Hugging Face's logo

LeoChiuu
/
sbert-base-ja-arc

TensorBoard
Safetensors
Japanese
bert
Model card Files Metrics Community
LeoChiuu commited on
Commit
e0c1c90
•
1 Parent(s): 9b9fcdb

Add new SentenceTransformer model.

Browse files
Files changed (47) hide show
  1. README.md +127 -127
  2. checkpoint-816/1_Pooling/config.json +10 -0
  3. checkpoint-816/README.md +589 -0
  4. checkpoint-816/added_tokens.json +3 -0
  5. checkpoint-816/config.json +33 -0
  6. checkpoint-816/config_sentence_transformers.json +10 -0
  7. checkpoint-816/model.safetensors +3 -0
  8. checkpoint-816/modules.json +14 -0
  9. checkpoint-816/optimizer.pt +3 -0
  10. checkpoint-816/rng_state.pth +3 -0
  11. checkpoint-816/scheduler.pt +3 -0
  12. checkpoint-816/sentence_bert_config.json +4 -0
  13. checkpoint-816/special_tokens_map.json +15 -0
  14. checkpoint-816/spm.model +3 -0
  15. checkpoint-816/tokenizer.json +0 -0
  16. checkpoint-816/tokenizer_config.json +65 -0
  17. checkpoint-816/trainer_state.json +633 -0
  18. checkpoint-816/training_args.bin +3 -0
  19. checkpoint-884/1_Pooling/config.json +10 -0
  20. checkpoint-884/README.md +590 -0
  21. checkpoint-884/added_tokens.json +3 -0
  22. checkpoint-884/config.json +33 -0
  23. checkpoint-884/config_sentence_transformers.json +10 -0
  24. checkpoint-884/model.safetensors +3 -0
  25. checkpoint-884/modules.json +14 -0
  26. checkpoint-884/optimizer.pt +3 -0
  27. checkpoint-884/rng_state.pth +3 -0
  28. checkpoint-884/scheduler.pt +3 -0
  29. checkpoint-884/sentence_bert_config.json +4 -0
  30. checkpoint-884/special_tokens_map.json +15 -0
  31. checkpoint-884/spm.model +3 -0
  32. checkpoint-884/tokenizer.json +0 -0
  33. checkpoint-884/tokenizer_config.json +65 -0
  34. checkpoint-884/trainer_state.json +683 -0
  35. checkpoint-884/training_args.bin +3 -0
  36. model.safetensors +1 -1
  37. runs/Sep03_22-46-20_default/events.out.tfevents.1725403583.default.1138.0 +3 -0
  38. runs/Sep04_17-30-25_default/events.out.tfevents.1725471030.default.394.0 +3 -0
  39. runs/Sep04_21-08-57_default/events.out.tfevents.1725484141.default.793.0 +3 -0
  40. runs/Sep11_17-50-24_default/events.out.tfevents.1726077038.default.828.0 +3 -0
  41. runs/Sep11_18-02-35_default/events.out.tfevents.1726077764.default.959.0 +3 -0
  42. runs/Sep11_18-05-21_default/events.out.tfevents.1726077928.default.1078.0 +3 -0
  43. runs/Sep11_23-48-08_default/events.out.tfevents.1726098512.default.5852.0 +3 -0
  44. runs/Sep12_00-21-44_default/events.out.tfevents.1726100510.default.6560.0 +3 -0
  45. runs/Sep12_00-34-34_default/events.out.tfevents.1726101282.default.6715.0 +3 -0
  46. tokenizer.model +3 -0
  47. tokenizer_config.json +14 -64
README.md CHANGED
@@ -43,34 +43,34 @@ tags:
43
  - sentence-similarity
44
  - feature-extraction
45
  - generated_from_trainer
46
- - dataset_size:601
47
  - loss:CoSENTLoss
48
  widget:
49
- - source_sentence: だれかが魔法で花をぬいぐるみに変えた
50
  sentences:
51
- - 誰かが魔法の呪文で花をぬいぐるみに変えた
52
- - 村長は誰？
53
- - どこ？
54
- - source_sentence: 暖炉にスカーフを置いた？
55
  sentences:
56
- - 魔法をかけられる人
57
- - ロウソク
58
- - 晩ご飯のとき
59
- - source_sentence: あほ
60
  sentences:
61
- - 調子はどう？
62
- - きらい
63
- - オッケー
64
- - source_sentence: 猫のぬいぐるみ
65
  sentences:
66
- - 赤い染みが皿にあった
67
- - 好きじゃないの？
68
- - ぬいぐるみ
69
- - source_sentence: リリアンはどんな呪文が使えるの？
70
  sentences:
71
- - あなたは魔法使い？
72
- - 姿かたちを変える魔法
73
- - どのくらいのサイズ？
74
  model-index:
75
  - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
76
  results:
@@ -82,109 +82,109 @@ model-index:
82
  type: custom-arc-semantics-data-jp
83
  metrics:
84
  - type: cosine_accuracy
85
- value: 0.9090909090909091
86
  name: Cosine Accuracy
87
  - type: cosine_accuracy_threshold
88
- value: 0.4785935878753662
89
  name: Cosine Accuracy Threshold
90
  - type: cosine_f1
91
- value: 0.9341317365269461
92
  name: Cosine F1
93
  - type: cosine_f1_threshold
94
- value: 0.4785935878753662
95
  name: Cosine F1 Threshold
96
  - type: cosine_precision
97
- value: 0.9176470588235294
98
  name: Cosine Precision
99
  - type: cosine_recall
100
- value: 0.9512195121951219
101
  name: Cosine Recall
102
  - type: cosine_ap
103
- value: 0.9287829842425579
104
  name: Cosine Ap
105
  - type: dot_accuracy
106
- value: 0.9008264462809917
107
  name: Dot Accuracy
108
  - type: dot_accuracy_threshold
109
- value: 234.1079864501953
110
  name: Dot Accuracy Threshold
111
  - type: dot_f1
112
- value: 0.9302325581395349
113
  name: Dot F1
114
  - type: dot_f1_threshold
115
- value: 209.4735870361328
116
  name: Dot F1 Threshold
117
  - type: dot_precision
118
- value: 0.8888888888888888
119
  name: Dot Precision
120
  - type: dot_recall
121
- value: 0.975609756097561
122
  name: Dot Recall
123
  - type: dot_ap
124
- value: 0.9635932205663708
125
  name: Dot Ap
126
  - type: manhattan_accuracy
127
- value: 0.9008264462809917
128
  name: Manhattan Accuracy
129
  - type: manhattan_accuracy_threshold
130
- value: 558.378173828125
131
  name: Manhattan Accuracy Threshold
132
  - type: manhattan_f1
133
- value: 0.9302325581395349
134
  name: Manhattan F1
135
  - type: manhattan_f1_threshold
136
- value: 580.81640625
137
  name: Manhattan F1 Threshold
138
  - type: manhattan_precision
139
- value: 0.8888888888888888
140
  name: Manhattan Precision
141
  - type: manhattan_recall
142
- value: 0.975609756097561
143
  name: Manhattan Recall
144
  - type: manhattan_ap
145
- value: 0.92846470083454
146
  name: Manhattan Ap
147
  - type: euclidean_accuracy
148
- value: 0.9090909090909091
149
  name: Euclidean Accuracy
150
  - type: euclidean_accuracy_threshold
151
- value: 24.130870819091797
152
  name: Euclidean Accuracy Threshold
153
  - type: euclidean_f1
154
- value: 0.9341317365269461
155
  name: Euclidean F1
156
  - type: euclidean_f1_threshold
157
- value: 24.130870819091797
158
  name: Euclidean F1 Threshold
159
  - type: euclidean_precision
160
- value: 0.9176470588235294
161
  name: Euclidean Precision
162
  - type: euclidean_recall
163
- value: 0.9512195121951219
164
  name: Euclidean Recall
165
  - type: euclidean_ap
166
- value: 0.9287963056027329
167
  name: Euclidean Ap
168
  - type: max_accuracy
169
- value: 0.9090909090909091
170
  name: Max Accuracy
171
  - type: max_accuracy_threshold
172
- value: 558.378173828125
173
  name: Max Accuracy Threshold
174
  - type: max_f1
175
- value: 0.9341317365269461
176
  name: Max F1
177
  - type: max_f1_threshold
178
- value: 580.81640625
179
  name: Max F1 Threshold
180
  - type: max_precision
181
- value: 0.9176470588235294
182
  name: Max Precision
183
  - type: max_recall
184
- value: 0.975609756097561
185
  name: Max Recall
186
  - type: max_ap
187
- value: 0.9635932205663708
188
  name: Max Ap
189
  ---
190
 
@@ -235,12 +235,12 @@ Then you can load this model and run inference.
235
  from sentence_transformers import SentenceTransformer
236
 
237
  # Download from the 🤗 Hub
238
- model = SentenceTransformer("LeoChiuu/sbert-base-ja-arc")
239
  # Run inference
240
  sentences = [
241
- 'リリアンはどんな呪文が使えるの？',
242
- '姿かたちを変える魔法',
243
- 'どのくらいのサイズ？',
244
  ]
245
  embeddings = model.encode(sentences)
246
  print(embeddings.shape)
@@ -286,41 +286,41 @@ You can finetune this model on your own dataset.
286
 
287
  | Metric | Value |
288
  |:-----------------------------|:-----------|
289
- | cosine_accuracy | 0.9091 |
290
- | cosine_accuracy_threshold | 0.4786 |
291
- | cosine_f1 | 0.9341 |
292
- | cosine_f1_threshold | 0.4786 |
293
- | cosine_precision | 0.9176 |
294
- | cosine_recall | 0.9512 |
295
- | cosine_ap | 0.9288 |
296
- | dot_accuracy | 0.9008 |
297
- | dot_accuracy_threshold | 234.108 |
298
- | dot_f1 | 0.9302 |
299
- | dot_f1_threshold | 209.4736 |
300
- | dot_precision | 0.8889 |
301
- | dot_recall | 0.9756 |
302
- | dot_ap | 0.9636 |
303
- | manhattan_accuracy | 0.9008 |
304
- | manhattan_accuracy_threshold | 558.3782 |
305
- | manhattan_f1 | 0.9302 |
306
- | manhattan_f1_threshold | 580.8164 |
307
- | manhattan_precision | 0.8889 |
308
- | manhattan_recall | 0.9756 |
309
- | manhattan_ap | 0.9285 |
310
- | euclidean_accuracy | 0.9091 |
311
- | euclidean_accuracy_threshold | 24.1309 |
312
- | euclidean_f1 | 0.9341 |
313
- | euclidean_f1_threshold | 24.1309 |
314
- | euclidean_precision | 0.9176 |
315
- | euclidean_recall | 0.9512 |
316
- | euclidean_ap | 0.9288 |
317
- | max_accuracy | 0.9091 |
318
- | max_accuracy_threshold | 558.3782 |
319
- | max_f1 | 0.9341 |
320
- | max_f1_threshold | 580.8164 |
321
- | max_precision | 0.9176 |
322
- | max_recall | 0.9756 |
323
- | **max_ap** | **0.9636** |
324
 
325
  <!--
326
  ## Bias, Risks and Limitations
@@ -341,19 +341,19 @@ You can finetune this model on your own dataset.
341
  #### csv
342
 
