Hugging Face's logo

Detomo
/
cl-nagoya-sup-simcse-ja-nss-v1_1

Sentence Similarity
sentence-transformers
ONNX
Safetensors
OpenVINO
bert
feature-extraction
Generated from Trainer
dataset_size:16199
loss:CustomBatchAllTripletLoss
Model card Files
xet
Community
2
vumichien commited on
Commit
183f8bb
·
verified ·
1 Parent(s): 0498048

Add new SentenceTransformer model with an onnx backend

Browse files

Hello!

*This pull request has been automatically generated from the [`push_to_hub`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.push_to_hub) method from the Sentence Transformers library.*

## Full Model Architecture:
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: ORTModelForFeatureExtraction
(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})
)
```

## Tip:
Consider testing this pull request before merging by loading the model from this PR with the `revision` argument:
```python
from sentence_transformers import SentenceTransformer

# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1",
revision=f"refs/pr/{pr_number}",
backend="onnx",
)

# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)

similarities = model.similarity(embeddings, embeddings)
print(similarities)
```

Files changed (10) hide show
  1. 1_Pooling/config.json +9 -9
  2. README.md +409 -409
  3. config.json +25 -24
  4. config_sentence_transformers.json +9 -9
  5. modules.json +13 -13
  6. onnx/model.onnx +3 -0
  7. sentence_bert_config.json +3 -3
  8. special_tokens_map.json +37 -37
  9. tokenizer_config.json +64 -64
  10. vocab.txt +0 -0
1_Pooling/config.json CHANGED
@@ -1,10 +1,10 @@
1
- {
2
- "word_embedding_dimension": 768,
3
- "pooling_mode_cls_token": true,
4
- "pooling_mode_mean_tokens": false,
5
- "pooling_mode_max_tokens": false,
6
- "pooling_mode_mean_sqrt_len_tokens": false,
7
- "pooling_mode_weightedmean_tokens": false,
8
- "pooling_mode_lasttoken": false,
9
- "include_prompt": true
10
  }
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": true,
4
+ "pooling_mode_mean_tokens": false,
5
+ "pooling_mode_max_tokens": false,
6
+ "pooling_mode_mean_sqrt_len_tokens": false,
7
+ "pooling_mode_weightedmean_tokens": false,
8
+ "pooling_mode_lasttoken": false,
9
+ "include_prompt": true
10
  }
README.md CHANGED
@@ -1,410 +1,410 @@
1
- ---
2
- tags:
3
- - sentence-transformers
4
- - sentence-similarity
5
- - feature-extraction
6
- - generated_from_trainer
7
- - dataset_size:16199
8
- - loss:CustomBatchAllTripletLoss
9
- widget:
10
- - source_sentence: 科目：コンクリート。名称：立上り壁ｺﾝｸﾘｰﾄ。
11
- sentences:
12
- - 科目：ユニット及びその他。名称：棚。
13
- - 科目：ユニット及びその他。名称：事務室ｽﾁｰﾙﾊﾟｰﾃｨｼｮﾝ。
14
- - 科目：ユニット及びその他。名称：F-R#収納棚。
15
- - source_sentence: 科目：タイル。名称：段鼻ﾀｲﾙ。
16
- sentences:
17
- - 科目：タイル。名称：巾木磁器質ﾀｲﾙ。
18
- - 科目：タイル。名称：立上りﾀｲﾙA。
19
- - 科目：タイル。名称：ｱﾌﾟﾛｰﾁﾃﾗｽ立上り天端床ﾀｲﾙA。
20
- - source_sentence: 科目：ユニット及びその他。名称：#階F-WC#他ﾊﾟｳﾀﾞｰｶｳﾝﾀｰ。
21
- sentences:
22
- - 科目：ユニット及びその他。名称：便所ﾌｯｸ（二段）。
23
- - 科目：ユニット及びその他。名称：ﾃﾗｽ床ｳｯﾄﾞﾃﾞｯｷ。
24
- - 科目：ユニット及びその他。名称：ﾌﾗｯﾄﾃﾗｽ床ｳｯﾄﾞﾃﾞｯｷ。
25
- - source_sentence: 科目：ユニット及びその他。名称：階数表示+停止階案内ｻｲﾝ。
26
- sentences:
27
- - 科目：ユニット及びその他。名称：ｴﾚﾍﾞｰﾀｰﾎｰﾙ入口ｻｲﾝ。
28
- - 科目：ユニット及びその他。名称：場外離着陸用ｵｲﾙﾄﾗｯﾌﾟ。
29
- - 科目：ユニット及びその他。名称：器材ｶｳﾝﾀｰ。
30
- - source_sentence: 科目：ユニット及びその他。名称：階段内踊場階数ｻｲﾝ。
31
- sentences:
32
- - 科目：ユニット及びその他。名称：F-T#布団収納棚。
33
- - 科目：ユニット及びその他。名称：#F廊下#飾り棚。
34
- - 科目：ユニット及びその他。名称：F-#階理科室#収納棚。
35
- pipeline_tag: sentence-similarity
36
- library_name: sentence-transformers
37
- ---
38
-
39
- # SentenceTransformer
40
-
41
- 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.
42
-
43
- ## Model Details
44
-
45
- ### Model Description
46
- - **Model Type:** Sentence Transformer
47
- <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
48
- - **Maximum Sequence Length:** 512 tokens
49
- - **Output Dimensionality:** 768 dimensions
50
- - **Similarity Function:** Cosine Similarity
51
- <!-- - **Training Dataset:** Unknown -->
52
- <!-- - **Language:** Unknown -->
53
- <!-- - **License:** Unknown -->
54
-
55
- ### Model Sources
56
-
57
- - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
58
- - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
59
- - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
60
-
61
- ### Full Model Architecture
62
-
63
- ```
64
- SentenceTransformer(
65
- (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
66
- (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})
67
- )
68
- ```
69
-
70
- ## Usage
71
-
72
- ### Direct Usage (Sentence Transformers)
73
-
74
- First install the Sentence Transformers library:
75
-
76
- ```bash
77
- pip install -U sentence-transformers
78
- ```
79
-
80
- Then you can load this model and run inference.
