---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:16547
- loss:CustomBatchAllTripletLoss
widget:
- source_sentence: 科目：ユニット及びその他。名称：突出ｻｲﾝ。
  sentences:
  - 科目：ユニット及びその他。名称：ｶｳﾝﾀｰ上ｶﾞﾗｽ台。
  - 科目：ユニット及びその他。名称：ｼｬﾜｰﾕﾆｯﾄ枠。
  - 科目：ユニット及びその他。名称：ｴﾝﾄﾗﾝｽｻｲﾝ。
- source_sentence: 科目：タイル。名称：階段踏面ﾀｲﾙ。
  sentences:
  - 科目：コンクリート。名称：基礎部ｺﾝｸﾘｰﾄ。摘要：FC42N/mm2 ｽﾗﾝﾌﾟ21高性能AE減水剤。備考：代価表    0056。
  - 科目：ユニット及びその他。名称：配膳棚。
  - 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設。
- source_sentence: 科目：ユニット及びその他。名称：ｵｰﾊﾞｰﾌﾛｰ管。
  sentences:
  - 科目：ユニット及びその他。名称：自動支払機ｻｲﾝ。
  - 科目：ユニット及びその他。名称：SPﾎﾞｯｸｽ。
  - 科目：コンクリート。名称：捨てｺﾝｸﾘｰﾄ。
- source_sentence: 科目：ユニット及びその他。名称：執務室#-#規格品ｶｳﾝﾀｰ。
  sentences:
  - 科目：ユニット及びその他。名称：床ｺﾝｸﾘｰﾄ平板ﾃﾞｯｷ。
  - 科目：ユニット及びその他。名称：#階守衛室ｶｳﾝﾀｰ。
  - 科目：ユニット及びその他。名称：ｺﾝｸﾘｰﾄ舗装。
- source_sentence: 科目：ユニット及びその他。名称：#FHCU#床室ｶｳﾝﾀｰ。
  sentences:
  - 科目：ユニット及びその他。名称：室名ｻｲﾝ。
  - 科目：ユニット及びその他。名称：#階数表示（階段室内･踊り場）。
  - 科目：ユニット及びその他。名称：Co-#ﾋﾟｸﾄｻｲﾝ。
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-v1_0_2")
# Run inference
sentences = [
    '科目：ユニット及びその他。名称：#FHCU#床室ｶｳﾝﾀｰ。',
    '科目：ユニット及びその他。名称：#階数表示（階段室内･踊り場）。',
    '科目：ユニット及びその他。名称：Co-#ﾋﾟｸﾄｻｲﾝ。',
]
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: 16,547 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: 18.77 tokens</li><li>max: 72 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: ~3.40%</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: ~0.40%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.40%</li><li>16: ~0.30%</li><li>17: ~0.30%</li><li>18: ~0.30%</li><li>19: ~0.90%</li><li>20: ~0.30%</li><li>21: ~0.30%</li><li>22: ~1.10%</li><li>23: ~0.30%</li><li>24: ~0.30%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.30%</li><li>30: ~0.30%</li><li>31: ~0.30%</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: ~0.40%</li><li>41: ~0.30%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.30%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.30%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.80%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.60%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.50%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.50%</li><li>70: ~0.60%</li><li>71: ~0.30%</li><li>72: ~0.30%</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: ~0.50%</li><li>79: ~0.30%</li><li>80: ~0.30%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.80%</li><li>84: ~0.60%</li><li>85: ~0.50%</li><li>86: ~0.30%</li><li>87: ~0.30%</li><li>88: ~16.50%</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: ~0.30%</li><li>103: ~0.30%</li><li>104: ~0.30%</li><li>105: ~0.70%</li><li>106: ~0.70%</li><li>107: ~0.30%</li><li>108: ~3.20%</li><li>109: ~0.30%</li><li>110: ~0.40%</li><li>111: ~2.30%</li><li>112: ~0.30%</li><li>113: ~0.30%</li><li>114: ~0.50%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.40%</li><li>118: ~0.30%</li><li>119: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.80%</li><li>122: ~0.30%</li><li>123: ~0.30%</li><li>124: ~0.30%</li><li>125: ~0.30%</li><li>126: ~0.30%</li><li>127: ~0.30%</li><li>128: ~0.30%</li><li>129: ~0.30%</li><li>130: ~0.50%</li><li>131: ~0.30%</li><li>132: ~0.40%</li><li>133: ~0.30%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.30%</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: ~0.40%</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: ~0.70%</li><li>151: ~0.30%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~1.30%</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: ~1.50%</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: ~1.50%</li><li>169: ~0.30%</li><li>170: ~0.30%</li><li>171: ~7.20%</li><li>172: ~0.30%</li><li>173: ~1.00%</li><li>174: ~0.30%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~1.80%</li><li>178: ~0.30%</li><li>179: ~0.50%</li><li>180: ~0.70%</li><li>181: ~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>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`: 200
- `warmup_ratio`: 0.15
- `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`: 200