343
  * Dataset: csv
344
- * Size: 601 training samples
345
  * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
346
- * Approximate statistics based on the first 601 samples:
347
  | | text1 | text2 | label |
348
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
349
  | type | string | string | int |
350
- | details | <ul><li>min: 4 tokens</li><li>mean: 7.99 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.05 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~33.96%</li><li>1: ~66.04%</li></ul> |
351
  * Samples:
352
- | text1 | text2 | label |
353
- |:------------------------|:----------------------|:---------------|
354
- | <code>どっちがいいと思う？</code> | <code>どっちが欲しい？</code> | <code>1</code> |
355
- | <code>かわいいね</code> | <code>ばか</code> | <code>0</code> |
356
- | <code>別のは選べないの？</code> | <code>なにが欲しい？</code> | <code>0</code> |
357
  * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
358
  ```json
359
  {
@@ -367,19 +367,19 @@ You can finetune this model on your own dataset.
367
  #### csv
368
 
369
  * Dataset: csv
370
- * Size: 601 evaluation samples
371
  * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
372
- * Approximate statistics based on the first 601 samples:
373
  | | text1 | text2 | label |
374
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
375
  | type | string | string | int |
376
- | details | <ul><li>min: 4 tokens</li><li>mean: 8.26 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.94 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~32.23%</li><li>1: ~67.77%</li></ul> |
377
  * Samples:
378
- | text1 | text2 | label |
379
- |:-----------------------|:------------------------|:---------------|
380
- | <code>誰かが魔法を使った</code> | <code>誰かがが魔法をかけた</code> | <code>1</code> |
381
- | <code>これが花</code> | <code>ぬいぐるみが花</code> | <code>1</code> |
382
- | <code>夜ご飯を作る前</code> | <code>夜ご飯を食べる前</code> | <code>1</code> |
383
  * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
384
  ```json
385
  {
@@ -516,22 +516,22 @@ You can finetune this model on your own dataset.
516
  </details>
517
 
518
  ### Training Logs
519
- | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
520
- |:-------:|:----:|:-------------:|:------:|:-----------------------------------:|
521
- | None | 0 | - | - | 0.8596 |
522
- | 1.0167 | 61 | 2.775 | 2.0852 | 0.8927 |
523
- | 2.0167 | 122 | 1.213 | 1.7433 | 0.9291 |
524
- | 3.0167 | 183 | 0.5703 | 1.5724 | 0.9379 |
525
- | 4.0167 | 244 | 0.4603 | 1.6239 | 0.9432 |
526
- | 5.0167 | 305 | 0.3672 | 1.6444 | 0.9523 |
527
- | 6.0167 | 366 | 0.2947 | 1.6222 | 0.9603 |
528
- | 7.0167 | 427 | 0.2255 | 1.7302 | 0.9619 |
529
- | 8.0167 | 488 | 0.1678 | 1.7360 | 0.9633 |
530
- | 9.0167 | 549 | 0.1163 | 1.8029 | 0.9620 |
531
- | 10.0167 | 610 | 0.0706 | 1.8986 | 0.9639 |
532
- | 11.0167 | 671 | 0.0389 | 1.9671 | 0.9624 |
533
- | 12.0167 | 732 | 0.0333 | 2.0375 | 0.9636 |
534
- | 12.8 | 780 | 0.0618 | 1.9938 | 0.9636 |
535
 
536
 
537
  ### Framework Versions
 
43
  - sentence-similarity
44
  - feature-extraction
45
  - generated_from_trainer
46
+ - dataset_size:680
47
  - loss:CoSENTLoss
48
  widget:
49
+ - source_sentence: 中を見てみよう
50
  sentences:
51
+ - 外を調べよう
52
+ - リリアンはどんな魔法が使えるの？
53
+ - 花がぬいぐるみに変えられている
54
+ - source_sentence: キャンドル要らない
55
  sentences:
56
+ - なんで猫が話せる？
57
+ - 自分でやれば？
58
+ - 中を見てみよう
59
+ - source_sentence: 信用できない
60
  sentences:
61
+ - どっちでもいいよ
62
+ - 誰？
63
+ - 誰かが呪文で花をぬいぐるみに変えた
64
+ - source_sentence: 例えば？
65
  sentences:
66
+ - 誰かがが魔法をかけた
67
+ - ジャック
68
+ - なんでしなきゃいけないの？
69
+ - source_sentence: 魔法を使える人
70
  sentences:
71
+ - かっこいいね
72
+ - 物の姿を変えられる人
73
+ - 町って？
74
  model-index:
75
  - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
76
  results:
 
82
  type: custom-arc-semantics-data-jp
83
  metrics:
84
  - type: cosine_accuracy
85
+ value: 0.9044117647058824
86
  name: Cosine Accuracy
87
  - type: cosine_accuracy_threshold
88
+ value: 0.5485918521881104
89
  name: Cosine Accuracy Threshold
90
  - type: cosine_f1
91
+ value: 0.912751677852349
92
  name: Cosine F1
93
  - type: cosine_f1_threshold
94
+ value: 0.47659817337989807
95
  name: Cosine F1 Threshold
96
  - type: cosine_precision
97
+ value: 0.918918918918919
98
  name: Cosine Precision
99
  - type: cosine_recall
100
+ value: 0.9066666666666666
101
  name: Cosine Recall
102
  - type: cosine_ap
103
+ value: 0.9088999169341241
104
  name: Cosine Ap
105
  - type: dot_accuracy
106
+ value: 0.9117647058823529
107
  name: Dot Accuracy
108
  - type: dot_accuracy_threshold
109
+ value: 293.22845458984375
110
  name: Dot Accuracy Threshold
111
  - type: dot_f1
112
+ value: 0.9166666666666666
113
  name: Dot F1
114
  - type: dot_f1_threshold
115
+ value: 293.22845458984375
116
  name: Dot F1 Threshold
117
  - type: dot_precision
118
+ value: 0.9565217391304348
119
  name: Dot Precision
120
  - type: dot_recall
121
+ value: 0.88
122
  name: Dot Recall
123
  - type: dot_ap
124
+ value: 0.9171086358892895
125
  name: Dot Ap
126
  - type: manhattan_accuracy
127
+ value: 0.9117647058823529
128
  name: Manhattan Accuracy
129
  - type: manhattan_accuracy_threshold
130
+ value: 524.0676879882812
131
  name: Manhattan Accuracy Threshold
132
  - type: manhattan_f1
133
+ value: 0.918918918918919
134
  name: Manhattan F1
135
  - type: manhattan_f1_threshold
136
+ value: 524.0676879882812
137
  name: Manhattan F1 Threshold
138
  - type: manhattan_precision
139
+ value: 0.9315068493150684
140
  name: Manhattan Precision
141
  - type: manhattan_recall
142
+ value: 0.9066666666666666
143
  name: Manhattan Recall
144
  - type: manhattan_ap
145
+ value: 0.9111567321590129
146
  name: Manhattan Ap
147
  - type: euclidean_accuracy
148
+ value: 0.9117647058823529
149
  name: Euclidean Accuracy
150
  - type: euclidean_accuracy_threshold
151
+ value: 23.82940673828125
152
  name: Euclidean Accuracy Threshold
153
  - type: euclidean_f1
154
+ value: 0.918918918918919
155
  name: Euclidean F1
156
  - type: euclidean_f1_threshold
157
+ value: 23.82940673828125
158
  name: Euclidean F1 Threshold
159
  - type: euclidean_precision
160
+ value: 0.9315068493150684
161
  name: Euclidean Precision
162
  - type: euclidean_recall
163
+ value: 0.9066666666666666
164
  name: Euclidean Recall
165
  - type: euclidean_ap
166
+ value: 0.9094221163568814
167
  name: Euclidean Ap
168
  - type: max_accuracy
169
+ value: 0.9117647058823529
170
  name: Max Accuracy
171
  - type: max_accuracy_threshold
172
+ value: 524.0676879882812
173
  name: Max Accuracy Threshold
174
  - type: max_f1
175
+ value: 0.918918918918919
176
  name: Max F1
177
  - type: max_f1_threshold
178
+ value: 524.0676879882812
179
  name: Max F1 Threshold
180
  - type: max_precision
181
+ value: 0.9565217391304348
182
  name: Max Precision
183
  - type: max_recall
184
+ value: 0.9066666666666666
185
  name: Max Recall
186
  - type: max_ap
187
+ value: 0.9171086358892895
188
  name: Max Ap
189
  ---
190
 
 
235
  from sentence_transformers import SentenceTransformer
236
 
237
  # Download from the 🤗 Hub
238
+ model = SentenceTransformer("sentence_transformers_model_id")
239
  # Run inference
240
  sentences = [
241
+ '魔法を使える人',
242
+ '物の姿を変えられる人',
243
+ 'かっこいいね',
244
  ]
245
  embeddings = model.encode(sentences)
246
  print(embeddings.shape)
 
286
 
287
  | Metric | Value |
288
  |:-----------------------------|:-----------|
289
+ | cosine_accuracy | 0.9044 |
290
+ | cosine_accuracy_threshold | 0.5486 |
291
+ | cosine_f1 | 0.9128 |
292
+ | cosine_f1_threshold | 0.4766 |
293
+ | cosine_precision | 0.9189 |
294
+ | cosine_recall | 0.9067 |
295
+ | cosine_ap | 0.9089 |
296
+ | dot_accuracy | 0.9118 |
297
+ | dot_accuracy_threshold | 293.2285 |
298
+ | dot_f1 | 0.9167 |
299
+ | dot_f1_threshold | 293.2285 |
300
+ | dot_precision | 0.9565 |
301
+ | dot_recall | 0.88 |
302
+ | dot_ap | 0.9171 |
303
+ | manhattan_accuracy | 0.9118 |
304
+ | manhattan_accuracy_threshold | 524.0677 |
305
+ | manhattan_f1 | 0.9189 |
306
+ | manhattan_f1_threshold | 524.0677 |
307
+ | manhattan_precision | 0.9315 |
308
+ | manhattan_recall | 0.9067 |
309
+ | manhattan_ap | 0.9112 |
310
+ | euclidean_accuracy | 0.9118 |
311
+ | euclidean_accuracy_threshold | 23.8294 |
312
+ | euclidean_f1 | 0.9189 |
313
+ | euclidean_f1_threshold | 23.8294 |
314
+ | euclidean_precision | 0.9315 |
315
+ | euclidean_recall | 0.9067 |
316
+ | euclidean_ap | 0.9094 |
317
+ | max_accuracy | 0.9118 |
318
+ | max_accuracy_threshold | 524.0677 |
319
+ | max_f1 | 0.9189 |
320
+ | max_f1_threshold | 524.0677 |
321
+ | max_precision | 0.9565 |
322
+ | max_recall | 0.9067 |
323
+ | **max_ap** | **0.9171** |
324
 
325
  <!--
326
  ## Bias, Risks and Limitations
 
341
  #### csv
342
 
343
  * Dataset: csv
344
+ * Size: 680 training samples
345
  * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
346
+ * Approximate statistics based on the first 680 samples:
347
  | | text1 | text2 | label |
348
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
349
  | type | string | string | int |
350
+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.29 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.97 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~40.44%</li><li>1: ~59.56%</li></ul> |
351
  * Samples:
352
+ | text1 | text2 | label |
353
+ |:----------------------------|:----------------------------|:---------------|
354
+ | <code>いらない</code> | <code>うんよろしく</code> | <code>0</code> |
355
+ | <code>足元よりも更に深くってどこ？</code> | <code>足元よりも更に深くってなに？</code> | <code>1</code> |
356
+ | <code>他にはないの？</code> | <code>どう思う？</code> | <code>0</code> |
357
  * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
358
  ```json
359
  {
 
367
  #### csv
368
 
369
  * Dataset: csv
370
+ * Size: 680 evaluation samples
371
  * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
372
+ * Approximate statistics based on the first 680 samples:
373
  | | text1 | text2 | label |
374
  |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
375
  | type | string | string | int |
376
+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.16 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~44.85%</li><li>1: ~55.15%</li></ul> |
377
  * Samples:
378
+ | text1 | text2 | label |
379
+ |:-------------------------|:-------------------------|:---------------|
380
+ | <code>井戸から水をくんでいた</code> | <code>井戸を使っていた</code> | <code>1</code> |
381
+ | <code>夕飯は何だったの？</code> | <code>チキンヌードル食べた？</code> | <code>0</code> |
382
+ | <code>水を井戸からくんでいた</code> | <code>夜ごはんの前</code> | <code>0</code> |
383
  * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
384
  ```json
385
  {
 