81
- ```python
82
- from sentence_transformers import SentenceTransformer
83
-
84
- # Download from the 🤗 Hub
85
- model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1")
86
- # Run inference
87
- sentences = [
88
- '科目：ユニット及びその他。名称：階段内踊場階数ｻｲﾝ。',
89
- '科目：ユニット及びその他。名称：F-#階理科室#収納棚。',
90
- '科目：ユニット及びその他。名称：F-T#布団収納棚。',
91
- ]
92
- embeddings = model.encode(sentences)
93
- print(embeddings.shape)
94
- # [3, 768]
95
-
96
- # Get the similarity scores for the embeddings
97
- similarities = model.similarity(embeddings, embeddings)
98
- print(similarities.shape)
99
- # [3, 3]
100
- ```
101
-
102
- <!--
103
- ### Direct Usage (Transformers)
104
-
105
- <details><summary>Click to see the direct usage in Transformers</summary>
106
-
107
- </details>
108
- -->
109
-
110
- <!--
111
- ### Downstream Usage (Sentence Transformers)
112
-
113
- You can finetune this model on your own dataset.
114
-
115
- <details><summary>Click to expand</summary>
116
-
117
- </details>
118
- -->
119
-
120
- <!--
121
- ### Out-of-Scope Use
122
-
123
- *List how the model may foreseeably be misused and address what users ought not to do with the model.*
124
- -->
125
-
126
- <!--
127
- ## Bias, Risks and Limitations
128
-
129
- *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
130
- -->
131
-
132
- <!--
133
- ### Recommendations
134
-
135
- *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
136
- -->
137
-
138
- ## Training Details
139
-
140
- ### Training Dataset
141
-
142
- #### Unnamed Dataset
143
-
144
- * Size: 16,199 training samples
145
- * Columns: <code>sentence</code> and <code>label</code>
146
- * Approximate statistics based on the first 1000 samples:
147
- | | sentence | label |
148
- |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
149
- | type | string | int |
150
- | details | <ul><li>min: 11 tokens</li><li>mean: 18.73 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~2.40%</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: ~0.40%</li><li>15: ~0.30%</li><li>16: ~0.30%</li><li>17: ~0.30%</li><li>18: ~0.90%</li><li>19: ~0.30%</li><li>20: ~1.30%</li><li>21: ~0.30%</li><li>22: ~1.10%</li><li>23: ~0.30%</li><li>24: ~0.30%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.30%</li><li>30: ~0.30%</li><li>31: ~0.30%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.40%</li><li>41: ~0.30%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.30%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.30%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.80%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.60%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.50%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.50%</li><li>70: ~0.60%</li><li>71: ~0.30%</li><li>72: ~0.30%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.30%</li><li>78: ~0.30%</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: ~0.30%</li><li>107: ~0.70%</li><li>108: ~0.30%</li><li>109: ~3.20%</li><li>110: ~0.30%</li><li>111: ~0.40%</li><li>112: ~2.30%</li><li>113: ~0.30%</li><li>114: ~0.30%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.40%</li><li>119: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.30%</li><li>122: ~0.80%</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: ~0.50%</li><li>132: ~0.30%</li><li>133: ~0.40%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.30%</li><li>144: ~0.40%</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: ~0.30%</li><li>151: ~0.70%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~0.30%</li><li>155: ~1.30%</li><li>156: ~0.30%</li><li>157: ~0.40%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~0.30%</li><li>161: ~1.50%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.30%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~0.30%</li><li>169: ~1.50%</li><li>170: ~0.30%</li><li>171: ~0.30%</li><li>172: ~7.20%</li><li>173: ~0.30%</li><li>174: ~1.00%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~0.30%</li><li>178: ~1.80%</li><li>179: ~0.30%</li><li>180: ~0.50%</li><li>181: ~0.70%</li><li>182: ~0.30%</li><li>183: ~0.30%</li></ul> |
151
- * Samples:
152
- | sentence | label |
153
- |:-----------------------------------------|:---------------|
154
- | <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> |
155
- | <code>科目：��ンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
156
- | <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> |
157
- * Loss: <code>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code>
158
-
159
- ### Training Hyperparameters
160
- #### Non-Default Hyperparameters
161
-
162
- - `per_device_train_batch_size`: 512
163
- - `per_device_eval_batch_size`: 512
164
- - `learning_rate`: 1e-05
165
- - `weight_decay`: 0.01
166
- - `num_train_epochs`: 250
167
- - `warmup_ratio`: 0.1
168
- - `fp16`: True
169
- - `batch_sampler`: group_by_label
170
-
171
- #### All Hyperparameters
172
- <details><summary>Click to expand</summary>
