---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:72
- loss:BatchAllTripletLoss
base_model: cl-nagoya/sup-simcse-ja-base
widget:
- source_sentence: 打放し型枠（B種）
  sentences:
  - 埋込み（B種）（手間）
  - 埋込み（C種）（手間）
  - 盛土A種
- source_sentence: 埋込み[B種]
  sentences:
  - 打放し型枠（A種）
  - 盛土（C種）（手間）
  - 埋戻し[C種]
- source_sentence: 盛土[C種]
  sentences:
  - 埋込み[C種]
  - 盛土（A種）
  - 盛土[A種]
- source_sentence: 埋戻し[A種]
  sentences:
  - 打放し型枠C種
  - 打放し型枠（C種）（損料・手間）
  - 盛土[B種]
- source_sentence: 埋込み（B種）（損料・手間）
  sentences:
  - 埋戻し（A種）（損料）
  - 埋戻し（C種）（損料・手間）
  - 埋戻し（B種）（手間）
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# 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-v0_9_11")
# Run inference
sentences = [
    '埋込み（B種）（損料・手間）',
    '埋戻し（A種）（損料）',
    '埋戻し（B種）（手間）',
]
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: 72 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 72 samples:
  |         | sentence                                                                           | label                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
  |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | type    | string                                                                             | int                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
  | details | <ul><li>min: 11 tokens</li><li>mean: 16.21 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>0: ~0.50%</li><li>1: ~0.50%</li><li>2: ~0.50%</li><li>3: ~0.50%</li><li>4: ~0.50%</li><li>5: ~0.50%</li><li>6: ~0.50%</li><li>7: ~0.50%</li><li>8: ~0.50%</li><li>9: ~0.50%</li><li>10: ~0.50%</li><li>11: ~0.50%</li><li>12: ~0.50%</li><li>13: ~0.50%</li><li>14: ~0.50%</li><li>15: ~0.50%</li><li>16: ~0.50%</li><li>17: ~0.50%</li><li>18: ~0.50%</li><li>19: ~0.50%</li><li>20: ~0.50%</li><li>21: ~0.50%</li><li>22: ~0.50%</li><li>23: ~0.50%</li><li>24: ~0.50%</li><li>25: ~0.50%</li><li>26: ~0.50%</li><li>27: ~0.50%</li><li>28: ~0.50%</li><li>29: ~0.50%</li><li>30: ~0.50%</li><li>31: ~0.50%</li><li>32: ~0.50%</li><li>33: ~0.50%</li><li>34: ~0.50%</li><li>35: ~0.50%</li><li>36: ~0.50%</li><li>37: ~0.50%</li><li>38: ~0.50%</li><li>39: ~0.50%</li><li>40: ~0.50%</li><li>41: ~0.50%</li><li>42: ~0.50%</li><li>43: ~0.50%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.50%</li><li>47: ~0.50%</li><li>48: ~0.50%</li><li>49: ~0.50%</li><li>50: ~0.50%</li><li>51: ~0.50%</li><li>52: ~0.50%</li><li>53: ~0.50%</li><li>54: ~0.50%</li><li>55: ~0.50%</li><li>56: ~0.50%</li><li>57: ~0.80%</li><li>58: ~0.50%</li><li>59: ~0.50%</li><li>60: ~0.50%</li><li>61: ~0.50%</li><li>62: ~0.50%</li><li>63: ~0.50%</li><li>64: ~0.50%</li><li>65: ~0.50%</li><li>66: ~0.50%</li><li>67: ~0.50%</li><li>68: ~0.50%</li><li>69: ~0.50%</li><li>70: ~0.50%</li><li>71: ~0.50%</li><li>72: ~0.50%</li><li>73: ~0.50%</li><li>74: ~0.50%</li><li>75: ~0.50%</li><li>76: ~0.50%</li><li>77: ~0.50%</li><li>78: ~0.50%</li><li>79: ~0.50%</li><li>80: ~0.50%</li><li>81: ~0.50%</li><li>82: ~0.50%</li><li>83: ~0.50%</li><li>84: ~0.50%</li><li>85: ~0.50%</li><li>86: ~0.50%</li><li>87: ~0.50%</li><li>88: ~0.60%</li><li>89: ~0.50%</li><li>90: ~0.50%</li><li>91: ~0.50%</li><li>92: ~0.50%</li><li>93: ~0.50%</li><li>94: ~0.50%</li><li>95: ~1.20%</li><li>96: ~1.70%</li><li>97: ~3.90%</li><li>98: ~0.50%</li><li>99: ~0.50%</li><li>100: ~0.50%</li><li>101: ~0.60%</li><li>102: ~0.50%</li><li>103: ~0.50%</li><li>104: ~0.50%</li><li>105: ~0.50%</li><li>106: ~0.50%</li><li>107: ~1.20%</li><li>108: ~0.50%</li><li>109: ~0.50%</li><li>110: ~0.50%</li><li>111: ~0.50%</li><li>112: ~0.50%</li><li>113: ~0.50%</li><li>114: ~0.50%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.50%</li><li>119: ~0.50%</li><li>120: ~0.50%</li><li>121: ~0.50%</li><li>122: ~0.50%</li><li>123: ~0.50%</li><li>124: ~0.50%</li><li>125: ~0.50%</li><li>126: ~0.50%</li><li>127: ~0.50%</li><li>128: ~0.50%</li><li>129: ~0.50%</li><li>130: ~0.50%</li><li>131: ~0.50%</li><li>132: ~0.50%</li><li>133: ~0.50%</li><li>134: ~0.50%</li><li>135: ~0.50%</li><li>136: ~0.50%</li><li>137: ~0.50%</li><li>138: ~0.50%</li><li>139: ~0.50%</li><li>140: ~0.50%</li><li>141: ~0.50%</li><li>142: ~0.50%</li><li>143: ~0.50%</li><li>144: ~0.50%</li><li>145: ~0.50%</li><li>146: ~0.70%</li><li>147: ~0.50%</li><li>148: ~3.10%</li><li>149: ~0.50%</li><li>150: ~2.30%</li><li>151: ~0.50%</li><li>152: ~0.50%</li><li>153: ~0.50%</li><li>154: ~0.50%</li><li>155: ~0.50%</li><li>156: ~0.50%</li><li>157: ~0.50%</li><li>158: ~0.50%</li><li>159: ~0.50%</li><li>160: ~0.50%</li><li>161: ~0.50%</li><li>162: ~0.50%</li><li>163: ~0.50%</li><li>164: ~0.50%</li><li>165: ~0.50%</li><li>166: ~0.50%</li><li>167: ~0.50%</li><li>168: ~0.50%</li><li>169: ~0.50%</li><li>170: ~0.50%</li><li>171: ~0.50%</li><li>172: ~0.50%</li><li>173: ~0.50%</li><li>174: ~0.50%</li><li>175: ~0.50%</li><li>176: ~0.50%</li><li>177: ~0.10%</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>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)

### 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`: 0.1
- `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`: 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`: 0.1
- `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}
- `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
- `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 |
|:--------:|:----:|:-------------:|
| 10.0     | 10   | 1.6508        |
| 20.0     | 20   | 1.2554        |
| 30.0     | 30   | 0.8495        |
| 40.0     | 40   | 0.7182        |
| 50.0     | 50   | 0.6614        |
| 60.0     | 60   | 0.575         |
| 70.0     | 70   | 0.5027        |
| 80.0     | 80   | 0.32          |
| 90.0     | 90   | 0.1543        |
| 100.0    | 100  | 0.0102        |
| 110.0    | 110  | 0.012         |
| 120.0    | 120  | 0.1164        |
| 130.0    | 130  | 0.0           |
| 140.0    | 140  | 0.0           |
| 150.0    | 150  | 0.0           |
| 160.0    | 160  | 0.0157        |
| 170.0    | 170  | 0.0794        |
| 180.0    | 180  | 0.0           |
| 190.0    | 190  | 0.0           |
| 200.0    | 200  | 0.0141        |
| 210.0    | 210  | 0.0           |
| 220.0    | 220  | 0.0           |
| 230.0    | 230  | 0.1115        |
| 240.0    | 240  | 0.0           |
| 250.0    | 250  | 0.0           |
| 260.0    | 260  | 0.0           |
| 270.0    | 270  | 0.0           |
| 280.0    | 280  | 0.0           |
| 290.0    | 290  | 0.0           |
| 300.0    | 300  | 0.0           |
| 310.0    | 310  | 0.0           |
| 320.0    | 320  | 0.0           |
| 330.0    | 330  | 0.0           |
| 340.0    | 340  | 0.0           |
| 350.0    | 350  | 0.0           |
| 360.0    | 360  | 0.0197        |
| 370.0    | 370  | 0.0649        |
| 380.0    | 380  | 0.0           |
| 390.0    | 390  | 0.0           |
| 400.0    | 400  | 0.0           |
| 410.0    | 410  | 0.0           |