516
  </details>
517
 
518
  ### Training Logs
519
+ | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
520
+ |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
521
+ | None | 0 | - | - | 0.8596 |
522
+ | 1.0 | 68 | 2.6802 | 1.7807 | 0.8872 |
523
+ | 2.0 | 136 | 1.4014 | 1.7683 | 0.8945 |
524
+ | 3.0 | 204 | 0.7937 | 1.9877 | 0.9039 |
525
+ | 4.0 | 272 | 0.5443 | 1.9106 | 0.9075 |
526
+ | 5.0 | 340 | 0.4225 | 1.9418 | 0.9109 |
527
+ | 6.0 | 408 | 0.3347 | 2.0123 | 0.9107 |
528
+ | 7.0 | 476 | 0.3425 | 2.0387 | 0.9094 |
529
+ | 8.0 | 544 | 0.2427 | 1.9878 | 0.9103 |
530
+ | 9.0 | 612 | 0.2412 | 2.0424 | 0.9178 |
531
+ | 10.0 | 680 | 0.1623 | 2.0273 | 0.9188 |
532
+ | 11.0 | 748 | 0.1909 | 2.0955 | 0.9220 |
533
+ | 12.0 | 816 | 0.1507 | 2.2124 | 0.9157 |
534
+ | 13.0 | 884 | 0.1406 | 2.2126 | 0.9171 |
535
 
