sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:8788 loss:BatchAllTripletLoss arxiv:1908.10084 arxiv:1703.07737 base_model:cl-nagoya/sup-simcse-ja-base base_model:finetune:cl-nagoya/sup-simcse-ja-base autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: cl-nagoya/sup-simcse-ja-base
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:8788
- loss:BatchAllTripletLoss
widget:
- source_sentence: 科目：ユニット及びその他。名称：ピクチャーレールA。
  sentences:
  - 科目：ユニット及びその他。名称：床ゴムチップ舗装。
  - 科目：ユニット及びその他。名称：講堂スピーカー戸。
  - 科目：ユニット及びその他。名称：C7三槽シンク。
- source_sentence: 科目：ユニット及びその他。名称：A-#小児プレイルームアート。
  sentences:
  - 科目：ユニット及びその他。名称：F-#階ひまわり学級職員室ミニキッチン。
  - 科目：ユニット及びその他。名称：連絡通路梁用バトントラス。
  - 科目：ユニット及びその他。名称：体育館サブバレーボールコートライン。
- source_sentence: 科目：ユニット及びその他。名称：厨房カウンター。
  sentences:
  - 科目：コンクリート。名称：地上部暑中コンクリート。
  - 科目：コンクリート。名称：免震EXP.J用充填コンクリート。
  - 科目：コンクリート。名称：基礎コンクリート。
- source_sentence: 科目：ユニット及びその他。名称：1F電話コーナーカウンター。
  sentences:
  - 科目：ユニット及びその他。名称：1・2階男子・女子更衣室カーテンレール。
  - 科目：コンクリート。名称：鉄筋コンクリート（免震下部）。
  - 科目：タイル。名称：EXP.J上床磁器質タイルA。
- source_sentence: 科目：ユニット及びその他。名称：4F透析室カウンター。
  sentences:
  - 科目：ユニット及びその他。名称：2F初療1、2カウンター。
  - 科目：ユニット及びその他。名称：5Fファミリールームカウンター。
  - 科目：ユニット及びその他。名称：9Fスタッフステーション1カウンター。
---

# SentenceTransformer based on cl-nagoya/sup-simcse-ja-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base). 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:** [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base) <!-- at revision d7315d93baf2c20fffa2b6845330049963509f79 -->
- **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-for-standard-name-v1_1")
# Run inference
sentences = [
    '科目：ユニット及びその他。名称：4F透析室カウンター。',
    '科目：ユニット及びその他。名称：2F初療1、2カウンター。',
    '科目：ユニット及びその他。名称：9Fスタッフステーション1カウンター。',
]
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: 8,788 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
  |         | sentence                                                                           | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------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  | type    | string                                                                             | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
  | details | <ul><li>min: 13 tokens</li><li>mean: 23.19 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>0: ~0.20%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.20%</li><li>5: ~0.20%</li><li>6: ~0.20%</li><li>7: ~0.20%</li><li>8: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.20%</li><li>11: ~0.20%</li><li>12: ~0.20%</li><li>13: ~0.20%</li><li>14: ~0.20%</li><li>15: ~0.20%</li><li>16: ~0.40%</li><li>17: ~0.20%</li><li>18: ~0.20%</li><li>19: ~0.20%</li><li>20: ~0.20%</li><li>21: ~0.20%</li><li>22: ~0.20%</li><li>23: ~0.20%</li><li>24: ~0.20%</li><li>25: ~0.20%</li><li>26: ~0.20%</li><li>27: ~0.20%</li><li>28: ~0.20%</li><li>29: ~0.20%</li><li>30: ~0.20%</li><li>31: ~0.20%</li><li>32: ~0.20%</li><li>33: ~0.20%</li><li>34: ~0.20%</li><li>35: ~0.20%</li><li>36: ~0.20%</li><li>37: ~0.20%</li><li>38: ~0.20%</li><li>39: ~0.20%</li><li>40: ~0.20%</li><li>41: ~0.20%</li><li>42: ~0.60%</li><li>43: ~0.70%</li><li>44: ~0.20%</li><li>45: ~0.30%</li><li>46: ~0.20%</li><li>47: ~0.20%</li><li>48: ~0.30%</li><li>49: ~0.20%</li><li>50: ~0.20%</li><li>51: ~0.20%</li><li>52: ~0.20%</li><li>53: ~0.30%</li><li>54: ~0.40%</li><li>55: ~0.30%</li><li>56: ~0.20%</li><li>57: ~0.20%</li><li>58: ~0.20%</li><li>59: ~0.20%</li><li>60: ~0.20%</li><li>61: ~0.30%</li><li>62: ~0.20%</li><li>63: ~0.20%</li><li>64: ~0.20%</li><li>65: ~0.20%</li><li>66: ~0.40%</li><li>67: ~0.40%</li><li>68: ~0.20%</li><li>69: ~0.60%</li><li>70: ~0.20%</li><li>71: ~0.20%</li><li>72: ~0.20%</li><li>73: ~0.20%</li><li>74: ~0.20%</li><li>75: ~0.30%</li><li>76: ~0.20%</li><li>77: ~0.40%</li><li>78: ~0.20%</li><li>79: ~0.20%</li><li>80: ~0.50%</li><li>81: ~0.30%</li><li>82: ~0.60%</li><li>83: ~0.20%</li><li>84: ~0.30%</li><li>85: ~0.20%</li><li>86: ~0.20%</li><li>87: ~0.20%</li><li>88: ~0.20%</li><li>89: ~1.10%</li><li>90: ~1.70%</li><li>91: ~2.20%</li><li>92: ~0.50%</li><li>93: ~0.20%</li><li>94: ~0.20%</li><li>95: ~1.60%</li><li>96: ~0.20%</li><li>97: ~0.20%</li><li>98: ~0.20%</li><li>99: ~0.20%</li><li>100: ~0.30%</li><li>101: ~1.70%</li><li>102: ~0.20%</li><li>103: ~0.20%</li><li>104: ~0.40%</li><li>105: ~0.40%</li><li>106: ~0.20%</li><li>107: ~0.20%</li><li>108: ~0.20%</li><li>109: ~1.10%</li><li>110: ~0.20%</li><li>111: ~0.40%</li><li>112: ~0.50%</li><li>113: ~0.20%</li><li>114: ~0.20%</li><li>115: ~0.20%</li><li>116: ~0.20%</li><li>117: ~0.50%</li><li>118: ~0.20%</li><li>119: ~0.20%</li><li>120: ~0.20%</li><li>121: ~0.20%</li><li>122: ~0.20%</li><li>123: ~0.20%</li><li>124: ~0.30%</li><li>125: ~0.20%</li><li>126: ~0.20%</li><li>127: ~0.20%</li><li>128: ~0.40%</li><li>129: ~0.20%</li><li>130: ~0.20%</li><li>131: ~0.20%</li><li>132: ~0.20%</li><li>133: ~0.20%</li><li>134: ~0.20%</li><li>135: ~0.20%</li><li>136: ~0.20%</li><li>137: ~0.20%</li><li>138: ~0.30%</li><li>139: ~0.20%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.20%</li><li>143: ~0.20%</li><li>144: ~0.20%</li><li>145: ~0.20%</li><li>146: ~0.20%</li><li>147: ~0.20%</li><li>148: ~0.20%</li><li>149: ~0.20%</li><li>150: ~0.20%</li><li>151: ~0.20%</li><li>152: ~0.20%</li><li>153: ~0.20%</li><li>154: ~0.20%</li><li>155: ~0.20%</li><li>156: ~0.60%</li><li>157: ~0.20%</li><li>158: ~1.50%</li><li>159: ~0.20%</li><li>160: ~0.20%</li><li>161: ~0.20%</li><li>162: ~0.20%</li><li>163: ~0.50%</li><li>164: ~0.20%</li><li>165: ~0.20%</li><li>166: ~0.20%</li><li>167: ~0.20%</li><li>168: ~0.20%</li><li>169: ~0.30%</li><li>170: ~0.30%</li><li>171: ~0.20%</li><li>172: ~0.20%</li><li>173: ~1.30%</li><li>174: ~0.20%</li><li>175: ~0.20%</li><li>176: ~0.20%</li><li>177: ~0.20%</li><li>178: ~0.60%</li><li>179: ~0.20%</li><li>180: ~0.20%</li><li>181: ~0.20%</li><li>182: ~0.20%</li><li>183: ~0.20%</li><li>184: ~0.20%</li><li>185: ~0.30%</li><li>186: ~0.20%</li><li>187: ~0.20%</li><li>188: ~0.30%</li><li>189: ~0.20%</li><li>190: ~0.90%</li><li>191: ~0.30%</li><li>192: ~0.30%</li><li>193: ~0.20%</li><li>194: ~0.30%</li><li>195: ~0.20%</li><li>196: ~0.80%</li><li>197: ~0.20%</li><li>198: ~0.20%</li><li>199: ~0.30%</li><li>200: ~0.20%</li><li>201: ~0.20%</li><li>202: ~0.20%</li><li>203: ~0.20%</li><li>204: ~0.20%</li><li>205: ~1.20%</li><li>206: ~0.40%</li><li>207: ~0.20%</li><li>208: ~0.20%</li><li>209: ~0.20%</li><li>210: ~0.20%</li><li>211: ~0.30%</li><li>212: ~0.20%</li><li>213: ~0.80%</li><li>214: ~0.30%</li><li>215: ~0.20%</li><li>216: ~1.10%</li><li>217: ~0.30%</li><li>218: ~0.20%</li><li>219: ~0.20%</li><li>220: ~0.20%</li><li>221: ~0.20%</li><li>222: ~0.20%</li><li>223: ~0.20%</li><li>224: ~0.20%</li><li>225: ~0.30%</li><li>226: ~0.20%</li><li>227: ~0.90%</li><li>228: ~4.70%</li><li>229: ~0.20%</li><li>230: ~0.20%</li><li>231: ~0.20%</li><li>232: ~0.70%</li><li>233: ~0.20%</li><li>234: ~0.80%</li><li>235: ~0.20%</li><li>236: ~0.40%</li><li>237: ~0.30%</li><li>238: ~0.40%</li><li>239: ~0.20%</li><li>240: ~0.30%</li><li>241: ~0.50%</li><li>242: ~0.30%</li><li>243: ~0.20%</li><li>244: ~0.20%</li><li>245: ~0.30%</li><li>246: ~0.30%</li><li>247: ~0.30%</li><li>248: ~0.60%</li><li>249: ~0.20%</li><li>250: ~0.20%</li><li>251: ~0.20%</li><li>252: ~0.30%</li><li>253: ~0.30%</li><li>254: ~1.90%</li><li>255: ~0.20%</li><li>256: ~0.20%</li><li>257: ~0.20%</li><li>258: ~0.20%</li><li>259: ~0.20%</li><li>260: ~0.50%</li><li>261: ~0.20%</li><li>262: ~0.30%</li><li>263: ~0.20%</li><li>264: ~0.20%</li><li>265: ~1.00%</li><li>266: ~0.20%</li><li>267: ~0.20%</li><li>268: ~0.20%</li><li>269: ~0.40%</li><li>270: ~0.20%</li><li>271: ~0.20%</li><li>272: ~0.20%</li><li>273: ~0.20%</li><li>274: ~0.20%</li><li>275: ~0.20%</li><li>276: ~0.20%</li><li>277: ~3.70%</li><li>278: ~0.20%</li><li>279: ~0.40%</li><li>280: ~0.20%</li><li>281: ~0.20%</li><li>282: ~0.90%</li><li>283: ~0.40%</li><li>284: ~0.20%</li><li>285: ~2.30%</li><li>286: ~0.30%</li><li>287: ~0.20%</li><li>288: ~0.30%</li><li>289: ~0.60%</li></ul> |
* Samples:
  | sentence                                | label          |
  |:----------------------------------------|:---------------|
  | <code>科目：コンクリート。名称：免震基礎天端グラウト注入。</code> | <code>0</code> |
  | <code>科目：コンクリート。名称：免震基礎天端グラウト注入。</code> | <code>0</code> |
  | <code>科目：コンクリート。名称：コンクリートポンプ圧送。</code>  | <code>1</code> |
* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)

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

- `per_device_train_batch_size`: 256
- `per_device_eval_batch_size`: 256
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 250
- `warmup_ratio`: 0.1
- `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`: 256
- `per_device_eval_batch_size`: 256
- `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`: 0.1
- `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`: 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
- `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
- `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 |
|:--------:|:----:|:-------------:|
| 0.5714   | 20   | 0.787         |
| 1.2      | 40   | 0.7827        |
