~0.30%</li><li>173: ~0.30%</li><li>174: ~0.30%</li><li>175: ~0.30%</li><li>176: ~0.70%</li><li>177: ~0.30%</li><li>178: ~0.30%</li><li>179: ~0.30%</li><li>180: ~0.30%</li><li>181: ~1.30%</li><li>182: ~0.30%</li><li>183: ~0.40%</li><li>184: ~0.30%</li><li>185: ~0.30%</li><li>186: ~0.30%</li><li>187: ~1.50%</li><li>188: ~0.30%</li><li>189: ~0.30%</li><li>190: ~0.30%</li><li>191: ~0.30%</li><li>192: ~0.30%</li><li>193: ~0.30%</li><li>194: ~0.30%</li><li>195: ~1.60%</li><li>196: ~0.30%</li><li>197: ~0.30%</li><li>198: ~7.20%</li><li>199: ~0.30%</li><li>200: ~1.00%</li><li>201: ~0.30%</li><li>202: ~0.30%</li><li>203: ~0.30%</li><li>204: ~0.90%</li></ul> |
* Samples:
  | sentence                                 | label          |
  |:-----------------------------------------|:---------------|
  | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
  | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
  | <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
* Loss: <code>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code>

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 250
- `warmup_ratio`: 0.2
- `fp16`: True
- `batch_sampler`: group_by_label

