Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

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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]
+```
+
+<!--
+### 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: 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��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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 32768
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.51.1",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6e476a706ccc7c5e60d0adbe489e86eec52e25856f73e94488bcd413b87a16e
+size 444851048

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "do_subword_tokenize": true,
+  "do_word_tokenize": true,
+  "extra_special_tokens": {},
+  "jumanpp_kwargs": null,
+  "mask_token": "[MASK]",
+  "mecab_kwargs": {
+    "mecab_dic": "unidic_lite"
+  },
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "subword_tokenizer_type": "wordpiece",
+  "sudachi_kwargs": null,
+  "tokenizer_class": "BertJapaneseTokenizer",
+  "unk_token": "[UNK]",
+  "word_tokenizer_type": "mecab"
+}