173
-
174
- - `overwrite_output_dir`: False
175
- - `do_predict`: False
176
- - `eval_strategy`: no
177
- - `prediction_loss_only`: True
178
- - `per_device_train_batch_size`: 512
179
- - `per_device_eval_batch_size`: 512
180
- - `per_gpu_train_batch_size`: None
181
- - `per_gpu_eval_batch_size`: None
182
- - `gradient_accumulation_steps`: 1
183
- - `eval_accumulation_steps`: None
184
- - `torch_empty_cache_steps`: None
185
- - `learning_rate`: 1e-05
186
- - `weight_decay`: 0.01
187
- - `adam_beta1`: 0.9
188
- - `adam_beta2`: 0.999
189
- - `adam_epsilon`: 1e-08
190
- - `max_grad_norm`: 1.0
191
- - `num_train_epochs`: 250
192
- - `max_steps`: -1
193
- - `lr_scheduler_type`: linear
194
- - `lr_scheduler_kwargs`: {}
195
- - `warmup_ratio`: 0.1
196
- - `warmup_steps`: 0
197
- - `log_level`: passive
198
- - `log_level_replica`: warning
199
- - `log_on_each_node`: True
200
- - `logging_nan_inf_filter`: True
201
- - `save_safetensors`: True
202
- - `save_on_each_node`: False
203
- - `save_only_model`: False
204
- - `restore_callback_states_from_checkpoint`: False
205
- - `no_cuda`: False
206
- - `use_cpu`: False
207
- - `use_mps_device`: False
208
- - `seed`: 42
209
- - `data_seed`: None
210
- - `jit_mode_eval`: False
211
- - `use_ipex`: False
212
- - `bf16`: False
213
- - `fp16`: True
214
- - `fp16_opt_level`: O1
215
- - `half_precision_backend`: auto
216
- - `bf16_full_eval`: False
217
- - `fp16_full_eval`: False
218
- - `tf32`: None
219
- - `local_rank`: 0
220
- - `ddp_backend`: None
221
- - `tpu_num_cores`: None
222
- - `tpu_metrics_debug`: False
223
- - `debug`: []
224
- - `dataloader_drop_last`: False
225
- - `dataloader_num_workers`: 0
226
- - `dataloader_prefetch_factor`: None
227
- - `past_index`: -1
228
- - `disable_tqdm`: False
229
- - `remove_unused_columns`: True
230
- - `label_names`: None
231
- - `load_best_model_at_end`: False
232
- - `ignore_data_skip`: False
233
- - `fsdp`: []
234
- - `fsdp_min_num_params`: 0
235
- - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
236
- - `tp_size`: 0
237
- - `fsdp_transformer_layer_cls_to_wrap`: None
238
- - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
239
- - `deepspeed`: None
240
- - `label_smoothing_factor`: 0.0
241
- - `optim`: adamw_torch
242
- - `optim_args`: None
243
- - `adafactor`: False
244
- - `group_by_length`: False
245
- - `length_column_name`: length
246
- - `ddp_find_unused_parameters`: None
247
- - `ddp_bucket_cap_mb`: None
248
- - `ddp_broadcast_buffers`: False
249
- - `dataloader_pin_memory`: True
250
- - `dataloader_persistent_workers`: False
251
- - `skip_memory_metrics`: True
252
- - `use_legacy_prediction_loop`: False
253
- - `push_to_hub`: False
254
- - `resume_from_checkpoint`: None
255
- - `hub_model_id`: None
256
- - `hub_strategy`: every_save
257
- - `hub_private_repo`: None
258
- - `hub_always_push`: False
259
- - `gradient_checkpointing`: False
260
- - `gradient_checkpointing_kwargs`: None
261
- - `include_inputs_for_metrics`: False
262
- - `include_for_metrics`: []
263
- - `eval_do_concat_batches`: True
264
- - `fp16_backend`: auto
265
- - `push_to_hub_model_id`: None
266
- - `push_to_hub_organization`: None
267
- - `mp_parameters`:
268
- - `auto_find_batch_size`: False
269
- - `full_determinism`: False
270
- - `torchdynamo`: None
271
- - `ray_scope`: last
272
- - `ddp_timeout`: 1800
273
- - `torch_compile`: False
274
- - `torch_compile_backend`: None
275
- - `torch_compile_mode`: None
276
- - `include_tokens_per_second`: False
277
- - `include_num_input_tokens_seen`: False
278
- - `neftune_noise_alpha`: None
279
- - `optim_target_modules`: None
280
- - `batch_eval_metrics`: False
281
- - `eval_on_start`: False
282
- - `use_liger_kernel`: False
283
- - `eval_use_gather_object`: False
284
- - `average_tokens_across_devices`: False
285
- - `prompts`: None
286
- - `batch_sampler`: group_by_label
287
- - `multi_dataset_batch_sampler`: proportional
288
-
289
- </details>
290
-
291
- ### Training Logs
292
- | Epoch | Step | Training Loss |
293
- |:-------:|:----:|:-------------:|
294
- | 4.125 | 100 | 0.0682 |
295
- | 8.25 | 200 | 0.0745 |
296
- | 12.375 | 300 | 0.0764 |
297
- | 16.5 | 400 | 0.0778 |
298
- | 20.625 | 500 | 0.077 |
299
- | 24.75 | 600 | 0.0767 |
300
- | 29.125 | 700 | 0.0738 |
301
- | 33.25 | 800 | 0.0701 |
302
- | 37.375 | 900 | 0.0677 |
303
- | 41.5 | 1000 | 0.0689 |
304
- | 45.625 | 1100 | 0.0661 |
305
- | 49.75 | 1200 | 0.0677 |
306
- | 54.125 | 1300 | 0.0627 |
307
- | 58.25 | 1400 | 0.0629 |
308
- | 62.375 | 1500 | 0.0625 |
309
- | 66.5 | 1600 | 0.0655 |
310
- | 70.625 | 1700 | 0.0645 |
311
- | 74.75 | 1800 | 0.0595 |
312
- | 79.125 | 1900 | 0.0608 |
313
- | 83.25 | 2000 | 0.0614 |
314
- | 87.375 | 2100 | 0.0567 |
315
- | 91.5 | 2200 | 0.0612 |
316
- | 95.625 | 2300 | 0.0599 |
317
- | 99.75 | 2400 | 0.059 |
318
- | 104.125 | 2500 | 0.0547 |
319
- | 108.25 | 2600 | 0.0571 |
320
- | 112.375 | 2700 | 0.0543 |