536
 
537
  ### Framework Versions
checkpoint-816/1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
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
+ }
checkpoint-816/README.md ADDED
@@ -0,0 +1,589 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: colorfulscoop/sbert-base-ja
3
+ library_name: sentence-transformers
4
+ metrics:
5
+ - cosine_accuracy
6
+ - cosine_accuracy_threshold
7
+ - cosine_f1
8
+ - cosine_f1_threshold
9
+ - cosine_precision
10
+ - cosine_recall
11
+ - cosine_ap
12
+ - dot_accuracy
13
+ - dot_accuracy_threshold
14
+ - dot_f1
15
+ - dot_f1_threshold
16
+ - dot_precision
17
+ - dot_recall
18
+ - dot_ap
19
+ - manhattan_accuracy
20
+ - manhattan_accuracy_threshold
21
+ - manhattan_f1
22
+ - manhattan_f1_threshold
23
+ - manhattan_precision
24
+ - manhattan_recall
25
+ - manhattan_ap
26
+ - euclidean_accuracy
27
+ - euclidean_accuracy_threshold
28
+ - euclidean_f1
29
+ - euclidean_f1_threshold
30
+ - euclidean_precision
31
+ - euclidean_recall
32
+ - euclidean_ap
33
+ - max_accuracy
34
+ - max_accuracy_threshold
35
+ - max_f1
36
+ - max_f1_threshold
37
+ - max_precision
38
+ - max_recall
39
+ - max_ap
40
+ pipeline_tag: sentence-similarity
41
+ tags:
42
+ - sentence-transformers
43
+ - sentence-similarity
44
+ - feature-extraction
45
+ - generated_from_trainer
46
+ - dataset_size:680
47
+ - loss:CoSENTLoss
48
+ widget:
49
+ - source_sentence: 中を見てみよう
50
+ sentences:
51
+ - 外を調べよう
52
+ - リリアンはどんな魔法が使えるの？
53
+ - 花がぬいぐるみに変えられている
54
+ - source_sentence: キャンドル要らない
55
+ sentences:
56
+ - なんで猫が話せる？
57
+ - 自分でやれば？
58
+ - 中を見てみよう
59
+ - source_sentence: 信用できない
60
+ sentences:
61
+ - どっちでもいいよ
62
+ - 誰？
63
+ - 誰かが呪文で花をぬいぐるみに変えた
64
+ - source_sentence: 例えば？
65
+ sentences:
66
+ - 誰かがが魔法をかけた
67
+ - ジャック
68
+ - なんでしなきゃいけないの？
69
+ - source_sentence: 魔法を使える人
70
+ sentences:
71
+ - かっこいいね
72
+ - 物の姿を変えられる人
73
+ - 町って？
74
+ model-index:
75
+ - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
76
+ results:
77
+ - task:
78
+ type: binary-classification
79
+ name: Binary Classification
80
+ dataset:
81
+ name: custom arc semantics data jp
82
+ type: custom-arc-semantics-data-jp
83
+ metrics:
84
+ - type: cosine_accuracy
85
+ value: 0.9044117647058824
86
+ name: Cosine Accuracy
87
+ - type: cosine_accuracy_threshold
88
+ value: 0.5501536726951599
89
+ name: Cosine Accuracy Threshold
90
+ - type: cosine_f1
91
+ value: 0.912751677852349
92
+ name: Cosine F1
93
+ - type: cosine_f1_threshold
94
+ value: 0.4790937304496765
95
+ name: Cosine F1 Threshold
96
+ - type: cosine_precision
97
+ value: 0.918918918918919
98
+ name: Cosine Precision
99
+ - type: cosine_recall
100
+ value: 0.9066666666666666
101
+ name: Cosine Recall
102
+ - type: cosine_ap
103
+ value: 0.9084179566135925
104
+ name: Cosine Ap
105
+ - type: dot_accuracy
106
+ value: 0.9117647058823529
107
+ name: Dot Accuracy
108
+ - type: dot_accuracy_threshold
109
+ value: 294.13421630859375
110
+ name: Dot Accuracy Threshold
111
+ - type: dot_f1
112
+ value: 0.9166666666666666
113
+ name: Dot F1
114
+ - type: dot_f1_threshold
115
+ value: 294.13421630859375
116
+ name: Dot F1 Threshold
117
+ - type: dot_precision
118
+ value: 0.9565217391304348
119
+ name: Dot Precision
120
+ - type: dot_recall
121
+ value: 0.88
122
+ name: Dot Recall
123
+ - type: dot_ap
124
+ value: 0.915716305189008
125
+ name: Dot Ap
126
+ - type: manhattan_accuracy
127
+ value: 0.9044117647058824
128
+ name: Manhattan Accuracy
129
+ - type: manhattan_accuracy_threshold
130
+ value: 482.6566162109375
131
+ name: Manhattan Accuracy Threshold
132
+ - type: manhattan_f1
133
+ value: 0.913907284768212
134
+ name: Manhattan F1
135
+ - type: manhattan_f1_threshold
136
+ value: 532.9744262695312
137
+ name: Manhattan F1 Threshold
138
+ - type: manhattan_precision
139
+ value: 0.9078947368421053
140
+ name: Manhattan Precision
141
+ - type: manhattan_recall
142
+ value: 0.92
143
+ name: Manhattan Recall
144
+ - type: manhattan_ap
145
+ value: 0.9104676924615509
146
+ name: Manhattan Ap
147
+ - type: euclidean_accuracy
148
+ value: 0.9117647058823529
149
+ name: Euclidean Accuracy
150
+ - type: euclidean_accuracy_threshold
151
+ value: 23.818954467773438
152
+ name: Euclidean Accuracy Threshold
153
+ - type: euclidean_f1
154
+ value: 0.918918918918919
155
+ name: Euclidean F1
156
+ - type: euclidean_f1_threshold
157
+ value: 23.818954467773438
158
+ name: Euclidean F1 Threshold
159
+ - type: euclidean_precision
160
+ value: 0.9315068493150684
161
+ name: Euclidean Precision
162
+ - type: euclidean_recall
163
+ value: 0.9066666666666666
164
+ name: Euclidean Recall
165
+ - type: euclidean_ap
166
+ value: 0.9093211275077335
167
+ name: Euclidean Ap
168
+ - type: max_accuracy
169
+ value: 0.9117647058823529
170
+ name: Max Accuracy
171
+ - type: max_accuracy_threshold
172
+ value: 482.6566162109375
173
+ name: Max Accuracy Threshold
174
+ - type: max_f1
175
+ value: 0.918918918918919
176
+ name: Max F1
177
+ - type: max_f1_threshold
178
+ value: 532.9744262695312
179
+ name: Max F1 Threshold
180
+ - type: max_precision
181
+ value: 0.9565217391304348
182
+ name: Max Precision
183
+ - type: max_recall
184
+ value: 0.92
185
+ name: Max Recall
186
+ - type: max_ap
187
+ value: 0.915716305189008
188
+ name: Max Ap
189
+ ---
190
+
191
+ # SentenceTransformer based on colorfulscoop/sbert-base-ja
192
+
193
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) on the csv dataset. 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.
194
+
195
+ ## Model Details
196
+
197
+ ### Model Description
198
+ - **Model Type:** Sentence Transformer
199
+ - **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
200
+ - **Maximum Sequence Length:** 512 tokens
201
+ - **Output Dimensionality:** 768 tokens
202
+ - **Similarity Function:** Cosine Similarity
203
+ - **Training Dataset:**
204
+ - csv
205
+ <!-- - **Language:** Unknown -->
206
+ <!-- - **License:** Unknown -->
207
+
208
+ ### Model Sources
209
+
210
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
211
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
212
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
213
+
214
+ ### Full Model Architecture
215
+
216
+ ```
217
+ SentenceTransformer(
218
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
219
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
220
+ )
221
+ ```
222
+
223
+ ## Usage
224
+
225
+ ### Direct Usage (Sentence Transformers)
226
+
227
+ First install the Sentence Transformers library:
228
+
229
+ ```bash
230
+ pip install -U sentence-transformers
231
+ ```
232
+
233
+ Then you can load this model and run inference.
234
+ ```python
235
+ from sentence_transformers import SentenceTransformer
236
+
237
+ # Download from the 🤗 Hub
238
+ model = SentenceTransformer("sentence_transformers_model_id")
239
+ # Run inference
240
+ sentences = [
241
+ '魔法を使える人',
242
+ '物の姿を変えられる人',
243
+ 'かっこいいね',
244
+ ]
245
+ embeddings = model.encode(sentences)
246
+ print(embeddings.shape)
247
+ # [3, 768]
248
+
249
+ # Get the similarity scores for the embeddings
250
+ similarities = model.similarity(embeddings, embeddings)
251
+ print(similarities.shape)
252
+ # [3, 3]
253
+ ```
254
+
255
+ <!--
256
+ ### Direct Usage (Transformers)
257
+
258
+ <details><summary>Click to see the direct usage in Transformers</summary>
259
+
260
+ </details>
261
+ -->
262
+
263
+ <!--
264
+ ### Downstream Usage (Sentence Transformers)
265
+
266
+ You can finetune this model on your own dataset.
267
+
268
+ <details><summary>Click to expand</summary>
269
+
270
+ </details>
271
+ -->
272
+
273
+ <!--
274
+ ### Out-of-Scope Use
275
+
276
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
277
+ -->
278
+
279
+ ## Evaluation
280
+
281
+ ### Metrics
282
+
283
+ #### Binary Classification
284
+ * Dataset: `custom-arc-semantics-data-jp`
285
+ * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
286
+
287
+ | Metric | Value |
288
+ |:-----------------------------|:-----------|
289
+ | cosine_accuracy | 0.9044 |
290
+ | cosine_accuracy_threshold | 0.5502 |
291
+ | cosine_f1 | 0.9128 |
292
+ | cosine_f1_threshold | 0.4791 |
293
+ | cosine_precision | 0.9189 |
294
+ | cosine_recall | 0.9067 |
295
+ | cosine_ap | 0.9084 |
296
+ | dot_accuracy | 0.9118 |
297
+ | dot_accuracy_threshold | 294.1342 |
298
+ | dot_f1 | 0.9167 |
299
+ | dot_f1_threshold | 294.1342 |
300
+ | dot_precision | 0.9565 |
301
+ | dot_recall | 0.88 |
302
+ | dot_ap | 0.9157 |
303
+ | manhattan_accuracy | 0.9044 |
304
+ | manhattan_accuracy_threshold | 482.6566 |
305
+ | manhattan_f1 | 0.9139 |
306
+ | manhattan_f1_threshold | 532.9744 |
307
+ | manhattan_precision | 0.9079 |
308
+ | manhattan_recall | 0.92 |
309
+ | manhattan_ap | 0.9105 |
310
+ | euclidean_accuracy | 0.9118 |
311
+ | euclidean_accuracy_threshold | 23.819 |
312
+ | euclidean_f1 | 0.9189 |
313
+ | euclidean_f1_threshold | 23.819 |
314
+ | euclidean_precision | 0.9315 |
315
+ | euclidean_recall | 0.9067 |
316
+ | euclidean_ap | 0.9093 |
317
+ | max_accuracy | 0.9118 |
318
+ | max_accuracy_threshold | 482.6566 |
319
+ | max_f1 | 0.9189 |
320
+ | max_f1_threshold | 532.9744 |
321
+ | max_precision | 0.9565 |
322
+ | max_recall | 0.92 |
323
+ | **max_ap** | **0.9157** |
324
+
325
+ <!--
326
+ ## Bias, Risks and Limitations
327
+
328
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
329
+ -->
330
+
331
+ <!--
332
+ ### Recommendations
333
+
334
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
335
+ -->
336
+
337
+ ## Training Details
338
+
339
+ ### Training Dataset
340
+
341
+ #### csv
342
+
343
+ * Dataset: csv
344
+ * Size: 680 training samples
345
+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
346
+ * Approximate statistics based on the first 680 samples:
347
+ | | text1 | text2 | label |
348
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
349
+ | type | string | string | int |
350
+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.29 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.97 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~40.44%</li><li>1: ~59.56%</li></ul> |
351
+ * Samples:
352
+ | text1 | text2 | label |
353
+ |:----------------------------|:----------------------------|:---------------|
354
+ | <code>いらない</code> | <code>うんよろしく</code> | <code>0</code> |
355
+ | <code>足元よりも更に深くってどこ？</code> | <code>足元よりも更に深くってなに？</code> | <code>1</code> |
356
+ | <code>他にはないの？</code> | <code>どう思う？</code> | <code>0</code> |
357
+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
358
+ ```json
359
+ {
360
+ "scale": 20.0,
361
+ "similarity_fct": "pairwise_cos_sim"
362
+ }
363
+ ```
364
+
365
+ ### Evaluation Dataset
366
+
367
+ #### csv
368
+
369
+ * Dataset: csv
370
+ * Size: 680 evaluation samples
371
+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
372
+ * Approximate statistics based on the first 680 samples:
373
+ | | text1 | text2 | label |
374
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
375
+ | type | string | string | int |
376
+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.16 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~44.85%</li><li>1: ~55.15%</li></ul> |
377
+ * Samples:
378
+ | text1 | text2 | label |
379
+ |:-------------------------|:-------------------------|:---------------|
380
+ | <code>井戸から水をくんでいた</code> | <code>井戸を使っていた</code> | <code>1</code> |
381
+ | <code>夕飯は何だったの？</code> | <code>チキンヌードル食べた？</code> | <code>0</code> |
382
+ | <code>水を井戸からくんでいた</code> | <code>夜ごはんの前</code> | <code>0</code> |
383
+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
384
+ ```json
385
+ {
386
+ "scale": 20.0,
387
+ "similarity_fct": "pairwise_cos_sim"
388
+ }
389
+ ```
390
+
391
+ ### Training Hyperparameters
392
+ #### Non-Default Hyperparameters
393
+
394
+ - `eval_strategy`: epoch
395
+ - `learning_rate`: 2e-05
396
+ - `num_train_epochs`: 13
397
+ - `warmup_ratio`: 0.1
398
+ - `fp16`: True
399
+ - `batch_sampler`: no_duplicates
400
+
401
+ #### All Hyperparameters
402
+ <details><summary>Click to expand</summary>
403
+
404
+ - `overwrite_output_dir`: False
405
+ - `do_predict`: False
406
+ - `eval_strategy`: epoch
407
+ - `prediction_loss_only`: True
408
+ - `per_device_train_batch_size`: 8
409
+ - `per_device_eval_batch_size`: 8
410
+ - `per_gpu_train_batch_size`: None
411
+ - `per_gpu_eval_batch_size`: None
412
+ - `gradient_accumulation_steps`: 1
413
+ - `eval_accumulation_steps`: None
414
+ - `torch_empty_cache_steps`: None
415
+ - `learning_rate`: 2e-05
416
+ - `weight_decay`: 0.0
417
+ - `adam_beta1`: 0.9
418
+ - `adam_beta2`: 0.999
419
+ - `adam_epsilon`: 1e-08
420
+ - `max_grad_norm`: 1.0
421
+ - `num_train_epochs`: 13
422
+ - `max_steps`: -1
423
+ - `lr_scheduler_type`: linear
424
+ - `lr_scheduler_kwargs`: {}
425
+ - `warmup_ratio`: 0.1
426
+ - `warmup_steps`: 0
427
+ - `log_level`: passive
428
+ - `log_level_replica`: warning
429
+ - `log_on_each_node`: True
430
+ - `logging_nan_inf_filter`: True
431
+ - `save_safetensors`: True
432
+ - `save_on_each_node`: False
433
+ - `save_only_model`: False
434
+ - `restore_callback_states_from_checkpoint`: False
435
+ - `no_cuda`: False
436
+ - `use_cpu`: False
437
+ - `use_mps_device`: False
438
+ - `seed`: 42
439
+ - `data_seed`: None
440
+ - `jit_mode_eval`: False
441
+ - `use_ipex`: False
442
+ - `bf16`: False
443
+ - `fp16`: True
444
+ - `fp16_opt_level`: O1
445
+ - `half_precision_backend`: auto
446
+ - `bf16_full_eval`: False
447
+ - `fp16_full_eval`: False
448
+ - `tf32`: None
449
+ - `local_rank`: 0
450
+ - `ddp_backend`: None
451
+ - `tpu_num_cores`: None
452
+ - `tpu_metrics_debug`: False
453
+ - `debug`: []
454
+ - `dataloader_drop_last`: False
455
+ - `dataloader_num_workers`: 0
456
+ - `dataloader_prefetch_factor`: None
457
+ - `past_index`: -1
458
+ - `disable_tqdm`: False
459
+ - `remove_unused_columns`: True
460
+ - `label_names`: None
461
+ - `load_best_model_at_end`: False
462
+ - `ignore_data_skip`: False
463
+ - `fsdp`: []
464
+ - `fsdp_min_num_params`: 0
465
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
466
+ - `fsdp_transformer_layer_cls_to_wrap`: None
467
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
468
+ - `deepspeed`: None
469
+ - `label_smoothing_factor`: 0.0
470
+ - `optim`: adamw_torch
471
+ - `optim_args`: None
472
+ - `adafactor`: False
473
+ - `group_by_length`: False
474
+ - `length_column_name`: length
475
+ - `ddp_find_unused_parameters`: None
476
+ - `ddp_bucket_cap_mb`: None
477
+ - `ddp_broadcast_buffers`: False
478
+ - `dataloader_pin_memory`: True
479
+ - `dataloader_persistent_workers`: False
480
+ - `skip_memory_metrics`: True
481
+ - `use_legacy_prediction_loop`: False
482
+ - `push_to_hub`: False
483
+ - `resume_from_checkpoint`: None
484
+ - `hub_model_id`: None
485
+ - `hub_strategy`: every_save
486
+ - `hub_private_repo`: False
487
+ - `hub_always_push`: False
488
+ - `gradient_checkpointing`: False
489
+ - `gradient_checkpointing_kwargs`: None
490
+ - `include_inputs_for_metrics`: False
491
+ - `eval_do_concat_batches`: True
492
+ - `fp16_backend`: auto
493
+ - `push_to_hub_model_id`: None
494
+ - `push_to_hub_organization`: None
495
+ - `mp_parameters`:
496
+ - `auto_find_batch_size`: False
497
+ - `full_determinism`: False
498
+ - `torchdynamo`: None
499
+ - `ray_scope`: last
500
+ - `ddp_timeout`: 1800
501
+ - `torch_compile`: False
502
