---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:9054
- loss:BatchAllTripletLoss
base_model: cl-nagoya/sup-simcse-ja-base
widget:
- source_sentence: 科目:ﾕﾆｯﾄ及びその他｡名称:⑮傍聴席椅子#人掛け｡
  sentences:
  - 科目:ﾕﾆｯﾄ及びその他｡名称:#階副議長室ﾛｰﾙｽｸﾘｰﾝ｡
  - 科目:ﾕﾆｯﾄ及びその他｡名称:控室B#-#､B#-#ｶｳﾝﾀｰ｡
  - 科目:ﾕﾆｯﾄ及びその他｡名称:記載ｶｳﾝﾀｰｻｲﾝ｡
- source_sentence: 科目:ｺﾝｸﾘｰﾄ｡名称:多目的ﾎｰﾙ機械式移動座席基礎ｺﾝｸﾘｰﾄ｡
  sentences:
  - 科目:ﾕﾆｯﾄ及びその他｡名称:床ｱｽﾌｧﾙﾄ舗装｡
  - 科目:ﾀｲﾙ｡名称:立上りﾀｲﾙA｡
  - 科目:ﾀｲﾙ｡名称:段床磁器質ﾀｲﾙ｡
- source_sentence: 科目:ﾀｲﾙ｡名称:ﾄﾞﾗｲｴﾘｱ床ﾀｲﾙ張り｡
  sentences:
  - 科目:ﾕﾆｯﾄ及びその他｡名称:ｶｰﾃﾝﾎﾞｯｸｽD｡
  - 科目:ﾕﾆｯﾄ及びその他｡名称:総合案内ｶｳﾝﾀｰ｡
  - 科目:ﾕﾆｯﾄ及びその他｡名称:天井ｶｰﾃﾝﾚｰﾙ｡
- source_sentence: 科目:ﾀｲﾙ｡名称:屋外階段踊場ﾀｲﾙ張り｡
  sentences:
  - 科目:ﾕﾆｯﾄ及びその他｡名称:執務室#-#規格品ｶｳﾝﾀｰ｡
  - 科目:ﾕﾆｯﾄ及びその他｡名称:受付ﾛﾋﾞｰｶｳﾝﾀｰ｡
  - 科目:ﾕﾆｯﾄ及びその他｡名称:Sc-#踊場階数表示ｻｲﾝ｡
- 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:#救助袋ｻｲﾝ(ｶﾞﾗｽ面)｡
  - 科目:ﾕﾆｯﾄ及びその他｡名称:La-#開閉注意ｻｲﾝ｡
  - 科目:ﾕﾆｯﾄ及びその他｡名称:案内ｽﾀﾝﾄﾞｻｲﾝ｡
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer based on cl-nagoya/sup-simcse-ja-base

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base) <!-- at revision d7315d93baf2c20fffa2b6845330049963509f79 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-for-standard-name-v0_9_6")
# 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:#救助袋ｻｲﾝ(ｶﾞﾗｽ面)｡',
    '科目:ﾕﾆｯﾄ及びその他｡名称:案内ｽﾀﾝﾄﾞｻｲﾝ｡',
]
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: 9,054 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: 17.79 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>0: ~0.20%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~0.20%</li><li>5: ~0.20%</li><li>6: ~0.20%</li><li>7: ~0.20%</li><li>8: ~0.20%</li><li>9: ~0.20%</li><li>10: ~0.30%</li><li>11: ~0.20%</li><li>12: ~0.20%</li><li>13: ~0.20%</li><li>14: ~0.20%</li><li>15: ~0.20%</li><li>16: ~0.20%</li><li>17: ~0.40%</li><li>18: ~0.20%</li><li>19: ~0.20%</li><li>20: ~0.20%</li><li>21: ~0.20%</li><li>22: ~0.20%</li><li>23: ~0.20%</li><li>24: ~0.20%</li><li>25: ~0.20%</li><li>26: ~0.20%</li><li>27: ~0.20%</li><li>28: ~0.20%</li><li>29: ~0.20%</li><li>30: ~0.20%</li><li>31: ~0.20%</li><li>32: ~0.20%</li><li>33: ~0.20%</li><li>34: ~0.20%</li><li>35: ~0.20%</li><li>36: ~0.20%</li><li>37: ~0.20%</li><li>38: ~0.20%</li><li>39: ~0.20%</li><li>40: ~0.20%</li><li>41: ~0.20%</li><li>42: ~0.20%</li><li>43: ~0.60%</li><li>44: ~0.70%</li><li>45: ~0.20%</li><li>46: ~0.20%</li><li>47: ~0.20%</li><li>48: ~0.20%</li><li>49: ~0.20%</li><li>50: ~0.30%</li><li>51: ~0.20%</li><li>52: ~0.20%</li><li>53: ~0.20%</li><li>54: ~0.20%</li><li>55: ~0.30%</li><li>56: ~0.40%</li><li>57: ~0.30%</li><li>58: ~0.20%</li><li>59: ~0.20%</li><li>60: ~0.20%</li><li>61: ~0.20%</li><li>62: ~0.20%</li><li>63: ~0.30%</li><li>64: ~0.20%</li><li>65: ~0.20%</li><li>66: ~0.20%</li><li>67: ~0.20%</li><li>68: ~0.40%</li><li>69: ~0.40%</li><li>70: ~0.20%</li><li>71: ~0.60%</li><li>72: ~0.20%</li><li>73: ~0.20%</li><li>74: ~0.20%</li><li>75: ~0.20%</li><li>76: ~0.20%</li><li>77: ~0.30%</li><li>78: ~0.20%</li><li>79: ~0.40%</li><li>80: ~0.20%</li><li>81: ~0.20%</li><li>82: ~0.50%</li><li>83: ~0.30%</li><li>84: ~0.60%</li><li>85: ~0.20%</li><li>86: ~0.30%</li><li>87: ~0.20%</li><li>88: ~0.20%</li><li>89: ~0.20%</li><li>90: ~0.20%</li><li>91: ~1.10%</li><li>92: ~1.70%</li><li>93: ~2.20%</li><li>94: ~0.50%</li><li>95: ~0.20%</li><li>96: ~0.20%</li><li>97: ~1.50%</li><li>98: ~0.20%</li><li>99: ~0.20%</li><li>100: ~0.20%</li><li>101: ~0.20%</li><li>102: ~0.30%</li><li>103: ~1.70%</li><li>104: ~0.20%</li><li>105: ~0.20%</li><li>106: ~0.40%</li><li>107: ~0.40%</li><li>108: ~0.20%</li><li>109: ~0.20%</li><li>110: ~0.20%</li><li>111: ~1.10%</li><li>112: ~0.20%</li><li>113: ~0.50%</li><li>114: ~0.50%</li><li>115: ~0.20%</li><li>116: ~0.20%</li><li>117: ~0.20%</li><li>118: ~0.20%</li><li>119: ~0.50%</li><li>120: ~0.20%</li><li>121: ~0.20%</li><li>122: ~0.20%</li><li>123: ~0.20%</li><li>124: ~0.20%</li><li>125: ~0.20%</li><li>126: ~0.30%</li><li>127: ~0.20%</li><li>128: ~0.20%</li><li>129: ~0.20%</li><li>130: ~0.50%</li><li>131: ~0.20%</li><li>132: ~0.20%</li><li>133: ~0.20%</li><li>134: ~0.20%</li><li>135: ~0.20%</li><li>136: ~0.20%</li><li>137: ~0.20%</li><li>138: ~0.20%</li><li>139: ~0.20%</li><li>140: ~0.20%</li><li>141: ~1.80%</li><li>142: ~0.20%</li><li>143: ~0.20%</li><li>144: ~1.70%</li><li>145: ~0.30%</li><li>146: ~0.30%</li><li>147: ~0.50%</li><li>148: ~0.50%</li><li>149: ~0.50%</li><li>150: ~0.20%</li><li>151: ~0.20%</li><li>152: ~0.20%</li><li>153: ~0.20%</li><li>154: ~0.20%</li><li>155: ~0.20%</li><li>156: ~0.20%</li><li>157: ~0.20%</li><li>158: ~0.60%</li><li>159: ~0.20%</li><li>160: ~0.20%</li><li>161: ~0.20%</li><li>162: ~0.20%</li><li>163: ~0.20%</li><li>164: ~0.50%</li><li>165: ~0.20%</li><li>166: ~0.20%</li><li>167: ~0.20%</li><li>168: ~0.20%</li><li>169: ~0.20%</li><li>170: ~0.30%</li><li>171: ~0.30%</li><li>172: ~0.20%</li><li>173: ~0.20%</li><li>174: ~0.20%</li><li>175: ~0.20%</li><li>176: ~0.20%</li><li>177: ~0.60%</li><li>178: ~0.20%</li><li>179: ~0.20%</li><li>180: ~0.20%</li><li>181: ~0.20%</li><li>182: ~0.20%</li><li>183: ~0.40%</li><li>184: ~0.20%</li><li>185: ~0.20%</li><li>186: ~0.30%</li><li>187: ~0.20%</li><li>188: ~0.90%</li><li>189: ~0.30%</li><li>190: ~0.30%</li><li>191: ~0.20%</li><li>192: ~0.30%</li><li>193: ~0.20%</li><li>194: ~0.80%</li><li>195: ~0.20%</li><li>196: ~0.20%</li><li>197: ~0.30%</li><li>198: ~0.20%</li><li>199: ~0.20%</li><li>200: ~0.20%</li><li>201: ~0.20%</li><li>202: ~0.20%</li><li>203: ~1.20%</li><li>204: ~0.40%</li><li>205: ~0.20%</li><li>206: ~0.20%</li><li>207: ~0.20%</li><li>208: ~0.20%</li><li>209: ~1.00%</li><li>210: ~0.20%</li><li>211: ~0.30%</li><li>212: ~0.20%</li><li>213: ~1.10%</li><li>214: ~0.30%</li><li>215: ~0.20%</li><li>216: ~0.20%</li><li>217: ~0.20%</li><li>218: ~0.20%</li><li>219: ~0.20%</li><li>220: ~0.20%</li><li>221: ~0.20%</li><li>222: ~0.30%</li><li>223: ~0.20%</li><li>224: ~0.90%</li><li>225: ~4.70%</li><li>226: ~0.20%</li><li>227: ~0.20%</li><li>228: ~0.20%</li><li>229: ~0.70%</li><li>230: ~0.20%</li><li>231: ~0.80%</li><li>232: ~0.20%</li><li>233: ~0.40%</li><li>234: ~0.30%</li><li>235: ~0.40%</li><li>236: ~0.20%</li><li>237: ~0.30%</li><li>238: ~0.50%</li><li>239: ~0.30%</li><li>240: ~0.20%</li><li>241: ~0.20%</li><li>242: ~0.30%</li><li>243: ~0.30%</li><li>244: ~0.30%</li><li>245: ~0.60%</li><li>246: ~0.20%</li><li>247: ~0.20%</li><li>248: ~0.20%</li><li>249: ~0.30%</li><li>250: ~0.30%</li><li>251: ~1.90%</li><li>252: ~0.20%</li><li>253: ~0.20%</li><li>254: ~0.20%</li><li>255: ~0.20%</li><li>256: ~0.20%</li><li>257: ~0.50%</li><li>258: ~0.20%</li><li>259: ~0.30%</li><li>260: ~0.20%</li><li>261: ~0.20%</li><li>262: ~1.00%</li><li>263: ~0.20%</li><li>264: ~0.20%</li><li>265: ~0.20%</li><li>266: ~0.40%</li><li>267: ~0.20%</li><li>268: ~0.20%</li><li>269: ~0.20%</li><li>270: ~0.20%</li><li>271: ~0.20%</li><li>272: ~0.20%</li><li>273: ~0.20%</li><li>274: ~3.60%</li><li>275: ~0.20%</li><li>276: ~0.20%</li><li>277: ~0.40%</li><li>278: ~0.20%</li><li>279: ~0.20%</li><li>280: ~0.90%</li><li>281: ~0.40%</li><li>282: ~0.20%</li><li>283: ~2.30%</li><li>284: ~0.30%</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:ｺﾝｸﾘｰﾄﾎﾟﾝﾌﾟ圧送｡</code> | <code>1</code> |
* Loss: [<code>BatchAllTripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchalltripletloss)

### Training Hyperparameters
#### Non-Default Hyperparameters

- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 500
- `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`: 500
- `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}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: group_by_label
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
<details><summary>Click to expand</summary>

| Epoch    | Step | Training Loss |
|:--------:|:----:|:-------------:|
| 2.8889   | 50   | 0.7963        |
| 5.8333   | 100  | 0.7067        |
| 8.7778   | 150  | 0.6532        |
| 11.7222  | 200  | 0.6806        |
| 14.6667  | 250  | 0.652         |
| 17.6111  | 300  | 0.6508        |
| 20.5556  | 350  | 0.6566        |
| 23.5     | 400  | 0.6237        |
| 26.4444  | 450  | 0.6363        |
| 29.3889  | 500  | 0.6554        |
| 32.3333  | 550  | 0.6007        |
| 35.2778  | 600  | 0.6016        |
| 38.2222  | 650  | 0.5687        |
| 2.8889   | 50   | 0.5655        |
| 5.8333   | 100  | 0.6139        |
| 8.7778   | 150  | 0.514         |
| 11.7222  | 200  | 0.5867        |
| 14.6667  | 250  | 0.5699        |
| 17.6111  | 300  | 0.5472        |
| 20.5556  | 350  | 0.5793        |
| 23.5     | 400  | 0.5196        |
| 26.4444  | 450  | 0.5572        |
| 29.3889  | 500  | 0.5279        |
| 32.3333  | 550  | 0.5095        |
| 35.2778  | 600  | 0.4488        |
| 38.2222  | 650  | 0.4189        |
| 41.1667  | 700  | 0.5164        |
| 44.1111  | 750  | 0.591         |
| 47.0556  | 800  | 0.52          |