321
- | 116.5 | 2800 | 0.0574 |
322
- | 120.625 | 2900 | 0.0561 |
323
- | 124.75 | 3000 | 0.0534 |
324
- | 129.125 | 3100 | 0.0554 |
325
- | 133.25 | 3200 | 0.0507 |
326
- | 137.375 | 3300 | 0.0533 |
327
- | 141.5 | 3400 | 0.05 |
328
- | 145.625 | 3500 | 0.0569 |
329
- | 149.75 | 3600 | 0.0551 |
330
- | 154.125 | 3700 | 0.0558 |
331
- | 158.25 | 3800 | 0.0539 |
332
- | 162.375 | 3900 | 0.0498 |
333
- | 166.5 | 4000 | 0.0512 |
334
- | 170.625 | 4100 | 0.0481 |
335
- | 174.75 | 4200 | 0.0492 |
336
- | 179.125 | 4300 | 0.0513 |
337
- | 183.25 | 4400 | 0.0474 |
338
- | 187.375 | 4500 | 0.0491 |
339
- | 191.5 | 4600 | 0.0513 |
340
- | 195.625 | 4700 | 0.0453 |
341
- | 199.75 | 4800 | 0.0453 |
342
- | 204.125 | 4900 | 0.0489 |
343
- | 208.25 | 5000 | 0.0481 |
344
- | 212.375 | 5100 | 0.0498 |
345
- | 216.5 | 5200 | 0.044 |
346
- | 220.625 | 5300 | 0.0486 |
347
- | 224.75 | 5400 | 0.0399 |
348
- | 229.125 | 5500 | 0.0384 |
349
- | 233.25 | 5600 | 0.0428 |
350
- | 237.375 | 5700 | 0.0447 |
351
- | 241.5 | 5800 | 0.0479 |
352
- | 245.625 | 5900 | 0.0434 |
353
- | 249.75 | 6000 | 0.0442 |
354
-
355
-
356
- ### Framework Versions
357
- - Python: 3.11.12
358
- - Sentence Transformers: 3.4.1
359
- - Transformers: 4.51.3
360
- - PyTorch: 2.6.0+cu124
361
- - Accelerate: 1.5.2
362
- - Datasets: 3.5.0
363
- - Tokenizers: 0.21.1
364
-
365
- ## Citation
366
-
367
- ### BibTeX
368
-
369
- #### Sentence Transformers
370
- ```bibtex
371
- @inproceedings{reimers-2019-sentence-bert,
372
- title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
373
- author = "Reimers, Nils and Gurevych, Iryna",
374
- booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
375
- month = "11",
376
- year = "2019",
377
- publisher = "Association for Computational Linguistics",
378
- url = "https://arxiv.org/abs/1908.10084",
379
- }
380
- ```
381
-
382
- #### CustomBatchAllTripletLoss
383
- ```bibtex
384
- @misc{hermans2017defense,
385
- title={In Defense of the Triplet Loss for Person Re-Identification},
386
- author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
387
- year={2017},
388
- eprint={1703.07737},
389
- archivePrefix={arXiv},
390
- primaryClass={cs.CV}
391
- }
392
- ```
393
-
394
- <!--
395
- ## Glossary
396
-
397
- *Clearly define terms in order to be accessible across audiences.*
398
- -->
399
-
400
- <!--
401
- ## Model Card Authors
402
-
403
- *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
404
- -->
405
-
406
- <!--
407
- ## Model Card Contact
408
-
409
- *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
410
  -->
 
1
+ ---
2
+ tags:
3
+ - sentence-transformers
4
+ - sentence-similarity
5
+ - feature-extraction
6
+ - generated_from_trainer
7
+ - dataset_size:16199
8
+ - loss:CustomBatchAllTripletLoss
9
+ widget:
10
+ - source_sentence: 科目：コンクリート。名称：立上り壁ｺﾝｸﾘｰﾄ。
11
+ sentences:
12
+ - 科目：ユニット及びその他。名称：棚。
13
+ - 科目：ユニット及びその他。名称：事務室ｽﾁｰﾙﾊﾟｰﾃｨｼｮﾝ。
14
+ - 科目：ユニット及びその他。名称：F-R#収納棚。
15
+ - source_sentence: 科目：タイル。名称：段鼻ﾀｲﾙ。
16
+ sentences:
17
+ - 科目：タイル。名称：巾木磁器質ﾀｲﾙ。
18
+ - 科目：タイル。名称：立上りﾀｲﾙA。
19
+ - 科目：タイル。名称：ｱﾌﾟﾛｰﾁﾃﾗｽ立上り天端床ﾀｲﾙA。
20
+ - source_sentence: 科目：ユニット及びその他。名称：#階F-WC#他ﾊﾟｳﾀﾞｰｶｳﾝﾀｰ。
21
+ sentences:
22
+ - 科目：ユニット及びその他。名称：便所ﾌｯｸ（二段）。
23
+ - 科目：ユニット及びその他。名称：ﾃﾗｽ床ｳｯﾄﾞﾃﾞｯｷ。
24
+ - 科目：ユニット及びその他。名称：ﾌﾗｯﾄﾃﾗｽ床ｳｯﾄﾞﾃﾞｯｷ。
25
+ - source_sentence: 科目：ユニット及びその他。名称：階数表示+停止階案内ｻｲﾝ。
26
+ sentences:
27
+ - 科目：ユニット及びその他。名称：ｴﾚﾍﾞｰﾀｰﾎｰﾙ入口ｻｲﾝ。
28
+ - 科目：ユニット及びその他。名称：場外離着陸用ｵｲﾙﾄﾗｯﾌﾟ。
29
+ - 科目：ユニット及びその他。名称：器材ｶｳﾝﾀｰ。
30
+ - source_sentence: 科目：ユニット及びその他。名称：階段内踊場階数ｻｲﾝ。
31
+ sentences:
32
+ - 科目：ユニット及びその他。名称：F-T#布団収納棚。
33
+ - 科目：ユニット及びその他。名称：#F廊下#飾り棚。
34
+ - 科目：ユニット及びその他。名称：F-#階理科室#収納棚。
35
+ pipeline_tag: sentence-similarity
36
+ library_name: sentence-transformers
37
+ ---
38
+
39
+ # SentenceTransformer
40
+
41
+ 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.
42
+
43
+ ## Model Details
44
+
45
+ ### Model Description
46
+ - **Model Type:** Sentence Transformer
47
+ <!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
48
+ - **Maximum Sequence Length:** 512 tokens
49
+ - **Output Dimensionality:** 768 dimensions
50
+ - **Similarity Function:** Cosine Similarity
51
+ <!-- - **Training Dataset:** Unknown -->
52
+ <!-- - **Language:** Unknown -->
53
+ <!-- - **License:** Unknown -->
54
+
55
+ ### Model Sources
56
+
57
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
58
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
59
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
60
+
61
+ ### Full Model Architecture
62
+
63
+ ```
64
+ SentenceTransformer(
65
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
66
+ (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})
67
+ )
68
+ ```
69
+
70
+ ## Usage
71
+
72
+ ### Direct Usage (Sentence Transformers)
73
+
74
+ First install the Sentence Transformers library:
75
+
76
+ ```bash
77
+ pip install -U sentence-transformers
78
+ ```
79
+
80
+ Then you can load this model and run inference.