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `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`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 250
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `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}
- `tp_size`: 0
- `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`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `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
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: group_by_label
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch    | Step | Training Loss |
|:--------:|:----:|:-------------:|
| 2.16     | 50   | 0.0584        |
| 4.32     | 100  | 0.0591        |
| 6.48     | 150  | 0.0675        |
| 8.64     | 200  | 0.0637        |
| 10.8     | 250  | 0.0637        |
| 13.04    | 300  | 0.0647        |
| 15.2     | 350  | 0.0656        |
| 17.36    | 400  | 0.0578        |
| 19.52    | 450  | 0.0585        |
| 21.68    | 500  | 0.0546        |
| 23.84    | 550  | 0.0523        |
| 26.08    | 600  | 0.0563        |
| 28.24    | 650  | 0.0526        |
| 30.4     | 700  | 0.0532        |
| 32.56    | 750  | 0.0546        |
| 34.72    | 800  | 0.0483        |
| 36.88    | 850  | 0.0566        |
| 39.12    | 900  | 0.0482        |
| 41.28    | 950  | 0.0508        |
| 43.44    | 1000 | 0.05          |
| 45.6     | 1050 | 0.0471        |
| 47.76    | 1100 | 0.0502        |
| 49.92    | 1150 | 0.0477        |
| 52.16    | 1200 | 0.0429        |
| 54.32    | 1250 | 0.0415        |
| 56.48    | 1300 | 0.0433        |
| 58.64    | 1350 | 0.0489        |
| 60.8     | 1400 | 0.0494        |
| 63.04    | 1450 | 0.0412        |
| 65.2     | 1500 | 0.0447        |
| 67.36    | 1550 | 0.0379        |
| 69.52    | 1600 | 0.0401        |
| 71.68    | 1650 | 0.0449        |
| 73.84    | 1700 | 0.0377        |
| 76.08    | 1750 | 0.0375        |
| 78.24    | 1800 | 0.0394        |
| 80.4     | 1850 | 0.0392        |
| 82.56    | 1900 | 0.0404        |
| 84.72    | 1950 | 0.0392        |
| 86.88    | 2000 | 0.0427        |
| 89.12    | 2050 | 0.0357        |
| 91.28    | 2100 | 0.0339        |
| 93.44    | 2150 | 0.0443        |
| 95.6     | 2200 | 0.0405        |
| 97.76    | 2250 | 0.0362        |
| 99.92    | 2300 | 0.0323        |
| 102.16   | 2350 | 0.0335        |
| 104.32   | 2400 | 0.0408        |
| 106.48   | 2450 | 0.034         |
| 108.64   | 2500 | 0.0383        |
| 110.8    | 2550 | 0.0299        |
| 113.04   | 2600 | 0.0306        |
| 115.2    | 2650 | 0.0351        |
| 117.36   | 2700 | 0.0322        |
| 119.52   | 2750 | 0.041         |
| 121.68   | 2800 | 0.0292        |
| 123.84   | 2850 | 0.027         |
| 126.08   | 2900 | 0.0323        |
| 128.24   | 2950 | 0.0355        |
| 130.4    | 3000 | 0.0366        |
| 132.56   | 3050 | 0.0312        |
| 134.72   | 3100 | 0.0279        |
| 136.88   | 3150 | 0.0306        |
| 139.12   | 3200 | 0.0245        |
| 141.28   | 3250 | 0.0325        |
| 143.44   | 3300 | 0.0356        |
| 145.6    | 3350 | 0.0362        |
| 147.76   | 3400 | 0.0287        |
| 149.92   | 3450 | 0.0339        |
| 1.6389   | 50   | 0.0386        |
| 3.5278   | 100  | 0.0366        |
| 5.4167   | 150  | 0.0364        |
| 7.3056   | 200  | 0.0394        |
| 9.1944   | 250  | 0.0387        |
| 11.0833  | 300  | 0.0407        |
| 12.7222  | 350  | 0.0392        |
| 14.6111  | 400  | 0.0395        |
| 16.5     | 450  | 0.0393        |
| 18.3889  | 500  | 0.0361        |
| 20.2778  | 550  | 0.0347        |
| 22.1667  | 600  | 0.0346        |
| 24.0556  | 650  | 0.0371        |
| 25.6944  | 700  | 0.0411        |
| 27.5833  | 750  | 0.0329        |
| 29.4722  | 800  | 0.0337        |
| 31.3611  | 850  | 0.0325        |
| 33.25    | 900  | 0.034         |
| 35.1389  | 950  | 0.0352        |
| 37.0278  | 1000 | 0.0305        |
| 38.6667  | 1050 | 0.0311        |
| 40.5556  | 1100 | 0.0314        |
| 42.4444  | 1150 | 0.0307        |
| 44.3333  | 1200 | 0.0324        |
| 46.2222  | 1250 | 0.0355        |
| 48.1111  | 1300 | 0.0306        |
| 49.75    | 1350 | 0.027         |
| 51.6389  | 1400 | 0.0282        |