| 1.7714   | 60   | 0.7361        |
| 2.4      | 80   | 0.6798        |
| 3.0286   | 100  | 0.6569        |
| 3.6      | 120  | 0.6669        |
| 4.2286   | 140  | 0.6163        |
| 4.8      | 160  | 0.6277        |
| 5.4286   | 180  | 0.6449        |
| 6.0571   | 200  | 0.6135        |
| 6.6286   | 220  | 0.6445        |
| 7.2571   | 240  | 0.6572        |
| 7.8286   | 260  | 0.6268        |
| 8.4571   | 280  | 0.6034        |
| 9.0857   | 300  | 0.5598        |
| 9.6571   | 320  | 0.5801        |
| 10.2857  | 340  | 0.5471        |
| 10.8571  | 360  | 0.6579        |
| 11.4857  | 380  | 0.6059        |
| 12.1143  | 400  | 0.5715        |
| 12.6857  | 420  | 0.5986        |
| 13.3143  | 440  | 0.5601        |
| 13.8857  | 460  | 0.5547        |
| 14.5143  | 480  | 0.5642        |
| 15.1429  | 500  | 0.697         |
| 15.7143  | 520  | 0.5688        |
| 16.3429  | 540  | 0.5736        |
| 16.9143  | 560  | 0.5088        |
| 17.5429  | 580  | 0.5677        |
| 18.1714  | 600  | 0.6028        |
| 18.7429  | 620  | 0.5674        |
| 19.3714  | 640  | 0.5665        |
| 19.9429  | 660  | 0.6289        |
| 20.5714  | 680  | 0.5456        |
| 21.2     | 700  | 0.4944        |
| 21.7714  | 720  | 0.5712        |
| 22.4     | 740  | 0.6106        |
| 23.0286  | 760  | 0.5209        |
| 23.6     | 780  | 0.5236        |
| 24.2286  | 800  | 0.6091        |
| 24.8     | 820  | 0.6678        |
| 25.4286  | 840  | 0.4693        |
| 26.0571  | 860  | 0.4582        |
| 26.6286  | 880  | 0.5627        |
| 27.2571  | 900  | 0.5525        |
| 27.8286  | 920  | 0.503         |
| 28.4571  | 940  | 0.4801        |
| 29.0857  | 960  | 0.5039        |
| 29.6571  | 980  | 0.5049        |
| 30.2857  | 1000 | 0.595         |
| 30.8571  | 1020 | 0.4733        |
| 31.4857  | 1040 | 0.5804        |
| 32.1143  | 1060 | 0.4101        |
| 32.6857  | 1080 | 0.4311        |
| 33.3143  | 1100 | 0.4746        |
| 33.8857  | 1120 | 0.4964        |
| 34.5143  | 1140 | 0.4436        |
| 35.1429  | 1160 | 0.6351        |
| 35.7143  | 1180 | 0.5267        |
| 36.3429  | 1200 | 0.4685        |
| 36.9143  | 1220 | 0.4201        |
| 37.5429  | 1240 | 0.4256        |
| 38.1714  | 1260 | 0.5543        |
| 38.7429  | 1280 | 0.5176        |
| 39.3714  | 1300 | 0.4328        |
| 39.9429  | 1320 | 0.4746        |
| 40.5714  | 1340 | 0.4768        |
| 41.2     | 1360 | 0.4663        |
| 41.7714  | 1380 | 0.4729        |
| 42.4     | 1400 | 0.4141        |
| 43.0286  | 1420 | 0.3195        |
| 43.6     | 1440 | 0.3789        |
| 44.2286  | 1460 | 0.4032        |
| 44.8     | 1480 | 0.443         |
| 45.4286  | 1500 | 0.4116        |
| 46.0571  | 1520 | 0.4951        |
| 46.6286  | 1540 | 0.3845        |
| 47.2571  | 1560 | 0.3461        |
| 47.8286  | 1580 | 0.4754        |
| 48.4571  | 1600 | 0.5583        |
| 49.0857  | 1620 | 0.4282        |
| 49.6571  | 1640 | 0.436         |
| 50.2857  | 1660 | 0.4097        |
| 50.8571  | 1680 | 0.4642        |
| 51.4857  | 1700 | 0.3243        |
| 52.1143  | 1720 | 0.4395        |