+ - `torch_compile_backend`: None
503
+ - `torch_compile_mode`: None
504
+ - `dispatch_batches`: None
505
+ - `split_batches`: None
506
+ - `include_tokens_per_second`: False
507
+ - `include_num_input_tokens_seen`: False
508
+ - `neftune_noise_alpha`: None
509
+ - `optim_target_modules`: None
510
+ - `batch_eval_metrics`: False
511
+ - `eval_on_start`: False
512
+ - `eval_use_gather_object`: False
513
+ - `batch_sampler`: no_duplicates
514
+ - `multi_dataset_batch_sampler`: proportional
515
+
516
+ </details>
517
+
518
+ ### Training Logs
519
+ | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
520
+ |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
521
+ | None | 0 | - | - | 0.8596 |
522
+ | 1.0 | 68 | 2.6802 | 1.7807 | 0.8872 |
523
+ | 2.0 | 136 | 1.4014 | 1.7683 | 0.8945 |
524
+ | 3.0 | 204 | 0.7937 | 1.9877 | 0.9039 |
525
+ | 4.0 | 272 | 0.5443 | 1.9106 | 0.9075 |
526
+ | 5.0 | 340 | 0.4225 | 1.9418 | 0.9109 |
527
+ | 6.0 | 408 | 0.3347 | 2.0123 | 0.9107 |
528
+ | 7.0 | 476 | 0.3425 | 2.0387 | 0.9094 |
529
+ | 8.0 | 544 | 0.2427 | 1.9878 | 0.9103 |
530
+ | 9.0 | 612 | 0.2412 | 2.0424 | 0.9178 |
531
+ | 10.0 | 680 | 0.1623 | 2.0273 | 0.9188 |
532
+ | 11.0 | 748 | 0.1909 | 2.0955 | 0.9220 |
533
+ | 12.0 | 816 | 0.1507 | 2.2124 | 0.9157 |
534
+
535
+
536
+ ### Framework Versions
537
+ - Python: 3.10.14
538
+ - Sentence Transformers: 3.1.0
539
+ - Transformers: 4.44.2
540
+ - PyTorch: 2.4.1+cu121
541
+ - Accelerate: 0.34.2
542
+ - Datasets: 2.20.0
543
+ - Tokenizers: 0.19.1
544
+
545
+ ## Citation
546
+
547
+ ### BibTeX
548
+
549
+ #### Sentence Transformers
550
+ ```bibtex
551
+ @inproceedings{reimers-2019-sentence-bert,
552
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
553
+ author = "Reimers, Nils and Gurevych, Iryna",
554
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
555
+ month = "11",
556
+ year = "2019",
557
+ publisher = "Association for Computational Linguistics",
558
+ url = "https://arxiv.org/abs/1908.10084",
559
+ }
560
+ ```
561
+
562
+ #### CoSENTLoss
563
+ ```bibtex
564
+ @online{kexuefm-8847,
565
+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
566
+ author={Su Jianlin},
567
+ year={2022},
568
+ month={Jan},
569
+ url={https://kexue.fm/archives/8847},
570
+ }
571
+ ```
572
+
573
+ <!--
574
+ ## Glossary
575
+
576
+ *Clearly define terms in order to be accessible across audiences.*
577
+ -->
578
+
579
+ <!--
580
+ ## Model Card Authors
581
+
582
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
583
+ -->
584
+
585
+ <!--
586
+ ## Model Card Contact
587
+
588
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
589
+ -->
checkpoint-816/added_tokens.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ {
2
+ "[PAD]": 32000
3
+ }
checkpoint-816/config.json ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "colorfulscoop/sbert-base-ja",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 2,
8
+ "classifier_dropout": null,
9
+ "cls_token_id": 2,
10
+ "eos_token_id": 3,
11
+ "gradient_checkpointing": false,
12
+ "hidden_act": "gelu",
13
+ "hidden_dropout_prob": 0.1,
14
+ "hidden_size": 768,
15
+ "initializer_range": 0.02,
16
+ "intermediate_size": 3072,
17
+ "layer_norm_eps": 1e-12,
18
+ "mask_token_id": 4,
19
+ "max_position_embeddings": 512,
20
+ "model_type": "bert",
21
+ "num_attention_heads": 12,
22
+ "num_hidden_layers": 12,
23
+ "pad_token_id": 0,
24
+ "position_embedding_type": "absolute",
25
+ "sep_token_id": 3,
26
+ "tokenizer_class": "DebertaV2Tokenizer",
27
+ "torch_dtype": "float32",
28
+ "transformers_version": "4.44.2",
29
+ "type_vocab_size": 2,
30
+ "unk_token_id": 1,
31
+ "use_cache": true,
32
+ "vocab_size": 32000
33
+ }
checkpoint-816/config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.1.0",
4
+ "transformers": "4.44.2",
5
+ "pytorch": "2.4.1+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": null
10
+ }
checkpoint-816/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4b39d988a62cf3328c005ebecdda07f0df50a10d199b1f8812f8ce2729238961
3
+ size 442491744
checkpoint-816/modules.json ADDED
@@ -0,0 +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
+ ]
checkpoint-816/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:13cda44a810c660a8f509a2dd6d1695114c4b0f55ee02b394a52aca420e66684
3
+ size 880373306
checkpoint-816/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:23f1a25ebc26b1fcdc98956b5af5bc23a09d13a4dc85b2e06beec26dd1f847f8
3
+ size 13990
checkpoint-816/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1469853985d8479d14e56985ee2e4845f271b406135d192db2351f4fb4f0ed07
3
+ size 1064
checkpoint-816/sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": false
4
+ }
checkpoint-816/special_tokens_map.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": "[CLS]",
3
+ "cls_token": "[CLS]",
4
+ "eos_token": "[SEP]",
5
+ "mask_token": "[MASK]",
6
+ "pad_token": "<pad>",
7
+ "sep_token": "[SEP]",
8
+ "unk_token": {
9
+ "content": "<unk>",
10
+ "lstrip": false,
11
+ "normalized": true,
12
+ "rstrip": false,
13
+ "single_word": false
14
+ }
15
+ }
checkpoint-816/spm.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d6467857b4b0c77ded9bac7ad2fb5c16eb64e17e417ce46624dacac2bbb404fc
3
+ size 802713
checkpoint-816/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-816/tokenizer_config.json ADDED
@@ -0,0 +1,65 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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": true,
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": false
26
+ },
27
+ "3": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": false
34
+ },
35
+ "4": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": false
42
+ },
43
+ "32000": {
44
+ "content": "[PAD]",
45
+ "lstrip": false,
46
+ "normalized": true,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": false
50
+ }
51
+ },
52
+ "bos_token": "[CLS]",
53
+ "clean_up_tokenization_spaces": true,
54
+ "cls_token": "[CLS]",
55
+ "do_lower_case": false,
56
+ "eos_token": "[SEP]",
57
+ "mask_token": "[MASK]",
58
+ "model_max_length": 512,
59
+ "pad_token": "<pad>",
60
+ "sep_token": "[SEP]",
61
+ "sp_model_kwargs": {},
62
+ "split_by_punct": false,
63
+ "tokenizer_class": "DebertaV2Tokenizer",
64
+ "unk_token": "<unk>"
65
+ }
checkpoint-816/trainer_state.json ADDED
@@ -0,0 +1,633 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 12.0,
5
+ "eval_steps": 50,
6
+ "global_step": 816,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 1.0,
13
+ "grad_norm": 37.9459228515625,
14
+ "learning_rate": 1.5280898876404495e-05,
15
+ "loss": 2.6802,
16
+ "step": 68
17
+ },
18
+ {
19
+ "epoch": 1.0,
20
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.8529411764705882,
21
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.7407679557800293,
22
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.8861687056931384,
23
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.863013698630137,
24
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.7407679557800293,
25
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.8873239436619719,
26
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.84,
27
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8529411764705882,
28
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 413.34197998046875,
29
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.887218522968793,
30
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.8648648648648648,
31
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 413.34197998046875,
32
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.8767123287671232,
33
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8533333333333334,
34
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.8602941176470589,
35
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 17.441856384277344,
36
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.8855166425710176,
37
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.87248322147651,
38
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 17.441856384277344,
39
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.8783783783783784,
40
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.8666666666666667,
41
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.8602941176470589,
42
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 382.9211730957031,
43
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.8854876210985647,
44
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.8741721854304636,
45
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 386.8717956542969,
46
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.868421052631579,
47
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.88,
48
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.8602941176470589,
49
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 413.34197998046875,
50
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.887218522968793,
51
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.8741721854304636,
52
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 413.34197998046875,
53
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.8873239436619719,
54
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.88,
55
+ "eval_loss": 1.7807204723358154,
56
+ "eval_runtime": 14.8133,
57
+ "eval_samples_per_second": 9.181,
58
+ "eval_steps_per_second": 1.148,
59
+ "step": 68
60
+ },
61
+ {
62
+ "epoch": 2.0,
63
+ "grad_norm": 101.59432983398438,
64
+ "learning_rate": 1.8817610062893082e-05,
65
+ "loss": 1.4014,
66
+ "step": 136
67
+ },
68
+ {
69
+ "epoch": 2.0,
70
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.8676470588235294,
71
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.656826376914978,
72
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.894049495028942,
73
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.8783783783783784,
74
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.656826376914978,
75
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.8904109589041096,
76
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.8666666666666667,
77
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8602941176470589,
78
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 367.9239501953125,
79
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.8915744113817081,
80
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.87248322147651,
81
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 363.2611999511719,
82
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.8783783783783784,
83
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8666666666666667,
84
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.8602941176470589,
85
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 19.78958511352539,
86
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.8925974243856872,
87
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.8819875776397516,
88
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 20.95208740234375,
89
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.8255813953488372,
90
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9466666666666667,
91
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.875,
92
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 456.61602783203125,
93
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.8944639878933996,
94
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.8930817610062893,
95
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 456.61602783203125,
96
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.8452380952380952,
97
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9466666666666667,
98
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.875,
99
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 456.61602783203125,
100
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.8944639878933996,
101
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.8930817610062893,
102
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 456.61602783203125,
103
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.8904109589041096,
104
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9466666666666667,
105
+ "eval_loss": 1.7683357000350952,
106
+ "eval_runtime": 13.9834,
107
+ "eval_samples_per_second": 9.726,
108
+ "eval_steps_per_second": 1.216,
109
+ "step": 136
110
+ },
111
+ {
112
+ "epoch": 3.0,
113
+ "grad_norm": 102.5489730834961,
114
+ "learning_rate": 1.7106918238993714e-05,
115
+ "loss": 0.7937,
116
+ "step": 204
117
+ },
118
+ {
119
+ "epoch": 3.0,
120
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.8897058823529411,
121
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5597226619720459,
122
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9038906301326961,
123
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.9006622516556291,
124
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5076813697814941,
125
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.8947368421052632,
126
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
127
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8823529411764706,
128
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 314.1024475097656,
129
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9012158574599884,
130
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.8904109589041095,
131
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 295.25762939453125,
132
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9154929577464789,
133
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8666666666666667,
134
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.8970588235294118,
135
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 22.511234283447266,
136
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9033993255493061,
137
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.9054054054054055,
138
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 22.511234283447266,
139
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9178082191780822,
140
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.8933333333333333,
141
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.8897058823529411,
142
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 480.63677978515625,
143
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9033851155012284,
144
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.8993288590604026,
145
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 502.66156005859375,
146
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9054054054054054,
147
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.8933333333333333,
148
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.8970588235294118,
149
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 480.63677978515625,
150
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9038906301326961,
151
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.9054054054054055,
152
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 502.66156005859375,
153
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9178082191780822,
154
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
155
+ "eval_loss": 1.9876813888549805,
156
+ "eval_runtime": 15.0797,
157
+ "eval_samples_per_second": 9.019,
158
+ "eval_steps_per_second": 1.127,
159
+ "step": 204
160
+ },