| 49.9444  | 850  | 0.5235        |
| 52.8889  | 900  | 0.5317        |
| 55.8333  | 950  | 0.5517        |
| 58.7778  | 1000 | 0.5618        |
| 61.7222  | 1050 | 0.5318        |
| 64.6667  | 1100 | 0.4685        |
| 67.6111  | 1150 | 0.4836        |
| 70.5556  | 1200 | 0.5426        |
| 73.5     | 1250 | 0.5356        |
| 76.4444  | 1300 | 0.4231        |
| 79.3889  | 1350 | 0.5104        |
| 82.3333  | 1400 | 0.4944        |
| 85.2778  | 1450 | 0.5301        |
| 88.2222  | 1500 | 0.4499        |
| 91.1667  | 1550 | 0.4745        |
| 94.1111  | 1600 | 0.4432        |
| 97.0556  | 1650 | 0.3892        |
| 99.9444  | 1700 | 0.4429        |
| 102.8889 | 1750 | 0.4973        |
| 105.8333 | 1800 | 0.5222        |
| 108.7778 | 1850 | 0.4502        |
| 111.7222 | 1900 | 0.4073        |
| 114.6667 | 1950 | 0.408         |
| 117.6111 | 2000 | 0.403         |
| 120.5556 | 2050 | 0.4122        |
| 123.5    | 2100 | 0.4357        |
| 126.4444 | 2150 | 0.4765        |
| 129.3889 | 2200 | 0.4069        |
| 132.3333 | 2250 | 0.388         |
| 135.2778 | 2300 | 0.341         |
| 138.2222 | 2350 | 0.333         |
| 141.1667 | 2400 | 0.4587        |
| 144.1111 | 2450 | 0.355         |
| 147.0556 | 2500 | 0.3552        |
| 149.9444 | 2550 | 0.3804        |
| 152.8889 | 2600 | 0.3692        |
| 155.8333 | 2650 | 0.3367        |
| 158.7778 | 2700 | 0.3662        |
| 161.7222 | 2750 | 0.3089        |
| 164.6667 | 2800 | 0.3016        |
| 167.6111 | 2850 | 0.3252        |
| 170.5556 | 2900 | 0.3409        |
| 173.5    | 2950 | 0.3128        |
| 176.4444 | 3000 | 0.3287        |
| 179.3889 | 3050 | 0.3148        |
| 182.3333 | 3100 | 0.3843        |
| 185.2778 | 3150 | 0.2281        |
| 188.2222 | 3200 | 0.2973        |
| 191.1667 | 3250 | 0.2891        |
| 194.1111 | 3300 | 0.3623        |
| 197.0556 | 3350 | 0.3626        |
| 199.9444 | 3400 | 0.2931        |
| 202.8889 | 3450 | 0.2755        |
| 205.8333 | 3500 | 0.2849        |
| 208.7778 | 3550 | 0.2608        |
| 211.7222 | 3600 | 0.3081        |
| 214.6667 | 3650 | 0.2724        |
| 217.6111 | 3700 | 0.2583        |
| 220.5556 | 3750 | 0.3132        |
| 223.5    | 3800 | 0.196         |
| 226.4444 | 3850 | 0.2554        |
| 229.3889 | 3900 | 0.2           |
| 232.3333 | 3950 | 0.2936        |
| 235.2778 | 4000 | 0.2326        |
| 238.2222 | 4050 | 0.2031        |
| 241.1667 | 4100 | 0.2492        |
| 244.1111 | 4150 | 0.2234        |
| 247.0556 | 4200 | 0.3034        |
| 249.9444 | 4250 | 0.2325        |
| 252.8889 | 4300 | 0.2453        |
| 255.8333 | 4350 | 0.2848        |
| 258.7778 | 4400 | 0.2447        |
| 261.7222 | 4450 | 0.2599        |
| 264.6667 | 4500 | 0.2073        |
| 267.6111 | 4550 | 0.2134        |
| 270.5556 | 4600 | 0.1886        |
| 273.5    | 4650 | 0.1229        |
| 276.4444 | 4700 | 0.2147        |
| 279.3889 | 4750 | 0.1993        |
| 282.3333 | 4800 | 0.1814        |
| 285.2778 | 4850 | 0.202         |
| 288.2222 | 4900 | 0.1947        |