| 52.6857  | 1740 | 0.3672        |
| 53.3143  | 1760 | 0.4781        |
| 53.8857  | 1780 | 0.5362        |
| 54.5143  | 1800 | 0.4401        |
| 55.1429  | 1820 | 0.4313        |
| 55.7143  | 1840 | 0.2751        |
| 56.3429  | 1860 | 0.331         |
| 56.9143  | 1880 | 0.4325        |
| 57.5429  | 1900 | 0.2995        |
| 58.1714  | 1920 | 0.4159        |
| 58.7429  | 1940 | 0.5603        |
| 59.3714  | 1960 | 0.4575        |
| 59.9429  | 1980 | 0.4677        |
| 60.5714  | 2000 | 0.4653        |
| 61.2     | 2020 | 0.3098        |
| 61.7714  | 2040 | 0.3188        |
| 62.4     | 2060 | 0.3769        |
| 63.0286  | 2080 | 0.2902        |
| 63.6     | 2100 | 0.4064        |
| 64.2286  | 2120 | 0.3663        |
| 64.8     | 2140 | 0.3184        |
| 65.4286  | 2160 | 0.4874        |
| 66.0571  | 2180 | 0.4094        |
| 66.6286  | 2200 | 0.4261        |
| 67.2571  | 2220 | 0.3808        |
| 67.8286  | 2240 | 0.2991        |
| 68.4571  | 2260 | 0.3242        |
| 69.0857  | 2280 | 0.2582        |
| 69.6571  | 2300 | 0.3806        |
| 70.2857  | 2320 | 0.3573        |
| 70.8571  | 2340 | 0.3183        |
| 71.4857  | 2360 | 0.4043        |
| 72.1143  | 2380 | 0.4266        |
| 72.6857  | 2400 | 0.5612        |
| 73.3143  | 2420 | 0.3476        |
| 73.8857  | 2440 | 0.3018        |
| 74.5143  | 2460 | 0.2952        |
| 75.1429  | 2480 | 0.2633        |
| 75.7143  | 2500 | 0.3564        |
| 76.3429  | 2520 | 0.2283        |
| 76.9143  | 2540 | 0.3615        |
| 77.5429  | 2560 | 0.2174        |
| 78.1714  | 2580 | 0.3049        |
| 78.7429  | 2600 | 0.2838        |
| 79.3714  | 2620 | 0.3191        |
| 79.9429  | 2640 | 0.2529        |
| 80.5714  | 2660 | 0.3192        |
| 81.2     | 2680 | 0.5119        |
| 81.7714  | 2700 | 0.2459        |
| 82.4     | 2720 | 0.4136        |
| 83.0286  | 2740 | 0.3266        |
| 83.6     | 2760 | 0.2863        |
| 84.2286  | 2780 | 0.3563        |
| 84.8     | 2800 | 0.2605        |
| 85.4286  | 2820 | 0.254         |
| 86.0571  | 2840 | 0.2252        |
| 86.6286  | 2860 | 0.3191        |
| 87.2571  | 2880 | 0.3074        |
| 87.8286  | 2900 | 0.274         |
| 88.4571  | 2920 | 0.3864        |
| 89.0857  | 2940 | 0.3206        |
| 89.6571  | 2960 | 0.2752        |
| 90.2857  | 2980 | 0.2033        |
| 90.8571  | 3000 | 0.3979        |
| 91.4857  | 3020 | 0.4327        |
| 92.1143  | 3040 | 0.1999        |
| 92.6857  | 3060 | 0.3939        |
| 93.3143  | 3080 | 0.2733        |
| 93.8857  | 3100 | 0.4334        |
| 94.5143  | 3120 | 0.3726        |
| 95.1429  | 3140 | 0.2567        |
| 95.7143  | 3160 | 0.258         |
| 96.3429  | 3180 | 0.1805        |
| 96.9143  | 3200 | 0.3244        |
| 97.5429  | 3220 | 0.2038        |
| 98.1714  | 3240 | 0.2689        |
| 98.7429  | 3260 | 0.433         |
| 99.3714  | 3280 | 0.1587        |
| 99.9429  | 3300 | 0.3088        |
| 100.5714 | 3320 | 0.3049        |
| 101.2    | 3340 | 0.335         |
| 101.7714 | 3360 | 0.2688        |
| 102.4    | 3380 | 0.359         |
| 103.0286 | 3400 | 0.2512        |