161
+ {
162
+ "epoch": 4.0,
163
+ "grad_norm": 54.64971160888672,
164
+ "learning_rate": 1.539622641509434e-05,
165
+ "loss": 0.5443,
166
+ "step": 272
167
+ },
168
+ {
169
+ "epoch": 4.0,
170
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9117647058823529,
171
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5411365032196045,
172
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9075251070302803,
173
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.918918918918919,
174
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5411365032196045,
175
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.9315068493150684,
176
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
177
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8897058823529411,
178
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 311.26678466796875,
179
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9040853015076152,
180
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9006622516556291,
181
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 285.96728515625,
182
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.8947368421052632,
183
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.9066666666666666,
184
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
185
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 22.630477905273438,
186
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.906411643380887,
187
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
188
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 22.630477905273438,
189
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
190
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
191
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
192
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 495.93194580078125,
193
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9068108312799359,
194
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
195
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 495.93194580078125,
196
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
197
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
198
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
199
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 495.93194580078125,
200
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9075251070302803,
201
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
202
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 495.93194580078125,
203
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9315068493150684,
204
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
205
+ "eval_loss": 1.910571813583374,
206
+ "eval_runtime": 14.3178,
207
+ "eval_samples_per_second": 9.499,
208
+ "eval_steps_per_second": 1.187,
209
+ "step": 272
210
+ },
211
+ {
212
+ "epoch": 5.0,
213
+ "grad_norm": 7.771420955657959,
214
+ "learning_rate": 1.368553459119497e-05,
215
+ "loss": 0.4225,
216
+ "step": 340
217
+ },
218
+ {
219
+ "epoch": 5.0,
220
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9117647058823529,
221
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5279971361160278,
222
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9104573720737034,
223
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.918918918918919,
224
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5279971361160278,
225
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.9315068493150684,
226
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
227
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9044117647058824,
228
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 319.6995849609375,
229
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.907606464463115,
230
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9115646258503401,
231
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 290.19317626953125,
232
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9305555555555556,
233
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8933333333333333,
234
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
235
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 22.626989364624023,
236
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9092245976210003,
237
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
238
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 22.626989364624023,
239
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
240
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
241
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
242
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 494.6650390625,
243
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9108555121538641,
244
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.92,
245
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 514.7510986328125,
246
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.92,
247
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.92,
248
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
249
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 494.6650390625,
250
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9108555121538641,
251
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.92,
252
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 514.7510986328125,
253
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9315068493150684,
254
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.92,
255
+ "eval_loss": 1.9418467283248901,
256
+ "eval_runtime": 14.4159,
257
+ "eval_samples_per_second": 9.434,
258
+ "eval_steps_per_second": 1.179,
259
+ "step": 340
260
+ },
261
+ {
262
+ "epoch": 6.0,
263
+ "grad_norm": 0.8858943581581116,
264
+ "learning_rate": 1.1974842767295597e-05,
265
+ "loss": 0.3347,
266
+ "step": 408
267
+ },
268
+ {
269
+ "epoch": 6.0,
270
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9117647058823529,
271
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5068449974060059,
272
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9088529074244793,
273
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.918918918918919,
274
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5068449974060059,
275
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.9315068493150684,
276
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
277
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8970588235294118,
278
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 322.01324462890625,
279
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9107273300403762,
280
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9054054054054055,
281
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 276.34893798828125,
282
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9178082191780822,
283
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8933333333333333,
284
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
285
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.167011260986328,
286
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9088745561179659,
287
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
288
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.167011260986328,
289
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
290
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
291
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
292
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 506.7710266113281,
293
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9092068556819233,
294
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
295
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 506.7710266113281,
296
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
297
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
298
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
299
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 506.7710266113281,
300
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9107273300403762,
301
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
302
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 506.7710266113281,
303
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9315068493150684,
304
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
305
+ "eval_loss": 2.012265920639038,
306
+ "eval_runtime": 13.9077,
307
+ "eval_samples_per_second": 9.779,
308
+ "eval_steps_per_second": 1.222,
309
+ "step": 408
310
+ },
311
+ {
312
+ "epoch": 7.0,
313
+ "grad_norm": 0.24478188157081604,
314
+ "learning_rate": 1.0264150943396227e-05,
315
+ "loss": 0.3425,
316
+ "step": 476
317
+ },
318
+ {
319
+ "epoch": 7.0,
320
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
321
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5215883255004883,
322
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9081430244026829,
323
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
324
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5215883255004883,
325
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
326
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
327
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9044117647058824,
328
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 313.0162353515625,
329
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9094090969210814,
330
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9090909090909091,
331
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 313.0162353515625,
332
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9558823529411765,
333
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8666666666666667,
334
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9044117647058824,
335
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.342838287353516,
336
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.907981722850528,
337
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.912751677852349,
338
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.342838287353516,
339
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.918918918918919,
340
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
341
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9044117647058824,
342
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 511.61737060546875,
343
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9085688725632555,
344
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.912751677852349,
345
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 511.61737060546875,
346
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.918918918918919,
347
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
348
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9044117647058824,
349
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 511.61737060546875,
350
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9094090969210814,
351
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.912751677852349,
352
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 511.61737060546875,
353
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9558823529411765,
354
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
355
+ "eval_loss": 2.038686752319336,
356
+ "eval_runtime": 14.7627,
357
+ "eval_samples_per_second": 9.212,
358
+ "eval_steps_per_second": 1.152,
359
+ "step": 476
360
+ },
361
+ {
362
+ "epoch": 8.0,
363
+ "grad_norm": 0.09618318825960159,
364
+ "learning_rate": 8.553459119496857e-06,
365
+ "loss": 0.2427,
366
+ "step": 544
367
+ },
368
+ {
369
+ "epoch": 8.0,
370
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
371
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5501157641410828,
372
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9086817589029806,
373
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
374
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5119773149490356,
375
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
376
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
377
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
378
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 294.31005859375,
379
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9096226292878296,
380
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
381
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 294.31005859375,
382
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
383
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
384
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
385
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.28342056274414,
386
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9089227316320502,
387
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
388
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.28342056274414,
389
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
390
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
391
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
392
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 509.5169677734375,
393
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9102569832574411,
394
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
395
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 509.5169677734375,
396
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
397
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
398
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
399
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 509.5169677734375,
400
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9102569832574411,
401
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
402
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 509.5169677734375,
403
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
404
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
405
+ "eval_loss": 1.9878324270248413,
406
+ "eval_runtime": 13.7764,
407
+ "eval_samples_per_second": 9.872,
408
+ "eval_steps_per_second": 1.234,
409
+ "step": 544
410
+ },
411
+ {
412
+ "epoch": 9.0,
413
+ "grad_norm": 0.046123113483190536,
414
+ "learning_rate": 6.842767295597485e-06,
415
+ "loss": 0.2412,
416
+ "step": 612
417
+ },
418
+ {
419
+ "epoch": 9.0,
420
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
421
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5531296730041504,
422
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9088232956862466,
423
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
424
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.49595877528190613,
425
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
426
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
427
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
428
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 295.7380676269531,
429
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9178358242971421,
430
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
431
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 295.7380676269531,
432
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
433
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
434
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
435
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.565818786621094,
436
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9092338106583002,