81
+ ```python
82
+ from sentence_transformers import SentenceTransformer
83
+
84
+ # Download from the 🤗 Hub
85
+ model = SentenceTransformer("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1")
86
+ # Run inference
87
+ sentences = [
88
+ '科目：ユニット及びその他。名称：階段内踊場階数ｻｲﾝ。',
89
+ '科目：ユニット及びその他。名称：F-#階理科室#収納棚。',
90
+ '科目：ユニット及びその他。名称：F-T#布団収納棚。',
91
+ ]
92
+ embeddings = model.encode(sentences)
93
+ print(embeddings.shape)
94
+ # [3, 768]
95
+
96
+ # Get the similarity scores for the embeddings
97
+ similarities = model.similarity(embeddings, embeddings)
98
+ print(similarities.shape)
99
+ # [3, 3]
100
+ ```
101
+
102
+ <!--
103
+ ### Direct Usage (Transformers)
104
+
105
+ <details><summary>Click to see the direct usage in Transformers</summary>
106
+
107
+ </details>
108
+ -->
109
+
110
+ <!--
111
+ ### Downstream Usage (Sentence Transformers)
112
+
113
+ You can finetune this model on your own dataset.
114
+
115
+ <details><summary>Click to expand</summary>
116
+
117
+ </details>
118
+ -->
119
+
120
+ <!--
121
+ ### Out-of-Scope Use
122
+
123
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
124
+ -->
125
+
126
+ <!--
127
+ ## Bias, Risks and Limitations
128
+
129
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
130
+ -->
131
+
132
+ <!--
133
+ ### Recommendations
134
+
135
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
136
+ -->
137
+
138
+ ## Training Details
139
+
140
+ ### Training Dataset
141
+
142
+ #### Unnamed Dataset
143
+
144
+ * Size: 16,199 training samples
145
+ * Columns: <code>sentence</code> and <code>label</code>
146
+ * Approximate statistics based on the first 1000 samples:
147
+ | | sentence | label |
148
+ |:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
149
+ | type | string | int |
150
+ | details | <ul><li>min: 11 tokens</li><li>mean: 18.73 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~2.40%</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: ~0.40%</li><li>15: ~0.30%</li><li>16: ~0.30%</li><li>17: ~0.30%</li><li>18: ~0.90%</li><li>19: ~0.30%</li><li>20: ~1.30%</li><li>21: ~0.30%</li><li>22: ~1.10%</li><li>23: ~0.30%</li><li>24: ~0.30%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.30%</li><li>30: ~0.30%</li><li>31: ~0.30%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.40%</li><li>41: ~0.30%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.30%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.30%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.80%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.60%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.50%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.50%</li><li>70: ~0.60%</li><li>71: ~0.30%</li><li>72: ~0.30%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.30%</li><li>78: ~0.30%</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: ~0.30%</li><li>107: ~0.70%</li><li>108: ~0.30%</li><li>109: ~3.20%</li><li>110: ~0.30%</li><li>111: ~0.40%</li><li>112: ~2.30%</li><li>113: ~0.30%</li><li>114: ~0.30%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.40%</li><li>119: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.30%</li><li>122: ~0.80%</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: ~0.50%</li><li>132: ~0.30%</li><li>133: ~0.40%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.30%</li><li>144: ~0.40%</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: ~0.30%</li><li>151: ~0.70%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~0.30%</li><li>155: ~1.30%</li><li>156: ~0.30%</li><li>157: ~0.40%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~0.30%</li><li>161: ~1.50%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.30%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~0.30%</li><li>169: ~1.50%</li><li>170: ~0.30%</li><li>171: ~0.30%</li><li>172: ~7.20%</li><li>173: ~0.30%</li><li>174: ~1.00%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~0.30%</li><li>178: ~1.80%</li><li>179: ~0.30%</li><li>180: ~0.50%</li><li>181: ~0.70%</li><li>182: ~0.30%</li><li>183: ~0.30%</li></ul> |
151
+ * Samples:
152
+ | sentence | label |
153
+ |:-----------------------------------------|:---------------|
154
+ | <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> |
155
+ | <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> |
156
+ | <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> |
157
+ * Loss: <code>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code>
158
+
159
+ ### Training Hyperparameters
160
+ #### Non-Default Hyperparameters
161
+
162
+ - `per_device_train_batch_size`: 512
163
+ - `per_device_eval_batch_size`: 512
164
+ - `learning_rate`: 1e-05
165
+ - `weight_decay`: 0.01
166
+ - `num_train_epochs`: 250
167
+ - `warmup_ratio`: 0.1
168
+ - `fp16`: True
169
+ - `batch_sampler`: group_by_label
170
+
171
+ #### All Hyperparameters
172