| 103.6    | 3420 | 0.2818        |
| 104.2286 | 3440 | 0.3606        |
| 104.8    | 3460 | 0.3254        |
| 105.4286 | 3480 | 0.2487        |
| 106.0571 | 3500 | 0.2184        |
| 106.6286 | 3520 | 0.2897        |
| 107.2571 | 3540 | 0.2849        |
| 107.8286 | 3560 | 0.362         |
| 108.4571 | 3580 | 0.2418        |
| 109.0857 | 3600 | 0.1498        |
| 109.6571 | 3620 | 0.2566        |
| 110.2857 | 3640 | 0.1181        |
| 110.8571 | 3660 | 0.3675        |
| 111.4857 | 3680 | 0.2722        |
| 112.1143 | 3700 | 0.3779        |
| 112.6857 | 3720 | 0.3882        |
| 113.3143 | 3740 | 0.1941        |
| 113.8857 | 3760 | 0.2281        |
| 114.5143 | 3780 | 0.2079        |
| 115.1429 | 3800 | 0.3443        |
| 115.7143 | 3820 | 0.2763        |
| 116.3429 | 3840 | 0.2331        |
| 116.9143 | 3860 | 0.2093        |
| 117.5429 | 3880 | 0.2439        |
| 118.1714 | 3900 | 0.1312        |
| 118.7429 | 3920 | 0.1098        |
| 119.3714 | 3940 | 0.2295        |
| 119.9429 | 3960 | 0.2501        |
| 120.5714 | 3980 | 0.3522        |
| 121.2    | 4000 | 0.3293        |
| 121.7714 | 4020 | 0.1698        |
| 122.4    | 4040 | 0.3992        |
| 123.0286 | 4060 | 0.1931        |
| 123.6    | 4080 | 0.1755        |
| 124.2286 | 4100 | 0.3408        |
| 124.8    | 4120 | 0.2337        |
| 125.4286 | 4140 | 0.2121        |
| 126.0571 | 4160 | 0.1628        |
| 126.6286 | 4180 | 0.2455        |
| 127.2571 | 4200 | 0.3342        |
| 127.8286 | 4220 | 0.1725        |
| 128.4571 | 4240 | 0.3714        |
| 129.0857 | 4260 | 0.2775        |
| 129.6571 | 4280 | 0.1764        |
| 130.2857 | 4300 | 0.1863        |
| 130.8571 | 4320 | 0.276         |
| 131.4857 | 4340 | 0.2006        |
| 132.1143 | 4360 | 0.2099        |
| 132.6857 | 4380 | 0.2397        |
| 133.3143 | 4400 | 0.223         |
| 133.8857 | 4420 | 0.1321        |
| 134.5143 | 4440 | 0.2499        |
| 135.1429 | 4460 | 0.2107        |
| 135.7143 | 4480 | 0.2374        |
| 136.3429 | 4500 | 0.2589        |
| 136.9143 | 4520 | 0.2382        |
| 137.5429 | 4540 | 0.1058        |
| 138.1714 | 4560 | 0.2519        |
| 138.7429 | 4580 | 0.23          |
| 139.3714 | 4600 | 0.2031        |
| 139.9429 | 4620 | 0.2424        |
| 140.5714 | 4640 | 0.1312        |
| 141.2    | 4660 | 0.1787        |
| 141.7714 | 4680 | 0.2445        |
| 142.4    | 4700 | 0.1948        |
| 143.0286 | 4720 | 0.2601        |
| 143.6    | 4740 | 0.1906        |
| 144.2286 | 4760 | 0.35          |
| 144.8    | 4780 | 0.1674        |
| 145.4286 | 4800 | 0.2339        |
| 146.0571 | 4820 | 0.2151        |
| 146.6286 | 4840 | 0.1986        |
| 147.2571 | 4860 | 0.1608        |
| 147.8286 | 4880 | 0.2729        |
| 148.4571 | 4900 | 0.1555        |
| 149.0857 | 4920 | 0.1536        |
| 149.6571 | 4940 | 0.1245        |
| 150.2857 | 4960 | 0.2635        |
| 150.8571 | 4980 | 0.1628        |
| 151.4857 | 5000 | 0.1869        |
| 152.1143 | 5020 | 0.2142        |
| 152.6857 | 5040 | 0.1867        |
| 153.3143 | 5060 | 0.2361        |
| 153.8857 | 5080 | 0.1811        |