| 53.5278  | 1450 | 0.0318        |
| 55.4167  | 1500 | 0.0314        |
| 57.3056  | 1550 | 0.0323        |
| 59.1944  | 1600 | 0.0286        |
| 61.0833  | 1650 | 0.0338        |
| 62.7222  | 1700 | 0.0287        |
| 64.6111  | 1750 | 0.0309        |
| 66.5     | 1800 | 0.0287        |
| 68.3889  | 1850 | 0.028         |
| 70.2778  | 1900 | 0.026         |
| 72.1667  | 1950 | 0.0269        |
| 74.0556  | 2000 | 0.0295        |
| 75.6944  | 2050 | 0.0257        |
| 77.5833  | 2100 | 0.0261        |
| 79.4722  | 2150 | 0.0304        |
| 81.3611  | 2200 | 0.0265        |
| 83.25    | 2250 | 0.0274        |
| 85.1389  | 2300 | 0.0276        |
| 87.0278  | 2350 | 0.0325        |
| 88.6667  | 2400 | 0.0233        |
| 90.5556  | 2450 | 0.0212        |
| 92.4444  | 2500 | 0.0243        |
| 94.3333  | 2550 | 0.0288        |
| 96.2222  | 2600 | 0.026         |
| 98.1111  | 2650 | 0.029         |
| 99.75    | 2700 | 0.0228        |
| 101.6389 | 2750 | 0.0265        |
| 103.5278 | 2800 | 0.017         |
| 105.4167 | 2850 | 0.026         |
| 107.3056 | 2900 | 0.0257        |
| 109.1944 | 2950 | 0.0237        |
| 111.0833 | 3000 | 0.0261        |
| 112.7222 | 3050 | 0.0204        |
| 114.6111 | 3100 | 0.0186        |
| 116.5    | 3150 | 0.0206        |
| 118.3889 | 3200 | 0.0233        |
| 120.2778 | 3250 | 0.0235        |
| 122.1667 | 3300 | 0.0232        |
| 124.0556 | 3350 | 0.0194        |
| 125.6944 | 3400 | 0.0242        |
| 127.5833 | 3450 | 0.0234        |
| 129.4722 | 3500 | 0.023         |
| 131.3611 | 3550 | 0.0187        |
| 133.25   | 3600 | 0.0208        |
| 135.1389 | 3650 | 0.0201        |
| 137.0278 | 3700 | 0.024         |
| 138.6667 | 3750 | 0.0255        |
| 140.5556 | 3800 | 0.0201        |
| 142.4444 | 3850 | 0.0231        |
| 144.3333 | 3900 | 0.0199        |
| 146.2222 | 3950 | 0.018         |
| 148.1111 | 4000 | 0.0228        |
| 149.75   | 4050 | 0.0204        |
| 151.6389 | 4100 | 0.025         |
| 153.5278 | 4150 | 0.0163        |
| 155.4167 | 4200 | 0.0157        |
| 157.3056 | 4250 | 0.0189        |
| 159.1944 | 4300 | 0.0176        |
| 161.0833 | 4350 | 0.03          |
| 162.7222 | 4400 | 0.0197        |
| 164.6111 | 4450 | 0.0207        |
| 166.5    | 4500 | 0.0189        |
| 168.3889 | 4550 | 0.0132        |
| 170.2778 | 4600 | 0.0178        |
| 172.1667 | 4650 | 0.0216        |
| 174.0556 | 4700 | 0.0174        |
| 175.6944 | 4750 | 0.0229        |
| 177.5833 | 4800 | 0.0181        |
| 179.4722 | 4850 | 0.0161        |
| 181.3611 | 4900 | 0.0236        |
| 183.25   | 4950 | 0.0185        |
| 185.1389 | 5000 | 0.02          |
| 187.0278 | 5050 | 0.0147        |
| 188.6667 | 5100 | 0.0203        |
| 190.5556 | 5150 | 0.0159        |
| 192.4444 | 5200 | 0.0133        |
| 194.3333 | 5250 | 0.0192        |
| 196.2222 | 5300 | 0.0162        |
| 198.1111 | 5350 | 0.0183        |
| 199.75   | 5400 | 0.015         |
| 201.6389 | 5450 | 0.0145        |
| 203.5278 | 5500 | 0.017         |
| 205.4167 | 5550 | 0.0219        |
| 207.3056 | 5600 | 0.0195        |
| 209.1944 | 5650 | 0.0186        |
| 211.0833 | 5700 | 0.0142        |
| 212.7222 | 5750 | 0.0191        |
| 214.6111 | 5800 | 0.0167        |
| 216.5    | 5850 | 0.013         |
| 218.3889 | 5900 | 0.0154        |
| 220.2778 | 5950 | 0.0135        |
| 222.1667 | 6000 | 0.0139        |
| 224.0556 | 6050 | 0.0203        |
| 225.6944 | 6100 | 0.0169        |
| 227.5833 | 6150 | 0.0146        |
| 229.4722 | 6200 | 0.0206        |
| 231.3611 | 6250 | 0.0149        |
| 233.25   | 6300 | 0.014         |
| 235.1389 | 6350 | 0.0174        |
| 237.0278 | 6400 | 0.0191        |
| 238.6667 | 6450 | 0.0137        |
| 240.5556 | 6500 | 0.0125        |
| 242.4444 | 6550 | 0.0081        |
| 244.3333 | 6600 | 0.0145        |
| 246.2222 | 6650 | 0.0116        |
| 248.1111 | 6700 | 0.0154        |
| 249.75   | 6750 | 0.0179        |

</details>

### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1

## 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",
}
```

#### CustomBatchAllTripletLoss
```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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