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cl-nagoya-sup-simcse-ja-nss-v_1_0_5

Sentence Similarity
sentence-transformers
Safetensors
OpenVINO
bert
feature-extraction
Generated from Trainer
dataset_size:12611
loss:CustomBatchAllTripletLoss
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Add new SentenceTransformer model with an openvino backend (#1)
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:12611
- loss:CustomBatchAllTripletLoss
widget:
- source_sentence: 科目：コンクリート。名称：免震基礎部ｺﾝｸﾘｰﾄ。
sentences:
- 科目：ユニット及びその他。名称：受付表示。
- 科目：ユニット及びその他。名称：F-#階数表示-A（EV前･屋外階段）。
- 科目：ユニット及びその他。名称：P-#-aEV前ﾌﾛｱ案内ｻｲﾝ。
- source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=24 S18粗骨材地上部。備考：代価表 0056。
sentences:
- 科目：タイル。名称：ｽﾛｰﾌﾟ床ﾀｲﾙ。
- 科目：ユニット及びその他。名称：議場表示。
- 科目：コンクリート。名称：ｺﾝｸﾘｰﾄ打設手間･ﾎﾟﾝﾌﾟ圧送。
- source_sentence: 科目：コンクリート。名称：普通ｺﾝｸﾘｰﾄ。摘要：FC=21 S18粗骨材地上部。備考：代価表 0057。
sentences:
- 科目：ユニット及びその他。名称：#階女子ﾄｲﾚ鏡。
- 科目：ユニット及びその他。名称：多目的ﾎｰﾙ座席案内ｻｲﾝ。
- 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：段床ﾀｲﾙ張り。
sentences:
- 科目：ユニット及びその他。名称：ｴﾚﾍﾞｰﾀｰｶｰﾄﾞﾘｰﾀﾞｰ関連工事。
- 科目：ユニット及びその他。名称：男子便所鏡。
- 科目：ユニット及びその他。名称：#階ﾃﾗｽ床人工木ﾃﾞｯｷ。
- source_sentence: 科目：タイル。名称：床磁器質ﾀｲﾙ。
sentences:
- 科目：ユニット及びその他。名称：#F薬渡し窓口ｶｳﾝﾀｰ。
- 科目：コンクリート。名称：設備基礎ｺﾝｸﾘｰﾄ。摘要：FC21N/mm2 ｽﾗﾝﾌﾟ18。備考：代価表 0036。
- 科目：ユニット及びその他。名称：F-#c教員棚。
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v_1_0_5")
# Run inference
sentences = [
'科目：タイル。名称：床磁器質ﾀｲﾙ。',
'科目：ユニット及びその他。名称：#F薬渡し窓口ｶｳﾝﾀｰ。',
'科目：ユニット及びその他。名称：F-#c教員棚。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 12,611 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | <ul><li>min: 11 tokens</li><li>mean: 18.16 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.30%</li><li>5: ~0.30%</li><li>6: ~0.30%</li><li>7: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.30%</li><li>11: ~0.30%</li><li>12: ~1.10%</li><li>13: ~0.30%</li><li>14: ~0.30%</li><li>15: ~0.30%</li><li>16: ~0.30%</li><li>17: ~0.30%</li><li>18: ~0.30%</li><li>19: ~0.30%</li><li>20: ~0.30%</li><li>21: ~0.30%</li><li>22: ~0.30%</li><li>23: ~0.40%</li><li>24: ~0.30%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.90%</li><li>28: ~0.30%</li><li>29: ~0.40%</li><li>30: ~0.30%</li><li>31: ~1.10%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.30%</li><li>41: ~0.30%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.30%</li><li>45: ~0.30%</li><li>46: ~0.30%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.40%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.60%</li><li>54: ~0.70%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.30%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.30%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.30%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.50%</li><li>69: ~0.30%</li><li>70: ~0.30%</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: ~0.30%</li><li>79: ~0.30%</li><li>80: ~0.30%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.30%</li><li>84: ~0.80%</li><li>85: ~0.60%</li><li>86: ~0.30%</li><li>87: ~0.30%</li><li>88: ~0.30%</li><li>89: ~0.30%</li><li>90: ~0.30%</li><li>91: ~0.30%</li><li>92: ~0.30%</li><li>93: ~0.50%</li><li>94: ~0.30%</li><li>95: ~0.30%</li><li>96: ~0.30%</li><li>97: ~0.30%</li><li>98: ~0.80%</li><li>99: ~0.60%</li><li>100: ~0.50%</li><li>101: ~0.30%</li><li>102: ~0.30%</li><li>103: ~16.50%</li><li>104: ~0.30%</li><li>105: ~0.30%</li><li>106: ~0.30%</li><li>107: ~0.30%</li><li>108: ~0.30%</li><li>109: ~0.30%</li><li>110: ~0.30%</li><li>111: ~0.30%</li><li>112: ~0.50%</li><li>113: ~0.30%</li><li>114: ~0.30%</li><li>115: ~0.30%</li><li>116: ~0.30%</li><li>117: ~0.30%</li><li>118: ~0.30%</li><li>119: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.70%</li><li>122: ~0.30%</li><li>123: ~0.30%</li><li>124: ~0.30%</li><li>125: ~0.40%</li><li>126: ~2.10%</li><li>127: ~2.10%</li><li>128: ~0.30%</li><li>129: ~0.30%</li><li>130: ~0.50%</li><li>131: ~0.50%</li><li>132: ~0.50%</li><li>133: ~0.40%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.80%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.30%</li><li>144: ~0.30%</li><li>145: ~0.30%</li><li>146: ~0.30%</li><li>147: ~0.30%</li><li>148: ~0.30%</li><li>149: ~0.30%</li><li>150: ~0.50%</li><li>151: ~0.30%</li><li>152: ~0.40%</li><li>153: ~0.30%</li><li>154: ~0.30%</li><li>155: ~0.30%</li><li>156: ~0.30%</li><li>157: ~0.30%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~0.30%</li><li>161: ~0.30%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.40%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~0.30%</li><li>169: ~0.30%</li><li>170: ~0.30%</li><li>171: ~0.70%</li><li>172: ~0.30%</li><li>173: ~0.30%</li><li>174: ~0.30%</li><li>175: ~1.30%</li><li>176: ~0.30%</li><li>177: ~0.40%</li><li>178: ~0.30%</li><li>179: ~0.30%</li><li>180: ~0.30%</li><li>181: ~1.50%</li><li>182: ~0.30%</li><li>183: ~0.30%</li><li>184: ~0.30%</li><li>185: ~0.30%</li><li>186: ~0.30%</li><li>187: ~0.30%</li><li>188: ~0.30%</li><li>189: ~1.60%</li><li>190: ~0.30%</li><li>191: ~0.30%</li><li>192: ~7.20%</li><li>193: ~0.30%</li><li>194: ~1.00%</li><li>195: ~0.30%</li><li>196: ~0.30%</li><li>197: ~0.30%</li><li>198: ~1.50%</li></ul> |
* Samples:
| sentence | label |
|:-----------------------------------------|:---------------|
| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
* Loss: <code>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 250