+ <details><summary>Click to expand</summary>
173
+
174
+ - `overwrite_output_dir`: False
175
+ - `do_predict`: False
176
+ - `eval_strategy`: no
177
+ - `prediction_loss_only`: True
178
+ - `per_device_train_batch_size`: 512
179
+ - `per_device_eval_batch_size`: 512
180
+ - `per_gpu_train_batch_size`: None
181
+ - `per_gpu_eval_batch_size`: None
182
+ - `gradient_accumulation_steps`: 1
183
+ - `eval_accumulation_steps`: None
184
+ - `torch_empty_cache_steps`: None
185
+ - `learning_rate`: 1e-05
186
+ - `weight_decay`: 0.01
187
+ - `adam_beta1`: 0.9
188
+ - `adam_beta2`: 0.999
189
+ - `adam_epsilon`: 1e-08
190
+ - `max_grad_norm`: 1.0
191
+ - `num_train_epochs`: 250
192
+ - `max_steps`: -1
193
+ - `lr_scheduler_type`: linear
194
+ - `lr_scheduler_kwargs`: {}
195
+ - `warmup_ratio`: 0.1
196
+ - `warmup_steps`: 0
197
+ - `log_level`: passive
198
+ - `log_level_replica`: warning
199
+ - `log_on_each_node`: True
200
+ - `logging_nan_inf_filter`: True
201
+ - `save_safetensors`: True
202
+ - `save_on_each_node`: False
203
+ - `save_only_model`: False
204
+ - `restore_callback_states_from_checkpoint`: False
205
+ - `no_cuda`: False
206
+ - `use_cpu`: False
207
+ - `use_mps_device`: False
208
+ - `seed`: 42
209
+ - `data_seed`: None
210
+ - `jit_mode_eval`: False
211
+ - `use_ipex`: False
212
+ - `bf16`: False
213
+ - `fp16`: True
214
+ - `fp16_opt_level`: O1
215
+ - `half_precision_backend`: auto
216
+ - `bf16_full_eval`: False
217
+ - `fp16_full_eval`: False
218
+ - `tf32`: None
219
+ - `local_rank`: 0
220
+ - `ddp_backend`: None
221
+ - `tpu_num_cores`: None
222
+ - `tpu_metrics_debug`: False
223
+ - `debug`: []
224
+ - `dataloader_drop_last`: False
225
+ - `dataloader_num_workers`: 0
226
+ - `dataloader_prefetch_factor`: None
227
+ - `past_index`: -1
228
+ - `disable_tqdm`: False
229
+ - `remove_unused_columns`: True
230
+ - `label_names`: None
231
+ - `load_best_model_at_end`: False
232
+ - `ignore_data_skip`: False
233
+ - `fsdp`: []
234
+ - `fsdp_min_num_params`: 0
235
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
236
+ - `tp_size`: 0
237
+ - `fsdp_transformer_layer_cls_to_wrap`: None
238
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
239
+ - `deepspeed`: None
240
+ - `label_smoothing_factor`: 0.0
241
+ - `optim`: adamw_torch
242
+ - `optim_args`: None
243
+ - `adafactor`: False
244
+ - `group_by_length`: False
245
+ - `length_column_name`: length
246
+ - `ddp_find_unused_parameters`: None
247
+ - `ddp_bucket_cap_mb`: None
248
+ - `ddp_broadcast_buffers`: False
249
+ - `dataloader_pin_memory`: True
250
+ - `dataloader_persistent_workers`: False
251
+ - `skip_memory_metrics`: True
252
+ - `use_legacy_prediction_loop`: False
253
+ - `push_to_hub`: False
254
+ - `resume_from_checkpoint`: None
255
+ - `hub_model_id`: None
256
+ - `hub_strategy`: every_save
257
+ - `hub_private_repo`: None
258
+ - `hub_always_push`: False
259
+ - `gradient_checkpointing`: False
260
+ - `gradient_checkpointing_kwargs`: None
261
+ - `include_inputs_for_metrics`: False
262
+ - `include_for_metrics`: []
263
+ - `eval_do_concat_batches`: True
264
+ - `fp16_backend`: auto
265
+ - `push_to_hub_model_id`: None
266
+ - `push_to_hub_organization`: None
267
+ - `mp_parameters`:
268
+ - `auto_find_batch_size`: False
269
+ - `full_determinism`: False
270
+ - `torchdynamo`: None
271
+ - `ray_scope`: last
272
+ - `ddp_timeout`: 1800
273
+ - `torch_compile`: False
274
+ - `torch_compile_backend`: None
275
+ - `torch_compile_mode`: None
276
+ - `include_tokens_per_second`: False
277
+ - `include_num_input_tokens_seen`: False
278
+ - `neftune_noise_alpha`: None
279
+ - `optim_target_modules`: None
280
+ - `batch_eval_metrics`: False
281
+ - `eval_on_start`: False
282
+ - `use_liger_kernel`: False
283
+ - `eval_use_gather_object`: False
284
+ - `average_tokens_across_devices`: False
285
+ - `prompts`: None
286
+ - `batch_sampler`: group_by_label
287
+ - `multi_dataset_batch_sampler`: proportional
288
+
289
+ </details>
290
+
291
+ ### Training Logs
292
+ | Epoch | Step | Training Loss |
293
+ |:-------:|:----:|:-------------:|
294
+ | 4.125 | 100 | 0.0682 |
295
+ | 8.25 | 200 | 0.0745 |
296
+ | 12.375 | 300 | 0.0764 |
297
+ | 16.5 | 400 | 0.0778 |
298
+ | 20.625 | 500 | 0.077 |
299
+ | 24.75 | 600 | 0.0767 |
300
+ | 29.125 | 700 | 0.0738 |
301
+ | 33.25 | 800 | 0.0701 |
302
+ | 37.375 | 900 | 0.0677 |
303
+ | 41.5 | 1000 | 0.0689 |
304
+ | 45.625 | 1100 | 0.0661 |
305
+ | 49.75 | 1200 | 0.0677 |
306
+ | 54.125 | 1300 | 0.0627 |
307
+ | 58.25 | 1400 | 0.0629 |
308
+ | 62.375 | 1500 | 0.0625 |
309
+ | 66.5 | 1600 | 0.0655 |
310
+ | 70.625 | 1700 | 0.0645 |
311
+ | 74.75 | 1800 | 0.0595 |
312
+ | 79.125 | 1900 | 0.0608 |
313
+ | 83.25 | 2000 | 0.0614 |
314
+ | 87.375 | 2100 | 0.0567 |
315
+ | 91.5 | 2200 | 0.0612 |
316
+ | 95.625 | 2300 | 0.0599 |
317
+ | 99.75 | 2400 | 0.059 |