| 291.1667 | 4950 | 0.14          |
| 294.1111 | 5000 | 0.2394        |
| 297.0556 | 5050 | 0.1798        |
| 299.9444 | 5100 | 0.1534        |
| 302.8889 | 5150 | 0.2622        |
| 305.8333 | 5200 | 0.1636        |
| 308.7778 | 5250 | 0.1966        |
| 311.7222 | 5300 | 0.1365        |
| 314.6667 | 5350 | 0.1501        |
| 317.6111 | 5400 | 0.1494        |
| 320.5556 | 5450 | 0.1341        |
| 323.5    | 5500 | 0.1791        |
| 326.4444 | 5550 | 0.1609        |
| 329.3889 | 5600 | 0.2268        |
| 332.3333 | 5650 | 0.2145        |
| 335.2778 | 5700 | 0.095         |
| 338.2222 | 5750 | 0.1161        |
| 341.1667 | 5800 | 0.1615        |
| 344.1111 | 5850 | 0.1261        |
| 347.0556 | 5900 | 0.2022        |
| 349.9444 | 5950 | 0.1503        |
| 352.8889 | 6000 | 0.1473        |
| 355.8333 | 6050 | 0.1703        |
| 358.7778 | 6100 | 0.1441        |
| 361.7222 | 6150 | 0.1439        |
| 364.6667 | 6200 | 0.1192        |
| 367.6111 | 6250 | 0.1312        |
| 370.5556 | 6300 | 0.0933        |
| 373.5    | 6350 | 0.1281        |
| 376.4444 | 6400 | 0.1516        |
| 379.3889 | 6450 | 0.1819        |
| 382.3333 | 6500 | 0.1877        |
| 385.2778 | 6550 | 0.1372        |
| 388.2222 | 6600 | 0.1551        |
| 391.1667 | 6650 | 0.1343        |
| 394.1111 | 6700 | 0.2394        |
| 397.0556 | 6750 | 0.1882        |
| 399.9444 | 6800 | 0.1786        |
| 402.8889 | 6850 | 0.125         |
| 405.8333 | 6900 | 0.1059        |
| 408.7778 | 6950 | 0.1414        |
| 411.7222 | 7000 | 0.0593        |
| 414.6667 | 7050 | 0.1037        |
| 417.6111 | 7100 | 0.098         |
| 420.5556 | 7150 | 0.1457        |
| 423.5    | 7200 | 0.1193        |
| 426.4444 | 7250 | 0.1061        |
| 429.3889 | 7300 | 0.1305        |
| 432.3333 | 7350 | 0.1416        |
| 435.2778 | 7400 | 0.1117        |
| 438.2222 | 7450 | 0.1003        |
| 441.1667 | 7500 | 0.1217        |
| 444.1111 | 7550 | 0.0872        |
| 447.0556 | 7600 | 0.1219        |
| 449.9444 | 7650 | 0.1061        |
| 452.8889 | 7700 | 0.1559        |
| 455.8333 | 7750 | 0.1599        |
| 458.7778 | 7800 | 0.1436        |
| 461.7222 | 7850 | 0.1207        |
| 464.6667 | 7900 | 0.1272        |
| 467.6111 | 7950 | 0.1048        |
| 470.5556 | 8000 | 0.1216        |
| 473.5    | 8050 | 0.133         |
| 476.4444 | 8100 | 0.0971        |
| 479.3889 | 8150 | 0.154         |
| 482.3333 | 8200 | 0.0697        |
| 485.2778 | 8250 | 0.136         |
| 488.2222 | 8300 | 0.1315        |
| 491.1667 | 8350 | 0.1103        |
| 494.1111 | 8400 | 0.1065        |
| 497.0556 | 8450 | 0.0784        |
| 499.9444 | 8500 | 0.134         |

</details>

### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.4.1
- 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",
}
```

#### BatchAllTripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification},
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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