| 420.0    | 420  | 0.0           |
| 430.0    | 430  | 0.0           |
| 440.0    | 440  | 0.0           |
| 450.0    | 450  | 0.0           |
| 460.0    | 460  | 0.0           |
| 470.0    | 470  | 0.0           |
| 480.0    | 480  | 0.0           |
| 490.0    | 490  | 0.0           |
| 500.0    | 500  | 0.0           |
| 3.1842   | 100  | 0.6748        |
| 6.3684   | 200  | 0.5883        |
| 9.5526   | 300  | 0.5815        |
| 12.7368  | 400  | 0.5338        |
| 16.1053  | 500  | 0.5498        |
| 19.2895  | 600  | 0.5359        |
| 22.4737  | 700  | 0.5359        |
| 25.6579  | 800  | 0.4893        |
| 29.0263  | 900  | 0.4665        |
| 32.2105  | 1000 | 0.4205        |
| 35.3947  | 1100 | 0.4383        |
| 38.5789  | 1200 | 0.4552        |
| 41.7632  | 1300 | 0.4003        |
| 45.1316  | 1400 | 0.3816        |
| 48.3158  | 1500 | 0.3744        |
| 51.5     | 1600 | 0.3504        |
| 54.6842  | 1700 | 0.359         |
| 58.0526  | 1800 | 0.3019        |
| 61.2368  | 1900 | 0.3109        |
| 64.4211  | 2000 | 0.3151        |
| 67.6053  | 2100 | 0.3292        |
| 70.7895  | 2200 | 0.2813        |
| 74.1579  | 2300 | 0.2697        |
| 77.3421  | 2400 | 0.1975        |
| 80.5263  | 2500 | 0.2492        |
| 83.7105  | 2600 | 0.2608        |
| 87.0789  | 2700 | 0.2401        |
| 90.2632  | 2800 | 0.2265        |
| 93.4474  | 2900 | 0.2032        |
| 96.6316  | 3000 | 0.2368        |
| 99.8158  | 3100 | 0.2066        |
| 103.1842 | 3200 | 0.1558        |
| 106.3684 | 3300 | 0.2029        |
| 109.5526 | 3400 | 0.244         |
| 112.7368 | 3500 | 0.1894        |
| 116.1053 | 3600 | 0.193         |
| 119.2895 | 3700 | 0.1769        |
| 122.4737 | 3800 | 0.1821        |
| 125.6579 | 3900 | 0.0912        |
| 129.0263 | 4000 | 0.1834        |
| 132.2105 | 4100 | 0.1391        |
| 135.3947 | 4200 | 0.1718        |
| 138.5789 | 4300 | 0.1585        |
| 141.7632 | 4400 | 0.1829        |
| 145.1316 | 4500 | 0.1246        |
| 148.3158 | 4600 | 0.1327        |
| 151.5    | 4700 | 0.1396        |
| 154.6842 | 4800 | 0.1028        |
| 158.0526 | 4900 | 0.0907        |
| 161.2368 | 5000 | 0.1179        |
| 164.4211 | 5100 | 0.1496        |
| 167.6053 | 5200 | 0.1156        |
| 170.7895 | 5300 | 0.1148        |
| 174.1579 | 5400 | 0.1275        |
| 177.3421 | 5500 | 0.1354        |
| 180.5263 | 5600 | 0.1334        |
| 183.7105 | 5700 | 0.0874        |
| 187.0789 | 5800 | 0.0922        |
| 190.2632 | 5900 | 0.1109        |
| 193.4474 | 6000 | 0.0708        |
| 196.6316 | 6100 | 0.0943        |
| 199.8158 | 6200 | 0.1164        |
| 203.1842 | 6300 | 0.0785        |
| 206.3684 | 6400 | 0.0853        |
| 209.5526 | 6500 | 0.0674        |
| 212.7368 | 6600 | 0.1009        |
| 216.1053 | 6700 | 0.0846        |
| 219.2895 | 6800 | 0.078         |
| 222.4737 | 6900 | 0.0958        |
| 225.6579 | 7000 | 0.0811        |
| 229.0263 | 7100 | 0.0452        |
| 232.2105 | 7200 | 0.0705        |
| 235.3947 | 7300 | 0.0664        |
| 238.5789 | 7400 | 0.0501        |
| 241.7632 | 7500 | 0.0696        |
| 245.1316 | 7600 | 0.0736        |
| 248.3158 | 7700 | 0.08          |

</details>

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.50.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.5.0
- 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",
}
```

#### 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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