318
+ | 104.125 | 2500 | 0.0547 |
319
+ | 108.25 | 2600 | 0.0571 |
320
+ | 112.375 | 2700 | 0.0543 |
321
+ | 116.5 | 2800 | 0.0574 |
322
+ | 120.625 | 2900 | 0.0561 |
323
+ | 124.75 | 3000 | 0.0534 |
324
+ | 129.125 | 3100 | 0.0554 |
325
+ | 133.25 | 3200 | 0.0507 |
326
+ | 137.375 | 3300 | 0.0533 |
327
+ | 141.5 | 3400 | 0.05 |
328
+ | 145.625 | 3500 | 0.0569 |
329
+ | 149.75 | 3600 | 0.0551 |
330
+ | 154.125 | 3700 | 0.0558 |
331
+ | 158.25 | 3800 | 0.0539 |
332
+ | 162.375 | 3900 | 0.0498 |
333
+ | 166.5 | 4000 | 0.0512 |
334
+ | 170.625 | 4100 | 0.0481 |
335
+ | 174.75 | 4200 | 0.0492 |
336
+ | 179.125 | 4300 | 0.0513 |
337
+ | 183.25 | 4400 | 0.0474 |
338
+ | 187.375 | 4500 | 0.0491 |
339
+ | 191.5 | 4600 | 0.0513 |
340
+ | 195.625 | 4700 | 0.0453 |
341
+ | 199.75 | 4800 | 0.0453 |
342
+ | 204.125 | 4900 | 0.0489 |
343
+ | 208.25 | 5000 | 0.0481 |
344
+ | 212.375 | 5100 | 0.0498 |
345
+ | 216.5 | 5200 | 0.044 |
346
+ | 220.625 | 5300 | 0.0486 |
347
+ | 224.75 | 5400 | 0.0399 |
348
+ | 229.125 | 5500 | 0.0384 |
349
+ | 233.25 | 5600 | 0.0428 |
350
+ | 237.375 | 5700 | 0.0447 |
351
+ | 241.5 | 5800 | 0.0479 |
352
+ | 245.625 | 5900 | 0.0434 |
353
+ | 249.75 | 6000 | 0.0442 |
354
+
355
+
356
+ ### Framework Versions
357
+ - Python: 3.11.12
358
+ - Sentence Transformers: 3.4.1
359
+ - Transformers: 4.51.3
360
+ - PyTorch: 2.6.0+cu124
361
+ - Accelerate: 1.5.2
362
+ - Datasets: 3.5.0
363
+ - Tokenizers: 0.21.1
364
+
365
+ ## Citation
366
+
367
+ ### BibTeX
368
+
369
+ #### Sentence Transformers
370
+ ```bibtex
371
+ @inproceedings{reimers-2019-sentence-bert,
372
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
373
+ author = "Reimers, Nils and Gurevych, Iryna",
374
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
375
+ month = "11",
376
+ year = "2019",
377
+ publisher = "Association for Computational Linguistics",
378
+ url = "https://arxiv.org/abs/1908.10084",
379
+ }
380
+ ```
381
+
382
+ #### CustomBatchAllTripletLoss
383
+ ```bibtex
384
+ @misc{hermans2017defense,
385
+ title={In Defense of the Triplet Loss for Person Re-Identification},
386
+ author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
387
+ year={2017},
388
+ eprint={1703.07737},
389
+ archivePrefix={arXiv},
390
+ primaryClass={cs.CV}
391
+ }
392
+ ```
393
+
394
+ <!--
395
+ ## Glossary
396
+
397
+ *Clearly define terms in order to be accessible across audiences.*
398
+ -->
399
+
400
+ <!--
401
+ ## Model Card Authors
402
+
403
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
404
+ -->
405
+
406
+ <!--
407
+ ## Model Card Contact
408
+
409
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
410
  -->
config.json CHANGED
@@ -1,24 +1,25 @@
1
- {
2
- "architectures": [
3
- "BertModel"
4
- ],
5
- "attention_probs_dropout_prob": 0.1,
6
- "classifier_dropout": null,
7
- "hidden_act": "gelu",
8
- "hidden_dropout_prob": 0.1,
9
- "hidden_size": 768,
10
- "initializer_range": 0.02,
11
- "intermediate_size": 3072,
12
- "layer_norm_eps": 1e-12,
13
- "max_position_embeddings": 512,
14
- "model_type": "bert",
15
- "num_attention_heads": 12,
16
- "num_hidden_layers": 12,
17
- "pad_token_id": 0,
18
- "position_embedding_type": "absolute",
19
- "torch_dtype": "float32",
20
- "transformers_version": "4.51.3",
21
- "type_vocab_size": 2,
22
- "use_cache": true,
23
- "vocab_size": 32768
24
- }
 
 
1
+ {
2
+ "_name_or_path": "C:/Project/Detomo/2025/meisai-check/meisai-api/meisaicheck-api/data/model\\Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1-onnx-quantized",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "classifier_dropout": null,
8
+ "hidden_act": "gelu",
9
+ "hidden_dropout_prob": 0.1,
10
+ "hidden_size": 768,
11
+ "initializer_range": 0.02,
12
+ "intermediate_size": 3072,
13
+ "layer_norm_eps": 1e-12,
14
+ "max_position_embeddings": 512,
15
+ "model_type": "bert",
16
+ "num_attention_heads": 12,
17
+ "num_hidden_layers": 12,
18
+ "pad_token_id": 0,
19
+ "position_embedding_type": "absolute",
20
+ "torch_dtype": "float32",
21
+ "transformers_version": "4.48.3",
22
+ "type_vocab_size": 2,
23
+ "use_cache": true,
24
+ "vocab_size": 32768
25
+ }
config_sentence_transformers.json CHANGED
@@ -1,10 +1,10 @@
1
- {
2
- "__version__": {
3
- "sentence_transformers": "3.4.1",
4
- "transformers": "4.51.3",
5
- "pytorch": "2.6.0+cu124"
6
- },
7
- "prompts": {},
8
- "default_prompt_name": null,
9
- "similarity_fn_name": "cosine"
10
  }
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.3.1",
4
+ "transformers": "4.48.3",
5
+ "pytorch": "2.6.0+cu126"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": "cosine"
10
  }
modules.json CHANGED
@@ -1,14 +1,14 @@
1
- [
2
- {
3
- "idx": 0,
4
- "name": "0",
5
- "path": "",
6
- "type": "sentence_transformers.models.Transformer"
7
- },
8
- {
9
- "idx": 1,
10
- "name": "1",
11
- "path": "1_Pooling",
12
