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cl-nagoya-sup-simcse-ja-nss-v0_9_15

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:7598
loss:DualMarginContrastiveLoss
loss:CustomBatchAllTripletLoss
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 ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:7598
- loss:DualMarginContrastiveLoss
- loss:CustomBatchAllTripletLoss
widget:
- source_sentence: 科目：塗装。名称：PCaﾌｯ素樹脂ｸﾘｱ塗り。
sentences:
- 科目：塗装。名称：PCa面塗り（細幅物）。
- 科目：塗装。名称：間接照明塗り。
- 科目：塗装。名称：PCa面塗り。
- source_sentence: 科目：塗装。名称：PCa水性ｼﾘｺﾝ樹脂ｸﾘﾔ塗り（細幅物）。
sentences:
- 科目：塗装。名称：間接照明塗り（細幅物）。
- 科目：塗装。名称：間接照明塗り。
- 科目：塗装。名称：PCa面塗り（細幅物）。
- source_sentence: 科目：塗装。名称：間接照明ﾎﾞｯｸｽOS塗り。
sentences:
- 科目：塗装。名称：間接照明塗り。
- 科目：塗装。名称：その他塗装。
- 科目：塗装。名称：照明ｽﾘｯﾄ下り天井塗り。
- source_sentence: 科目：塗装。名称：NAD塗り。
sentences:
- 科目：塗装。名称：その他塗装。
- 科目：塗装。名称：その他塗装。
- 科目：塗装。名称：PCa面塗り（細幅物）。
- source_sentence: 科目：塗装。名称：PCa保護塗り（細幅物）。
sentences:
- 科目：塗装。名称：PCa面塗り（細幅物）。
- 科目：塗装。名称：PCa面塗り（細幅物）。
- 科目：塗装。名称：PCa面塗り（細幅物）。
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-v0_9_15")
# Run inference
sentences = [
'科目：塗装。名称：PCa保護塗り（細幅物）。',
'科目：塗装。名称：PCa面塗り（細幅物）。',
'科目：塗装。名称：PCa面塗り（細幅物）。',
]
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]
```
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<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 7,598 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: 17.2 tokens</li><li>max: 29 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: ~0.40%</li><li>12: ~0.30%</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: ~0.50%</li><li>19: ~0.30%</li><li>20: ~0.30%</li><li>21: ~0.30%</li><li>22: ~0.30%</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: ~0.30%</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: ~0.30%</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: ~0.50%</li><li>87: ~0.30%</li><li>88: ~0.30%</li><li>89: ~16.30%</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: ~0.30%</li><li>98: ~0.50%</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: ~0.30%</li><li>106: ~1.20%</li><li>107: ~0.70%</li><li>108: ~0.30%</li><li>109: ~3.20%</li><li>110: ~0.30%</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: ~0.30%</li><li>118: ~0.30%</li><li>119: ~0.30%</li><li>120: ~0.80%</li><li>121: ~0.30%</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: ~0.30%</li><li>131: ~0.40%</li><li>132: ~0.30%</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: ~0.40%</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: ~0.70%</li><li>150: ~0.30%</li><li>151: ~0.30%</li><li>152: ~0.30%</li><li>153: ~1.30%</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: ~1.30%</li><li>160: ~0.30%</li><li>161: ~0.30%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.30%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~1.50%</li><li>168: ~0.30%</li><li>169: ~0.30%</li><li>170: ~7.90%</li><li>171: ~0.30%</li><li>172: ~1.00%</li><li>173: ~0.30%</li><li>174: ~0.30%</li><li>175: ~0.30%</li><li>176: ~1.80%</li><li>177: ~0.30%</li><li>178: ~0.50%</li><li>179: ~0.70%</li><li>180: ~0.30%</li><li>181: ~0.30%</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: ~2.50%</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`: 0.1