437
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
438
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.565818786621094,
439
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
440
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
441
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
442
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 515.80322265625,
443
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9102278723159124,
444
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
445
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 515.80322265625,
446
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
447
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
448
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
449
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 515.80322265625,
450
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9178358242971421,
451
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
452
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 515.80322265625,
453
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
454
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
455
+ "eval_loss": 2.0423502922058105,
456
+ "eval_runtime": 18.8817,
457
+ "eval_samples_per_second": 7.203,
458
+ "eval_steps_per_second": 0.9,
459
+ "step": 612
460
+ },
461
+ {
462
+ "epoch": 10.0,
463
+ "grad_norm": 0.029908249154686928,
464
+ "learning_rate": 5.1320754716981136e-06,
465
+ "loss": 0.1623,
466
+ "step": 680
467
+ },
468
+ {
469
+ "epoch": 10.0,
470
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
471
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5516438484191895,
472
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9085774598037802,
473
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.9103448275862068,
474
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.519783079624176,
475
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.9428571428571428,
476
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.88,
477
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
478
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 294.6772766113281,
479
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.918847565389113,
480
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
481
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 294.6772766113281,
482
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
483
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
484
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9044117647058824,
485
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 21.938934326171875,
486
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9089511554768472,
487
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.912751677852349,
488
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.713035583496094,
489
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.918918918918919,
490
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
491
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9044117647058824,
492
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 482.1662292480469,
493
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.910254731797207,
494
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.912751677852349,
495
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 524.188232421875,
496
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.918918918918919,
497
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
498
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
499
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 482.1662292480469,
500
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.918847565389113,
501
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.9166666666666666,
502
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 524.188232421875,
503
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
504
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
505
+ "eval_loss": 2.0273122787475586,
506
+ "eval_runtime": 20.5658,
507
+ "eval_samples_per_second": 6.613,
508
+ "eval_steps_per_second": 0.827,
509
+ "step": 680
510
+ },
511
+ {
512
+ "epoch": 11.0,
513
+ "grad_norm": 0.007907501421868801,
514
+ "learning_rate": 3.4213836477987424e-06,
515
+ "loss": 0.1909,
516
+ "step": 748
517
+ },
518
+ {
519
+ "epoch": 11.0,
520
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
521
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.550028383731842,
522
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9090819039672458,
523
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
524
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.48157936334609985,
525
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
526
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
527
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
528
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 293.80010986328125,
529
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9219827879602106,
530
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
531
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 293.80010986328125,
532
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
533
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
534
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
535
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.849559783935547,
536
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.90922024992935,
537
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
538
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.849559783935547,
539
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
540
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
541
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9044117647058824,
542
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 482.9535827636719,
543
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.910689468531078,
544
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.912751677852349,
545
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 527.540771484375,
546
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.918918918918919,
547
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
548
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
549
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 482.9535827636719,
550
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9219827879602106,
551
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
552
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 527.540771484375,
553
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
554
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
555
+ "eval_loss": 2.0955376625061035,
556
+ "eval_runtime": 20.4117,
557
+ "eval_samples_per_second": 6.663,
558
+ "eval_steps_per_second": 0.833,
559
+ "step": 748
560
+ },
561
+ {
562
+ "epoch": 12.0,
563
+ "grad_norm": 0.3338662087917328,
564
+ "learning_rate": 1.7106918238993712e-06,
565
+ "loss": 0.1507,
566
+ "step": 816
567
+ },
568
+ {
569
+ "epoch": 12.0,
570
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
571
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5501536726951599,
572
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9084179566135925,
573
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
574
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.4790937304496765,
575
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
576
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
577
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
578
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 294.13421630859375,
579
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.915716305189008,
580
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
581
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 294.13421630859375,
582
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
583
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
584
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
585
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.818954467773438,
586
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9093211275077335,
587
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
588
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.818954467773438,
589
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
590
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
591
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9044117647058824,
592
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 482.6566162109375,
593
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9104676924615509,
594
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.913907284768212,
595
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 532.9744262695312,
596
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9078947368421053,
597
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.92,
598
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
599
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 482.6566162109375,
600
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.915716305189008,
601
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
602
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 532.9744262695312,
603
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
604
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.92,
605
+ "eval_loss": 2.2124059200286865,
606
+ "eval_runtime": 20.6137,
607
+ "eval_samples_per_second": 6.598,
608
+ "eval_steps_per_second": 0.825,
609
+ "step": 816
610
+ }
611
+ ],
612
+ "logging_steps": 500,
613
+ "max_steps": 884,
614
+ "num_input_tokens_seen": 0,
615
+ "num_train_epochs": 13,
616
+ "save_steps": 100,
617
+ "stateful_callbacks": {
618
+ "TrainerControl": {
619
+ "args": {
620
+ "should_epoch_stop": false,
621
+ "should_evaluate": false,
622
+ "should_log": false,
623
+ "should_save": true,
624
+ "should_training_stop": false
625
+ },
626
+ "attributes": {}
627
+ }
628
+ },
629
+ "total_flos": 0.0,
630
+ "train_batch_size": 8,
631
+ "trial_name": null,
632
+ "trial_params": null
633
+ }
checkpoint-816/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cb4f571730ddebdbfd409aa41c36173377c8d33a0e18444875f0d792c4278e37
3
+ size 5432
checkpoint-884/1_Pooling/config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "word_embedding_dimension": 768,
3
+ "pooling_mode_cls_token": false,
4
+ "pooling_mode_mean_tokens": true,
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
+ }
checkpoint-884/README.md ADDED
@@ -0,0 +1,590 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ base_model: colorfulscoop/sbert-base-ja
3
+ library_name: sentence-transformers
4
+ metrics:
5
+ - cosine_accuracy
6
+ - cosine_accuracy_threshold
7
+ - cosine_f1
8
+ - cosine_f1_threshold
9
+ - cosine_precision
10
+ - cosine_recall
11
+ - cosine_ap
12
+ - dot_accuracy
13
+ - dot_accuracy_threshold
14
+ - dot_f1
15
+ - dot_f1_threshold
16
+ - dot_precision
17
+ - dot_recall
18
+ - dot_ap
19
+ - manhattan_accuracy
20
+ - manhattan_accuracy_threshold
21
+ - manhattan_f1
22
+ - manhattan_f1_threshold
23
+ - manhattan_precision
24
+ - manhattan_recall
25
+ - manhattan_ap
26
+ - euclidean_accuracy
27
+ - euclidean_accuracy_threshold
28
+ - euclidean_f1
29
+ - euclidean_f1_threshold
30
+ - euclidean_precision
31
+ - euclidean_recall
32
+ - euclidean_ap
33
+ - max_accuracy
34
+ - max_accuracy_threshold
35
+ - max_f1
36
+ - max_f1_threshold
37
+ - max_precision
38
+ - max_recall
39
+ - max_ap
40
+ pipeline_tag: sentence-similarity
41
+ tags:
42
+ - sentence-transformers
43
+ - sentence-similarity
44
+ - feature-extraction
45
+ - generated_from_trainer
46
+ - dataset_size:680
47
+ - loss:CoSENTLoss
48
+ widget:
49
+ - source_sentence: 中を見てみよう
50
+ sentences:
51
+ - 外を調べよう
52
+ - リリアンはどんな魔法が使えるの？
53
+ - 花がぬいぐるみに変えられている
54
+ - source_sentence: キャンドル要らない
55
+ sentences:
56
+ - なんで猫が話せる？
57
+ - 自分でやれば？
58
+ - 中を見てみよう
59
+ - source_sentence: 信用できない
60
+ sentences:
61
+ - どっちでもいいよ
62
+ - 誰？
63
+ - 誰かが呪文で花をぬいぐるみに変えた
64
+ - source_sentence: 例えば？
65
+ sentences:
66
+ - 誰かがが魔法をかけた
67
+ - ジャック
68
+ - なんでしなきゃいけないの？
69
+ - source_sentence: 魔法を使える人
70
+ sentences:
71
+ - かっこいいね
72
+ - 物の姿を変えられる人
73
+ - 町って？
74
+ model-index:
75
+ - name: SentenceTransformer based on colorfulscoop/sbert-base-ja
76
+ results:
77
+ - task:
78
+ type: binary-classification
79
+ name: Binary Classification
80
+ dataset:
81
+ name: custom arc semantics data jp
82
+ type: custom-arc-semantics-data-jp
83
+ metrics:
84
+ - type: cosine_accuracy
85
+ value: 0.9044117647058824
86
+ name: Cosine Accuracy
87
+ - type: cosine_accuracy_threshold
88
+ value: 0.5485918521881104
89
+ name: Cosine Accuracy Threshold
90
+ - type: cosine_f1
91
+ value: 0.912751677852349
92
+ name: Cosine F1
93
+ - type: cosine_f1_threshold
94
+ value: 0.47659817337989807
95
+ name: Cosine F1 Threshold
96
+ - type: cosine_precision
97
+ value: 0.918918918918919
98
+ name: Cosine Precision
99
+ - type: cosine_recall
100
+ value: 0.9066666666666666
101
+ name: Cosine Recall
102
+ - type: cosine_ap
103
+ value: 0.9088999169341241
104
+ name: Cosine Ap
105
+ - type: dot_accuracy
106
+ value: 0.9117647058823529
107
+ name: Dot Accuracy
108
+ - type: dot_accuracy_threshold
109
+ value: 293.22845458984375
110
+ name: Dot Accuracy Threshold
111
+ - type: dot_f1
112
+ value: 0.9166666666666666
113
+ name: Dot F1
114
+ - type: dot_f1_threshold
115
+ value: 293.22845458984375
116
+ name: Dot F1 Threshold
117
+ - type: dot_precision
118
+ value: 0.9565217391304348
119
+ name: Dot Precision
120
+ - type: dot_recall
121
+ value: 0.88
122
+ name: Dot Recall
123
+ - type: dot_ap
124
+ value: 0.9171086358892895
125
+ name: Dot Ap
126
+ - type: manhattan_accuracy
127
+ value: 0.9117647058823529
128
+ name: Manhattan Accuracy
129
+ - type: manhattan_accuracy_threshold
130
+ value: 524.0676879882812
131
+ name: Manhattan Accuracy Threshold
132
+ - type: manhattan_f1
133
+ value: 0.918918918918919
134
+ name: Manhattan F1
135
+ - type: manhattan_f1_threshold
136
+ value: 524.0676879882812
137
+ name: Manhattan F1 Threshold
138
+ - type: manhattan_precision
139
+ value: 0.9315068493150684
140
+ name: Manhattan Precision
141
+ - type: manhattan_recall
142
+ value: 0.9066666666666666
143
+ name: Manhattan Recall
144
+ - type: manhattan_ap
145
+ value: 0.9111567321590129
146
+ name: Manhattan Ap
147
+ - type: euclidean_accuracy
148
+ value: 0.9117647058823529
149
+ name: Euclidean Accuracy
150
+ - type: euclidean_accuracy_threshold
151
+ value: 23.82940673828125
152
+ name: Euclidean Accuracy Threshold
153
+ - type: euclidean_f1
154
+ value: 0.918918918918919
155
+ name: Euclidean F1
156
+ - type: euclidean_f1_threshold
157
+ value: 23.82940673828125
158
+ name: Euclidean F1 Threshold
159
+ - type: euclidean_precision
160
+ value: 0.9315068493150684
161
+ name: Euclidean Precision
162
+ - type: euclidean_recall
163
+ value: 0.9066666666666666
164
+ name: Euclidean Recall
165
+ - type: euclidean_ap
166
+ value: 0.9094221163568814
167
+ name: Euclidean Ap
168
+ - type: max_accuracy
169
+ value: 0.9117647058823529
170
+ name: Max Accuracy
171
+ - type: max_accuracy_threshold
172
+ value: 524.0676879882812
173