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.15
- `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
| Epoch    | Step | Training Loss |
|:--------:|:----:|:-------------:|
| 1.7576   | 50   | 0.0447        |
| 3.7576   | 100  | 0.048         |
| 5.7576   | 150  | 0.0472        |
| 7.7576   | 200  | 0.0505        |
| 9.7576   | 250  | 0.0547        |
| 11.7576  | 300  | 0.0548        |
| 13.7576  | 350  | 0.0527        |
| 15.7576  | 400  | 0.0522        |
| 17.7576  | 450  | 0.0496        |
| 19.7576  | 500  | 0.0506        |
| 21.7576  | 550  | 0.048         |
| 23.7576  | 600  | 0.0508        |
| 25.7576  | 650  | 0.0499        |
| 27.7576  | 700  | 0.0474        |
| 29.7576  | 750  | 0.0467        |
| 31.7576  | 800  | 0.0483        |
| 33.7576  | 850  | 0.0438        |
| 35.7576  | 900  | 0.0457        |
| 37.7576  | 950  | 0.0445        |
| 39.7576  | 1000 | 0.0452        |
| 41.7576  | 1050 | 0.046         |
| 43.7576  | 1100 | 0.0433        |
| 45.7576  | 1150 | 0.0419        |
| 47.7576  | 1200 | 0.0407        |
| 49.7576  | 1250 | 0.0397        |
| 51.7576  | 1300 | 0.043         |
| 53.7576  | 1350 | 0.0393        |
| 55.7576  | 1400 | 0.0411        |
| 57.7576  | 1450 | 0.0434        |
| 59.7576  | 1500 | 0.0446        |
| 61.7576  | 1550 | 0.0396        |
| 63.7576  | 1600 | 0.0375        |
| 65.7576  | 1650 | 0.0413        |
| 67.7576  | 1700 | 0.0398        |
| 69.7576  | 1750 | 0.0382        |
| 71.7576  | 1800 | 0.0346        |
| 73.7576  | 1850 | 0.0388        |
| 75.7576  | 1900 | 0.0347        |
| 77.7576  | 1950 | 0.0349        |
| 79.7576  | 2000 | 0.0402        |
| 81.7576  | 2050 | 0.039         |
| 83.7576  | 2100 | 0.0343        |
| 85.7576  | 2150 | 0.0465        |
| 87.7576  | 2200 | 0.033         |
| 89.7576  | 2250 | 0.0385        |
| 91.7576  | 2300 | 0.0305        |
| 93.7576  | 2350 | 0.0367        |
| 95.7576  | 2400 | 0.0377        |
| 97.7576  | 2450 | 0.0322        |
| 99.7576  | 2500 | 0.0354        |
| 101.7576 | 2550 | 0.0332        |
| 103.7576 | 2600 | 0.0365        |
| 105.7576 | 2650 | 0.0357        |
| 107.7576 | 2700 | 0.0301        |
| 109.7576 | 2750 | 0.0323        |
| 111.7576 | 2800 | 0.0328        |
| 113.7576 | 2850 | 0.0339        |
| 115.7576 | 2900 | 0.0379        |
| 117.7576 | 2950 | 0.0334        |
| 119.7576 | 3000 | 0.0338        |
| 121.7576 | 3050 | 0.0328        |
| 123.7576 | 3100 | 0.0281        |
| 125.7576 | 3150 | 0.0316        |
| 127.7576 | 3200 | 0.0387        |
| 129.7576 | 3250 | 0.0327        |
| 131.7576 | 3300 | 0.026         |
| 133.7576 | 3350 | 0.0247        |
| 135.7576 | 3400 | 0.0319        |
| 137.7576 | 3450 | 0.0299        |
| 139.7576 | 3500 | 0.0252        |
| 141.7576 | 3550 | 0.0265        |
| 143.7576 | 3600 | 0.0244        |
| 145.7576 | 3650 | 0.0317        |
| 147.7576 | 3700 | 0.0291        |
| 149.7576 | 3750 | 0.03          |
| 151.7576 | 3800 | 0.0299        |
| 153.7576 | 3850 | 0.0303        |
| 155.7576 | 3900 | 0.0296        |
| 157.7576 | 3950 | 0.0303        |
| 159.7576 | 4000 | 0.0282        |
| 161.7576 | 4050 | 0.0301        |
| 163.7576 | 4100 | 0.027         |
| 165.7576 | 4150 | 0.0259        |
| 167.7576 | 4200 | 0.0294        |
| 169.7576 | 4250 | 0.0267        |
| 171.7576 | 4300 | 0.0303        |
| 173.7576 | 4350 | 0.0199        |
| 175.7576 | 4400 | 0.0253        |
| 177.7576 | 4450 | 0.0254        |
| 179.7576 | 4500 | 0.0202        |
| 181.7576 | 4550 | 0.0263        |
| 183.7576 | 4600 | 0.0302        |
| 185.7576 | 4650 | 0.0292        |
| 187.7576 | 4700 | 0.0264        |
| 189.7576 | 4750 | 0.0289        |
| 191.7576 | 4800 | 0.026         |
| 193.7576 | 4850 | 0.0285        |
| 195.7576 | 4900 | 0.0234        |
| 197.7576 | 4950 | 0.0297        |
| 199.7576 | 5000 | 0.0238        |


### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- 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",
}
```

#### 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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