- "type": "sentence_transformers.models.Pooling"
13
- }
14
  ]
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ }
14
  ]
onnx/model.onnx ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1391fa06bbe6a33b9fbb2be41be5a3316d8d3f7d920df1c7e469750450b249cb
3
+ size 442744328
sentence_bert_config.json CHANGED
@@ -1,4 +1,4 @@
1
- {
2
- "max_seq_length": 512,
3
- "do_lower_case": false
4
  }
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": false
4
  }
special_tokens_map.json CHANGED
@@ -1,37 +1,37 @@
1
- {
2
- "cls_token": {
3
- "content": "[CLS]",
4
- "lstrip": false,
5
- "normalized": false,
6
- "rstrip": false,
7
- "single_word": false
8
- },
9
- "mask_token": {
10
- "content": "[MASK]",
11
- "lstrip": false,
12
- "normalized": false,
13
- "rstrip": false,
14
- "single_word": false
15
- },
16
- "pad_token": {
17
- "content": "[PAD]",
18
- "lstrip": false,
19
- "normalized": false,
20
- "rstrip": false,
21
- "single_word": false
22
- },
23
- "sep_token": {
24
- "content": "[SEP]",
25
- "lstrip": false,
26
- "normalized": false,
27
- "rstrip": false,
28
- "single_word": false
29
- },
30
- "unk_token": {
31
- "content": "[UNK]",
32
- "lstrip": false,
33
- "normalized": false,
34
- "rstrip": false,
35
- "single_word": false
36
- }
37
- }
 
1
+ {
2
+ "cls_token": {
3
+ "content": "[CLS]",
4
+ "lstrip": false,
5
+ "normalized": false,
6
+ "rstrip": false,
7
+ "single_word": false
8
+ },
9
+ "mask_token": {
10
+ "content": "[MASK]",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false
15
+ },
16
+ "pad_token": {
17
+ "content": "[PAD]",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ },
23
+ "sep_token": {
24
+ "content": "[SEP]",
25
+ "lstrip": false,
26
+ "normalized": false,
27
+ "rstrip": false,
28
+ "single_word": false
29
+ },
30
+ "unk_token": {
31
+ "content": "[UNK]",
32
+ "lstrip": false,
33
+ "normalized": false,
34
+ "rstrip": false,
35
+ "single_word": false
36
+ }
37
+ }
tokenizer_config.json CHANGED
@@ -1,64 +1,64 @@
1
- {
2
- "added_tokens_decoder": {
3
- "0": {
4
- "content": "[PAD]",
5
- "lstrip": false,
6
- "normalized": false,
7
- "rstrip": false,
8
- "single_word": false,
9
- "special": true
10
- },
11
- "1": {
12
- "content": "[UNK]",
13
- "lstrip": false,
14
- "normalized": false,
15
- "rstrip": false,
16
- "single_word": false,
17
- "special": true
18
- },
19
- "2": {
20
- "content": "[CLS]",
21
- "lstrip": false,
22
- "normalized": false,
23
- "rstrip": false,
24
- "single_word": false,
25
- "special": true
26
- },
27
- "3": {
28
- "content": "[SEP]",
29
- "lstrip": false,
30
- "normalized": false,
31
- "rstrip": false,
32
- "single_word": false,
33
- "special": true
34
- },
35
- "4": {
36
- "content": "[MASK]",
37
- "lstrip": false,
38
- "normalized": false,
39
- "rstrip": false,
40
- "single_word": false,
41
- "special": true
42
- }
43
- },
44
- "clean_up_tokenization_spaces": true,
45
- "cls_token": "[CLS]",
46
- "do_lower_case": false,
47
- "do_subword_tokenize": true,
48
- "do_word_tokenize": true,
49
- "extra_special_tokens": {},
50
- "jumanpp_kwargs": null,
51
- "mask_token": "[MASK]",
52
- "mecab_kwargs": {
53
- "mecab_dic": "unidic_lite"
54
- },
55
- "model_max_length": 512,
56
- "never_split": null,
57
- "pad_token": "[PAD]",
58
- "sep_token": "[SEP]",
59
- "subword_tokenizer_type": "wordpiece",
60
- "sudachi_kwargs": null,
61
- "tokenizer_class": "BertJapaneseTokenizer",
62
- "unk_token": "[UNK]",
63
- "word_tokenizer_type": "mecab"
64
- }
 
1
+ {
2
+ "added_tokens_decoder": {
3
+ "0": {
4
+ "content": "[PAD]",
5
+ "lstrip": false,
6
+ "normalized": false,
7
+ "rstrip": false,
8
+ "single_word": false,
9
+ "special": true
10
+ },
11
+ "1": {
12
+ "content": "[UNK]",
13
+ "lstrip": false,
14
+ "normalized": false,
15
+ "rstrip": false,
16
+ "single_word": false,
17
+ "special": true
18
+ },
19
+ "2": {
20
+ "content": "[CLS]",
21
+ "lstrip": false,
22
+ "normalized": false,
23
+ "rstrip": false,
24
+ "single_word": false,
25
+ "special": true
26
+ },
27
+ "3": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": true
34
+ },
35
+ "4": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": true
42
+ }
43
+ },
44
+ "clean_up_tokenization_spaces": true,
45
+ "cls_token": "[CLS]",
46
+ "do_lower_case": false,
47
+ "do_subword_tokenize": true,
48
+ "do_word_tokenize": true,
49
+ "extra_special_tokens": {},
50
+ "jumanpp_kwargs": null,
51
+ "mask_token": "[MASK]",
52
+ "mecab_kwargs": {
53
+ "mecab_dic": "unidic_lite"
54
+ },
55
+ "model_max_length": 512,
56
+ "never_split": null,
57
+ "pad_token": "[PAD]",
58
+ "sep_token": "[SEP]",
59
+ "subword_tokenizer_type": "wordpiece",
60
+ "sudachi_kwargs": null,
61
+ "tokenizer_class": "BertJapaneseTokenizer",
62
+ "unk_token": "[UNK]",
63
+ "word_tokenizer_type": "mecab"
64
+ }
vocab.txt CHANGED
The diff for this file is too large to render. See raw diff