- `warmup_ratio`: 0.2
- `fp16`: True
- `batch_sampler`: group_by_label
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 250
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.2
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: group_by_label
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:--------:|:----:|:-------------:|
| 2.24 | 50 | 0.0583 |
| 4.48 | 100 | 0.0626 |
| 6.72 | 150 | 0.0638 |
| 9.08 | 200 | 0.0659 |
| 11.32 | 250 | 0.0629 |
| 13.56 | 300 | 0.0608 |
| 15.8 | 350 | 0.0607 |
| 18.16 | 400 | 0.0584 |
| 20.4 | 450 | 0.0577 |
| 22.64 | 500 | 0.0566 |
| 24.88 | 550 | 0.0594 |
| 27.24 | 600 | 0.0552 |
| 29.48 | 650 | 0.0512 |
| 31.72 | 700 | 0.053 |
| 34.08 | 750 | 0.0538 |
| 36.32 | 800 | 0.0506 |
| 38.56 | 850 | 0.054 |
| 40.8 | 900 | 0.0498 |
| 43.16 | 950 | 0.0538 |
| 45.4 | 1000 | 0.0491 |
| 47.64 | 1050 | 0.0445 |
| 49.88 | 1100 | 0.0466 |
| 52.24 | 1150 | 0.0458 |
| 54.48 | 1200 | 0.0507 |
| 56.72 | 1250 | 0.0408 |
| 59.08 | 1300 | 0.0462 |
| 61.32 | 1350 | 0.0443 |
| 63.56 | 1400 | 0.0392 |
| 65.8 | 1450 | 0.0389 |
| 68.16 | 1500 | 0.0455 |
| 70.4 | 1550 | 0.049 |
| 72.64 | 1600 | 0.0435 |
| 74.88 | 1650 | 0.0416 |
| 77.24 | 1700 | 0.041 |
| 79.48 | 1750 | 0.0443 |
| 81.72 | 1800 | 0.0423 |
| 84.08 | 1850 | 0.0457 |
| 86.32 | 1900 | 0.0375 |
| 88.56 | 1950 | 0.0428 |
| 90.8 | 2000 | 0.037 |
| 93.16 | 2050 | 0.0441 |
| 95.4 | 2100 | 0.0382 |
| 97.64 | 2150 | 0.0424 |
| 99.88 | 2200 | 0.041 |
| 1.6667 | 50 | 0.0381 |
| 3.6111 | 100 | 0.0373 |
| 5.5556 | 150 | 0.0381 |
| 7.5 | 200 | 0.0394 |
| 9.4444 | 250 | 0.0399 |
| 11.3889 | 300 | 0.0405 |
| 13.3333 | 350 | 0.0409 |
| 15.2778 | 400 | 0.0408 |
| 17.2222 | 450 | 0.0404 |
| 19.1667 | 500 | 0.0396 |
| 21.1111 | 550 | 0.038 |
| 23.0556 | 600 | 0.0346 |
| 24.7222 | 650 | 0.0381 |
| 26.6667 | 700 | 0.0356 |
| 28.6111 | 750 | 0.0344 |
| 30.5556 | 800 | 0.0344 |
| 32.5 | 850 | 0.0365 |
| 34.4444 | 900 | 0.0354 |
| 36.3889 | 950 | 0.0324 |
| 38.3333 | 1000 | 0.0301 |
| 40.2778 | 1050 | 0.038 |
| 42.2222 | 1100 | 0.0351 |
| 44.1667 | 1150 | 0.0344 |
| 46.1111 | 1200 | 0.0339 |
| 48.0556 | 1250 | 0.0358 |
| 49.7222 | 1300 | 0.0312 |
| 51.6667 | 1350 | 0.0278 |
| 53.6111 | 1400 | 0.0342 |
| 55.5556 | 1450 | 0.0291 |
| 57.5 | 1500 | 0.03 |
| 59.4444 | 1550 | 0.03 |
| 61.3889 | 1600 | 0.0303 |
| 63.3333 | 1650 | 0.0339 |
| 65.2778 | 1700 | 0.0342 |
| 67.2222 | 1750 | 0.0283 |
| 69.1667 | 1800 | 0.0271 |
| 71.1111 | 1850 | 0.0327 |
| 73.0556 | 1900 | 0.0296 |
| 74.7222 | 1950 | 0.0295 |
| 76.6667 | 2000 | 0.0259 |
| 78.6111 | 2050 | 0.0296 |
| 80.5556 | 2100 | 0.0256 |
| 82.5 | 2150 | 0.0271 |