- `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
| Epoch | Step | Training Loss |
|:--------:|:----:|:-------------:|
| 0.6667 | 10 | 0.0662 |
| 1.3333 | 20 | 0.0 |
| 2.0 | 30 | 0.0 |
| 2.6667 | 40 | 0.0 |
| 3.3333 | 50 | 0.0 |
| 4.0 | 60 | 0.0 |
| 4.6667 | 70 | 0.0 |
| 5.3333 | 80 | 0.0 |
| 6.0 | 90 | 0.0 |
| 6.6667 | 100 | 0.0 |
| 7.3333 | 110 | 0.0 |
| 8.0 | 120 | 0.0 |
| 8.6667 | 130 | 0.0 |
| 9.3333 | 140 | 0.0 |
| 10.0 | 150 | 0.0 |
| 10.0 | 10 | 2.7711 |
| 20.0 | 20 | 1.2115 |
| 30.0 | 30 | 0.3753 |
| 40.0 | 40 | 0.1646 |
| 50.0 | 50 | 0.0876 |
| 60.0 | 60 | 0.0559 |
| 70.0 | 70 | 0.0344 |
| 80.0 | 80 | 0.0262 |
| 90.0 | 90 | 0.0194 |
| 100.0 | 100 | 0.0218 |
| 110.0 | 110 | 0.0214 |
| 120.0 | 120 | 0.014 |
| 130.0 | 130 | 0.0231 |
| 140.0 | 140 | 0.0132 |
| 150.0 | 150 | 0.0146 |
| 3.7576 | 100 | 0.0701 |
| 7.7576 | 200 | 0.0747 |
| 11.7576 | 300 | 0.0709 |
| 15.7576 | 400 | 0.0689 |
| 19.7576 | 500 | 0.0622 |
| 23.7576 | 600 | 0.0639 |
| 27.7576 | 700 | 0.063 |
| 31.7576 | 800 | 0.0605 |
| 35.7576 | 900 | 0.061 |
| 39.7576 | 1000 | 0.0602 |
| 43.7576 | 1100 | 0.0609 |
| 47.7576 | 1200 | 0.0596 |
| 51.7576 | 1300 | 0.0568 |
| 55.7576 | 1400 | 0.0593 |
| 59.7576 | 1500 | 0.058 |
| 63.7576 | 1600 | 0.0613 |
| 67.7576 | 1700 | 0.0515 |
| 71.7576 | 1800 | 0.0511 |
| 75.7576 | 1900 | 0.0538 |
| 79.7576 | 2000 | 0.0559 |
| 83.7576 | 2100 | 0.0482 |
| 87.7576 | 2200 | 0.0511 |
| 91.7576 | 2300 | 0.0553 |
| 95.7576 | 2400 | 0.0522 |
| 99.7576 | 2500 | 0.0534 |
| 103.7576 | 2600 | 0.0477 |
| 107.7576 | 2700 | 0.052 |
| 111.7576 | 2800 | 0.0518 |
| 115.7576 | 2900 | 0.047 |
| 119.7576 | 3000 | 0.0503 |
| 123.7576 | 3100 | 0.0494 |
| 127.7576 | 3200 | 0.0488 |
| 131.7576 | 3300 | 0.052 |
| 135.7576 | 3400 | 0.0459 |
| 139.7576 | 3500 | 0.0467 |
| 143.7576 | 3600 | 0.0493 |
| 147.7576 | 3700 | 0.0453 |
| 151.7576 | 3800 | 0.0457 |
| 155.7576 | 3900 | 0.0462 |
| 159.7576 | 4000 | 0.0451 |
| 163.7576 | 4100 | 0.0446 |
| 167.7576 | 4200 | 0.0438 |
| 171.7576 | 4300 | 0.0398 |
| 175.7576 | 4400 | 0.0414 |
| 179.7576 | 4500 | 0.045 |
| 183.7576 | 4600 | 0.0448 |
| 187.7576 | 4700 | 0.0426 |
| 191.7576 | 4800 | 0.0427 |
| 195.7576 | 4900 | 0.0434 |
| 199.7576 | 5000 | 0.039 |
| 203.7576 | 5100 | 0.0381 |
| 207.7576 | 5200 | 0.0434 |
| 211.7576 | 5300 | 0.041 |
| 215.7576 | 5400 | 0.0463 |
| 219.7576 | 5500 | 0.0386 |
| 223.7576 | 5600 | 0.0453 |
| 227.7576 | 5700 | 0.0412 |
| 231.7576 | 5800 | 0.0373 |
| 235.7576 | 5900 | 0.0393 |
| 239.7576 | 6000 | 0.0362 |
| 243.7576 | 6100 | 0.0363 |
| 247.7576 | 6200 | 0.0372 |
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.50.3
- 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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