| 154.5143 | 5100 | 0.4071        |
| 155.1429 | 5120 | 0.2499        |
| 155.7143 | 5140 | 0.2398        |
| 156.3429 | 5160 | 0.1486        |
| 156.9143 | 5180 | 0.1683        |
| 157.5429 | 5200 | 0.1434        |
| 158.1714 | 5220 | 0.1731        |
| 158.7429 | 5240 | 0.1674        |
| 159.3714 | 5260 | 0.1085        |
| 159.9429 | 5280 | 0.2573        |
| 160.5714 | 5300 | 0.1937        |
| 161.2    | 5320 | 0.0806        |
| 161.7714 | 5340 | 0.1411        |
| 162.4    | 5360 | 0.1603        |
| 163.0286 | 5380 | 0.1787        |
| 163.6    | 5400 | 0.2099        |
| 164.2286 | 5420 | 0.2676        |
| 164.8    | 5440 | 0.2658        |
| 165.4286 | 5460 | 0.2632        |
| 166.0571 | 5480 | 0.1839        |
| 166.6286 | 5500 | 0.2524        |
| 167.2571 | 5520 | 0.2018        |
| 167.8286 | 5540 | 0.2955        |
| 168.4571 | 5560 | 0.209         |
| 169.0857 | 5580 | 0.1999        |
| 169.6571 | 5600 | 0.2836        |
| 170.2857 | 5620 | 0.1559        |
| 170.8571 | 5640 | 0.2746        |
| 171.4857 | 5660 | 0.1939        |
| 172.1143 | 5680 | 0.1561        |
| 172.6857 | 5700 | 0.0935        |
| 173.3143 | 5720 | 0.1927        |
| 173.8857 | 5740 | 0.3022        |
| 174.5143 | 5760 | 0.2068        |
| 175.1429 | 5780 | 0.1384        |
| 175.7143 | 5800 | 0.086         |
| 176.3429 | 5820 | 0.1181        |
| 176.9143 | 5840 | 0.3145        |
| 177.5429 | 5860 | 0.0974        |
| 178.1714 | 5880 | 0.1891        |
| 178.7429 | 5900 | 0.1788        |
| 179.3714 | 5920 | 0.1954        |
| 179.9429 | 5940 | 0.1342        |
| 180.5714 | 5960 | 0.0936        |
| 181.2    | 5980 | 0.3109        |
| 181.7714 | 6000 | 0.1879        |
| 182.4    | 6020 | 0.0798        |
| 183.0286 | 6040 | 0.097         |
| 183.6    | 6060 | 0.0835        |
| 184.2286 | 6080 | 0.0931        |
| 184.8    | 6100 | 0.1377        |
| 185.4286 | 6120 | 0.1239        |
| 186.0571 | 6140 | 0.0307        |
| 186.6286 | 6160 | 0.1962        |
| 187.2571 | 6180 | 0.242         |
| 187.8286 | 6200 | 0.0886        |
| 188.4571 | 6220 | 0.2103        |
| 189.0857 | 6240 | 0.0746        |
| 189.6571 | 6260 | 0.1191        |
| 190.2857 | 6280 | 0.2356        |
| 190.8571 | 6300 | 0.2015        |
| 191.4857 | 6320 | 0.1728        |
| 192.1143 | 6340 | 0.1624        |
| 192.6857 | 6360 | 0.2528        |
| 193.3143 | 6380 | 0.0759        |
| 193.8857 | 6400 | 0.2138        |
| 194.5143 | 6420 | 0.1544        |
| 195.1429 | 6440 | 0.2444        |
| 195.7143 | 6460 | 0.1896        |
| 196.3429 | 6480 | 0.1646        |
| 196.9143 | 6500 | 0.1305        |
| 197.5429 | 6520 | 0.1379        |
| 198.1714 | 6540 | 0.1845        |
| 198.7429 | 6560 | 0.1997        |
| 199.3714 | 6580 | 0.2049        |
| 199.9429 | 6600 | 0.2891        |
| 200.5714 | 6620 | 0.1718        |
| 201.2    | 6640 | 0.1449        |
| 201.7714 | 6660 | 0.2096        |
| 202.4    | 6680 | 0.1056        |
| 203.0286 | 6700 | 0.0862        |
| 203.6    | 6720 | 0.0914        |
| 204.2286 | 6740 | 0.2433        |
| 204.8    | 6760 | 0.146         |