| 84.4444 | 2200 | 0.0287 |
| 86.3889 | 2250 | 0.028 |
| 88.3333 | 2300 | 0.0275 |
| 90.2778 | 2350 | 0.0294 |
| 92.2222 | 2400 | 0.0243 |
| 94.1667 | 2450 | 0.0275 |
| 96.1111 | 2500 | 0.0258 |
| 98.0556 | 2550 | 0.0215 |
| 99.7222 | 2600 | 0.0252 |
| 101.6667 | 2650 | 0.029 |
| 103.6111 | 2700 | 0.0265 |
| 105.5556 | 2750 | 0.0258 |
| 107.5 | 2800 | 0.0222 |
| 109.4444 | 2850 | 0.0263 |
| 111.3889 | 2900 | 0.0266 |
| 113.3333 | 2950 | 0.0211 |
| 115.2778 | 3000 | 0.0251 |
| 117.2222 | 3050 | 0.0224 |
| 119.1667 | 3100 | 0.0204 |
| 121.1111 | 3150 | 0.0226 |
| 123.0556 | 3200 | 0.025 |
| 124.7222 | 3250 | 0.0214 |
| 126.6667 | 3300 | 0.0237 |
| 128.6111 | 3350 | 0.0287 |
| 130.5556 | 3400 | 0.0229 |
| 132.5 | 3450 | 0.0171 |
| 134.4444 | 3500 | 0.0215 |
| 136.3889 | 3550 | 0.0236 |
| 138.3333 | 3600 | 0.0238 |
| 140.2778 | 3650 | 0.0168 |
| 142.2222 | 3700 | 0.0281 |
| 144.1667 | 3750 | 0.0247 |
| 146.1111 | 3800 | 0.02 |
| 148.0556 | 3850 | 0.0225 |
| 149.7222 | 3900 | 0.0189 |
| 151.6667 | 3950 | 0.0178 |
| 153.6111 | 4000 | 0.0174 |
| 155.5556 | 4050 | 0.0165 |
| 157.5 | 4100 | 0.0197 |
| 159.4444 | 4150 | 0.0226 |
| 161.3889 | 4200 | 0.0126 |
| 163.3333 | 4250 | 0.0224 |
| 165.2778 | 4300 | 0.0174 |
| 167.2222 | 4350 | 0.0214 |
| 169.1667 | 4400 | 0.0159 |
| 171.1111 | 4450 | 0.0121 |
| 173.0556 | 4500 | 0.0194 |
| 174.7222 | 4550 | 0.0216 |
| 176.6667 | 4600 | 0.0193 |
| 178.6111 | 4650 | 0.0157 |
| 180.5556 | 4700 | 0.0159 |
| 182.5 | 4750 | 0.016 |
| 184.4444 | 4800 | 0.0182 |
| 186.3889 | 4850 | 0.0181 |
| 188.3333 | 4900 | 0.0164 |
| 190.2778 | 4950 | 0.0204 |
| 192.2222 | 5000 | 0.0188 |
| 194.1667 | 5050 | 0.0155 |
| 196.1111 | 5100 | 0.0166 |
| 198.0556 | 5150 | 0.0165 |
| 199.7222 | 5200 | 0.0111 |
| 201.6667 | 5250 | 0.0181 |
| 203.6111 | 5300 | 0.0196 |
| 205.5556 | 5350 | 0.0164 |
| 207.5 | 5400 | 0.0125 |
| 209.4444 | 5450 | 0.0168 |
| 211.3889 | 5500 | 0.0174 |
| 213.3333 | 5550 | 0.0144 |
| 215.2778 | 5600 | 0.0169 |
| 217.2222 | 5650 | 0.019 |
| 219.1667 | 5700 | 0.0178 |
| 221.1111 | 5750 | 0.014 |
| 223.0556 | 5800 | 0.0154 |
| 224.7222 | 5850 | 0.0151 |
| 226.6667 | 5900 | 0.0105 |
| 228.6111 | 5950 | 0.013 |
| 230.5556 | 6000 | 0.0152 |
| 232.5 | 6050 | 0.0138 |
| 234.4444 | 6100 | 0.0133 |
| 236.3889 | 6150 | 0.015 |
| 238.3333 | 6200 | 0.0119 |
| 240.2778 | 6250 | 0.0185 |
| 242.2222 | 6300 | 0.0104 |
| 244.1667 | 6350 | 0.0155 |
| 246.1111 | 6400 | 0.0135 |
| 248.0556 | 6450 | 0.0141 |
| 249.7222 | 6500 | 0.0168 |
</details>
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.1
- 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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