| 205.4286 | 6780 | 0.2099        |
| 206.0571 | 6800 | 0.0877        |
| 206.6286 | 6820 | 0.1194        |
| 207.2571 | 6840 | 0.069         |
| 207.8286 | 6860 | 0.0742        |
| 208.4571 | 6880 | 0.2773        |
| 209.0857 | 6900 | 0.1762        |
| 209.6571 | 6920 | 0.1573        |
| 210.2857 | 6940 | 0.0922        |
| 210.8571 | 6960 | 0.1366        |
| 211.4857 | 6980 | 0.0746        |
| 212.1143 | 7000 | 0.2004        |
| 212.6857 | 7020 | 0.0922        |
| 213.3143 | 7040 | 0.0662        |
| 213.8857 | 7060 | 0.1828        |
| 214.5143 | 7080 | 0.1202        |
| 215.1429 | 7100 | 0.1388        |
| 215.7143 | 7120 | 0.0638        |
| 216.3429 | 7140 | 0.2259        |
| 216.9143 | 7160 | 0.1219        |
| 217.5429 | 7180 | 0.1599        |
| 218.1714 | 7200 | 0.2424        |
| 218.7429 | 7220 | 0.149         |
| 219.3714 | 7240 | 0.272         |
| 219.9429 | 7260 | 0.1051        |
| 220.5714 | 7280 | 0.2117        |
| 221.2    | 7300 | 0.1466        |
| 221.7714 | 7320 | 0.1155        |
| 222.4    | 7340 | 0.2247        |
| 223.0286 | 7360 | 0.096         |
| 223.6    | 7380 | 0.0566        |
| 224.2286 | 7400 | 0.2404        |
| 224.8    | 7420 | 0.1684        |
| 225.4286 | 7440 | 0.0927        |
| 226.0571 | 7460 | 0.1746        |
| 226.6286 | 7480 | 0.13          |
| 227.2571 | 7500 | 0.1027        |
| 227.8286 | 7520 | 0.1359        |
| 228.4571 | 7540 | 0.0937        |
| 229.0857 | 7560 | 0.1378        |
| 229.6571 | 7580 | 0.0458        |
| 230.2857 | 7600 | 0.0766        |
| 230.8571 | 7620 | 0.0896        |
| 231.4857 | 7640 | 0.1541        |
| 232.1143 | 7660 | 0.1464        |
| 232.6857 | 7680 | 0.1427        |
| 233.3143 | 7700 | 0.2471        |
| 233.8857 | 7720 | 0.1636        |
| 234.5143 | 7740 | 0.1601        |
| 235.1429 | 7760 | 0.1583        |
| 235.7143 | 7780 | 0.1473        |
| 236.3429 | 7800 | 0.1211        |
| 236.9143 | 7820 | 0.1582        |
| 237.5429 | 7840 | 0.1083        |
| 238.1714 | 7860 | 0.2014        |
| 238.7429 | 7880 | 0.0981        |
| 239.3714 | 7900 | 0.2449        |
| 239.9429 | 7920 | 0.1142        |
| 240.5714 | 7940 | 0.1177        |
| 241.2    | 7960 | 0.1241        |
| 241.7714 | 7980 | 0.2778        |
| 242.4    | 8000 | 0.1066        |
| 243.0286 | 8020 | 0.0867        |
| 243.6    | 8040 | 0.156         |
| 244.2286 | 8060 | 0.1413        |
| 244.8    | 8080 | 0.0598        |
| 245.4286 | 8100 | 0.1206        |
| 246.0571 | 8120 | 0.1883        |
| 246.6286 | 8140 | 0.1245        |
| 247.2571 | 8160 | 0.0949        |
| 247.8286 | 8180 | 0.1096        |
| 248.4571 | 8200 | 0.1567        |
| 249.0857 | 8220 | 0.065         |
| 249.6571 | 8240 | 0.1075        |

</details>

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.48.3
- PyTorch: 2.5.1+cu124
- Accelerate: 1.3.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0

## 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",
}
```

#### BatchAllTripletLoss
```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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