+ name: Max Accuracy Threshold
174
+ - type: max_f1
175
+ value: 0.918918918918919
176
+ name: Max F1
177
+ - type: max_f1_threshold
178
+ value: 524.0676879882812
179
+ name: Max F1 Threshold
180
+ - type: max_precision
181
+ value: 0.9565217391304348
182
+ name: Max Precision
183
+ - type: max_recall
184
+ value: 0.9066666666666666
185
+ name: Max Recall
186
+ - type: max_ap
187
+ value: 0.9171086358892895
188
+ name: Max Ap
189
+ ---
190
+
191
+ # SentenceTransformer based on colorfulscoop/sbert-base-ja
192
+
193
+ This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) on the csv dataset. 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.
194
+
195
+ ## Model Details
196
+
197
+ ### Model Description
198
+ - **Model Type:** Sentence Transformer
199
+ - **Base model:** [colorfulscoop/sbert-base-ja](https://huggingface.co/colorfulscoop/sbert-base-ja) <!-- at revision ecb8a98cd5176719ff7ab0d770a27420118732cf -->
200
+ - **Maximum Sequence Length:** 512 tokens
201
+ - **Output Dimensionality:** 768 tokens
202
+ - **Similarity Function:** Cosine Similarity
203
+ - **Training Dataset:**
204
+ - csv
205
+ <!-- - **Language:** Unknown -->
206
+ <!-- - **License:** Unknown -->
207
+
208
+ ### Model Sources
209
+
210
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
211
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
212
+ - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
213
+
214
+ ### Full Model Architecture
215
+
216
+ ```
217
+ SentenceTransformer(
218
+ (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
219
+ (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
220
+ )
221
+ ```
222
+
223
+ ## Usage
224
+
225
+ ### Direct Usage (Sentence Transformers)
226
+
227
+ First install the Sentence Transformers library:
228
+
229
+ ```bash
230
+ pip install -U sentence-transformers
231
+ ```
232
+
233
+ Then you can load this model and run inference.
234
+ ```python
235
+ from sentence_transformers import SentenceTransformer
236
+
237
+ # Download from the 🤗 Hub
238
+ model = SentenceTransformer("sentence_transformers_model_id")
239
+ # Run inference
240
+ sentences = [
241
+ '魔法を使える人',
242
+ '物の姿を変えられる人',
243
+ 'かっこいいね',
244
+ ]
245
+ embeddings = model.encode(sentences)
246
+ print(embeddings.shape)
247
+ # [3, 768]
248
+
249
+ # Get the similarity scores for the embeddings
250
+ similarities = model.similarity(embeddings, embeddings)
251
+ print(similarities.shape)
252
+ # [3, 3]
253
+ ```
254
+
255
+ <!--
256
+ ### Direct Usage (Transformers)
257
+
258
+ <details><summary>Click to see the direct usage in Transformers</summary>
259
+
260
+ </details>
261
+ -->
262
+
263
+ <!--
264
+ ### Downstream Usage (Sentence Transformers)
265
+
266
+ You can finetune this model on your own dataset.
267
+
268
+ <details><summary>Click to expand</summary>
269
+
270
+ </details>
271
+ -->
272
+
273
+ <!--
274
+ ### Out-of-Scope Use
275
+
276
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
277
+ -->
278
+
279
+ ## Evaluation
280
+
281
+ ### Metrics
282
+
283
+ #### Binary Classification
284
+ * Dataset: `custom-arc-semantics-data-jp`
285
+ * Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
286
+
287
+ | Metric | Value |
288
+ |:-----------------------------|:-----------|
289
+ | cosine_accuracy | 0.9044 |
290
+ | cosine_accuracy_threshold | 0.5486 |
291
+ | cosine_f1 | 0.9128 |
292
+ | cosine_f1_threshold | 0.4766 |
293
+ | cosine_precision | 0.9189 |
294
+ | cosine_recall | 0.9067 |
295
+ | cosine_ap | 0.9089 |
296
+ | dot_accuracy | 0.9118 |
297
+ | dot_accuracy_threshold | 293.2285 |
298
+ | dot_f1 | 0.9167 |
299
+ | dot_f1_threshold | 293.2285 |
300
+ | dot_precision | 0.9565 |
301
+ | dot_recall | 0.88 |
302
+ | dot_ap | 0.9171 |
303
+ | manhattan_accuracy | 0.9118 |
304
+ | manhattan_accuracy_threshold | 524.0677 |
305
+ | manhattan_f1 | 0.9189 |
306
+ | manhattan_f1_threshold | 524.0677 |
307
+ | manhattan_precision | 0.9315 |
308
+ | manhattan_recall | 0.9067 |
309
+ | manhattan_ap | 0.9112 |
310
+ | euclidean_accuracy | 0.9118 |
311
+ | euclidean_accuracy_threshold | 23.8294 |
312
+ | euclidean_f1 | 0.9189 |
313
+ | euclidean_f1_threshold | 23.8294 |
314
+ | euclidean_precision | 0.9315 |
315
+ | euclidean_recall | 0.9067 |
316
+ | euclidean_ap | 0.9094 |
317
+ | max_accuracy | 0.9118 |
318
+ | max_accuracy_threshold | 524.0677 |
319
+ | max_f1 | 0.9189 |
320
+ | max_f1_threshold | 524.0677 |
321
+ | max_precision | 0.9565 |
322
+ | max_recall | 0.9067 |
323
+ | **max_ap** | **0.9171** |
324
+
325
+ <!--
326
+ ## Bias, Risks and Limitations
327
+
328
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
329
+ -->
330
+
331
+ <!--
332
+ ### Recommendations
333
+
334
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
335
+ -->
336
+
337
+ ## Training Details
338
+
339
+ ### Training Dataset
340
+
341
+ #### csv
342
+
343
+ * Dataset: csv
344
+ * Size: 680 training samples
345
+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
346
+ * Approximate statistics based on the first 680 samples:
347
+ | | text1 | text2 | label |
348
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
349
+ | type | string | string | int |
350
+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.29 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 7.97 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~40.44%</li><li>1: ~59.56%</li></ul> |
351
+ * Samples:
352
+ | text1 | text2 | label |
353
+ |:----------------------------|:----------------------------|:---------------|
354
+ | <code>いらない</code> | <code>うんよろしく</code> | <code>0</code> |
355
+ | <code>足元よりも更に深くってどこ？</code> | <code>足元よりも更に深くってなに？</code> | <code>1</code> |
356
+ | <code>他にはないの？</code> | <code>どう思う？</code> | <code>0</code> |
357
+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
358
+ ```json
359
+ {
360
+ "scale": 20.0,
361
+ "similarity_fct": "pairwise_cos_sim"
362
+ }
363
+ ```
364
+
365
+ ### Evaluation Dataset
366
+
367
+ #### csv
368
+
369
+ * Dataset: csv
370
+ * Size: 680 evaluation samples
371
+ * Columns: <code>text1</code>, <code>text2</code>, and <code>label</code>
372
+ * Approximate statistics based on the first 680 samples:
373
+ | | text1 | text2 | label |
374
+ |:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:------------------------------------------------|
375
+ | type | string | string | int |
376
+ | details | <ul><li>min: 4 tokens</li><li>mean: 8.32 tokens</li><li>max: 15 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.16 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>0: ~44.85%</li><li>1: ~55.15%</li></ul> |
377
+ * Samples:
378
+ | text1 | text2 | label |
379
+ |:-------------------------|:-------------------------|:---------------|
380
+ | <code>井戸から水をくんでいた</code> | <code>井戸を使っていた</code> | <code>1</code> |
381
+ | <code>夕飯は何だったの？</code> | <code>チキンヌードル食べた？</code> | <code>0</code> |
382
+ | <code>水を井戸からくんでいた</code> | <code>夜ごはんの前</code> | <code>0</code> |
383
+ * Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
384
+ ```json
385
+ {
386
+ "scale": 20.0,
387
+ "similarity_fct": "pairwise_cos_sim"
388
+ }
389
+ ```
390
+
391
+ ### Training Hyperparameters
392
+ #### Non-Default Hyperparameters
393
+
394
+ - `eval_strategy`: epoch
395
+ - `learning_rate`: 2e-05
396
+ - `num_train_epochs`: 13
397
+ - `warmup_ratio`: 0.1
398
+ - `fp16`: True
399
+ - `batch_sampler`: no_duplicates
400
+
401
+ #### All Hyperparameters
402
+ <details><summary>Click to expand</summary>
403
+
404
+ - `overwrite_output_dir`: False
405
+ - `do_predict`: False
406
+ - `eval_strategy`: epoch
407
+ - `prediction_loss_only`: True
408
+ - `per_device_train_batch_size`: 8
409
+ - `per_device_eval_batch_size`: 8
410
+ - `per_gpu_train_batch_size`: None
411
+ - `per_gpu_eval_batch_size`: None
412
+ - `gradient_accumulation_steps`: 1
413
+ - `eval_accumulation_steps`: None
414
+ - `torch_empty_cache_steps`: None
415
+ - `learning_rate`: 2e-05
416
+ - `weight_decay`: 0.0
417
+ - `adam_beta1`: 0.9
418
+ - `adam_beta2`: 0.999
419
+ - `adam_epsilon`: 1e-08
420
+ - `max_grad_norm`: 1.0
421
+ - `num_train_epochs`: 13
422
+ - `max_steps`: -1
423
+ - `lr_scheduler_type`: linear
424
+ - `lr_scheduler_kwargs`: {}
425
+ - `warmup_ratio`: 0.1
426
+ - `warmup_steps`: 0
427
+ - `log_level`: passive
428
+ - `log_level_replica`: warning
429
+ - `log_on_each_node`: True
430
+ - `logging_nan_inf_filter`: True
431
+ - `save_safetensors`: True
432
+ - `save_on_each_node`: False
433
+ - `save_only_model`: False
434
+ - `restore_callback_states_from_checkpoint`: False
435
+ - `no_cuda`: False
436
+ - `use_cpu`: False
437
+ - `use_mps_device`: False
438
+ - `seed`: 42
439
+ - `data_seed`: None
440
+ - `jit_mode_eval`: False
441
+ - `use_ipex`: False
442
+ - `bf16`: False
443
+ - `fp16`: True
444
+ - `fp16_opt_level`: O1
445
+ - `half_precision_backend`: auto
446
+ - `bf16_full_eval`: False
447
+ - `fp16_full_eval`: False
448
+ - `tf32`: None
449
+ - `local_rank`: 0
450
+ - `ddp_backend`: None
451
+ - `tpu_num_cores`: None
452
+ - `tpu_metrics_debug`: False
453
+ - `debug`: []
454
+ - `dataloader_drop_last`: False
455
+ - `dataloader_num_workers`: 0
456
+ - `dataloader_prefetch_factor`: None
457
+ - `past_index`: -1
458
+ - `disable_tqdm`: False
459
+ - `remove_unused_columns`: True
460
+ - `label_names`: None
461
+ - `load_best_model_at_end`: False
462
+ - `ignore_data_skip`: False
463
+ - `fsdp`: []
464
+ - `fsdp_min_num_params`: 0
465
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
466
+ - `fsdp_transformer_layer_cls_to_wrap`: None
467
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
468
+ - `deepspeed`: None
469
+ - `label_smoothing_factor`: 0.0
470
+ - `optim`: adamw_torch
471
+ - `optim_args`: None
472
+ - `adafactor`: False
473
+ - `group_by_length`: False
474
+ - `length_column_name`: length
475
+ - `ddp_find_unused_parameters`: None
476
+ - `ddp_bucket_cap_mb`: None
477
+ - `ddp_broadcast_buffers`: False
478
+ - `dataloader_pin_memory`: True
479
+ - `dataloader_persistent_workers`: False
480
+ - `skip_memory_metrics`: True
481
+ - `use_legacy_prediction_loop`: False
482
+ - `push_to_hub`: False
483
+ - `resume_from_checkpoint`: None
484
+ - `hub_model_id`: None
485
+ - `hub_strategy`: every_save
486
+ - `hub_private_repo`: False
487
+ - `hub_always_push`: False
488
+ - `gradient_checkpointing`: False
489
+ - `gradient_checkpointing_kwargs`: None
490
+ - `include_inputs_for_metrics`: False
491
+ - `eval_do_concat_batches`: True
492
+ - `fp16_backend`: auto
493
+ - `push_to_hub_model_id`: None
494
+ - `push_to_hub_organization`: None
495
+ - `mp_parameters`:
496
+ - `auto_find_batch_size`: False
497
+ - `full_determinism`: False
498
+ - `torchdynamo`: None
499
+ - `ray_scope`: last
500
+ - `ddp_timeout`: 1800
501
+ - `torch_compile`: False
502
+ - `torch_compile_backend`: None
503
+ - `torch_compile_mode`: None
504
+ - `dispatch_batches`: None
505
+ - `split_batches`: None
506
+ - `include_tokens_per_second`: False
507
+ - `include_num_input_tokens_seen`: False
508
+ - `neftune_noise_alpha`: None
509
+ - `optim_target_modules`: None
510
+ - `batch_eval_metrics`: False
511
+ - `eval_on_start`: False
512
+ - `eval_use_gather_object`: False
513
+ - `batch_sampler`: no_duplicates
514
+ - `multi_dataset_batch_sampler`: proportional
515
+
516
+ </details>
517
+
518
+ ### Training Logs
519
+ | Epoch | Step | Training Loss | loss | custom-arc-semantics-data-jp_max_ap |
520
+ |:-----:|:----:|:-------------:|:------:|:-----------------------------------:|
521
+ | None | 0 | - | - | 0.8596 |
522
+ | 1.0 | 68 | 2.6802 | 1.7807 | 0.8872 |
523
+ | 2.0 | 136 | 1.4014 | 1.7683 | 0.8945 |
524
+ | 3.0 | 204 | 0.7937 | 1.9877 | 0.9039 |
525
+ | 4.0 | 272 | 0.5443 | 1.9106 | 0.9075 |
526
+ | 5.0 | 340 | 0.4225 | 1.9418 | 0.9109 |
527
+ | 6.0 | 408 | 0.3347 | 2.0123 | 0.9107 |
528
+ | 7.0 | 476 | 0.3425 | 2.0387 | 0.9094 |
529
+ | 8.0 | 544 | 0.2427 | 1.9878 | 0.9103 |
530
+ | 9.0 | 612 | 0.2412 | 2.0424 | 0.9178 |
531
+ | 10.0 | 680 | 0.1623 | 2.0273 | 0.9188 |
532
+ | 11.0 | 748 | 0.1909 | 2.0955 | 0.9220 |
533
+ | 12.0 | 816 | 0.1507 | 2.2124 | 0.9157 |
534
+ | 13.0 | 884 | 0.1406 | 2.2126 | 0.9171 |
535
+
536
+
537
+ ### Framework Versions
538
+ - Python: 3.10.14
539
+ - Sentence Transformers: 3.1.0
540
+ - Transformers: 4.44.2
541
+ - PyTorch: 2.4.1+cu121
542
+ - Accelerate: 0.34.2
543
+ - Datasets: 2.20.0
544
+ - Tokenizers: 0.19.1
545
+
546
+ ## Citation
547
+
548
+ ### BibTeX
549
+
550
+ #### Sentence Transformers
551
+ ```bibtex
552
+ @inproceedings{reimers-2019-sentence-bert,
553
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
554
+ author = "Reimers, Nils and Gurevych, Iryna",
555
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
556
+ month = "11",
557
+ year = "2019",
558
+ publisher = "Association for Computational Linguistics",
559
+ url = "https://arxiv.org/abs/1908.10084",
560
+ }
561
+ ```
562
+
563
+ #### CoSENTLoss
564
+ ```bibtex
565
+ @online{kexuefm-8847,
566
+ title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
567
+ author={Su Jianlin},
568
+ year={2022},
569
+ month={Jan},
570
+ url={https://kexue.fm/archives/8847},
571
+ }
572
+ ```
573
+
574
+ <!--
575
+ ## Glossary
576
+
577
+ *Clearly define terms in order to be accessible across audiences.*
578
+ -->
579
+
580
+ <!--
581
+ ## Model Card Authors
582
+
583
+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
584
+ -->
585
+
586
+ <!--
587
+ ## Model Card Contact
588
+
589
+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
590
+ -->
checkpoint-884/added_tokens.json ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ {
2
+ "[PAD]": 32000
3
+ }
checkpoint-884/config.json ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "colorfulscoop/sbert-base-ja",
3
+ "architectures": [
4
+ "BertModel"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.1,
7
+ "bos_token_id": 2,
8
+ "classifier_dropout": null,
9
+ "cls_token_id": 2,
10
+ "eos_token_id": 3,
11
+ "gradient_checkpointing": false,
12
+ "hidden_act": "gelu",
13
+ "hidden_dropout_prob": 0.1,
14
+ "hidden_size": 768,
15
+ "initializer_range": 0.02,
16
+ "intermediate_size": 3072,
17
+ "layer_norm_eps": 1e-12,
18
+ "mask_token_id": 4,
19
+ "max_position_embeddings": 512,
20
+ "model_type": "bert",
21
+ "num_attention_heads": 12,
22
+ "num_hidden_layers": 12,
23
+ "pad_token_id": 0,
24
+ "position_embedding_type": "absolute",
25
+ "sep_token_id": 3,
26
+ "tokenizer_class": "DebertaV2Tokenizer",
27
+ "torch_dtype": "float32",
28
+ "transformers_version": "4.44.2",
29
+ "type_vocab_size": 2,
30
+ "unk_token_id": 1,
31
+ "use_cache": true,
32
+ "vocab_size": 32000
33
+ }
checkpoint-884/config_sentence_transformers.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "__version__": {
3
+ "sentence_transformers": "3.1.0",
4
+ "transformers": "4.44.2",
5
+ "pytorch": "2.4.1+cu121"
6
+ },
7
+ "prompts": {},
8
+ "default_prompt_name": null,
9
+ "similarity_fn_name": null
10
+ }
checkpoint-884/model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6ea971648a5f2a8763054e8e39824b1f5acc795ea1e4e11c3565210e0f89f56c
3
+ size 442491744
checkpoint-884/modules.json ADDED
@@ -0,0 +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
+ ]
checkpoint-884/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a0d46a15c28464cd9937ebba27b97d8e74d87d66ef07628f0b7444366fa0c673
3
+ size 880373306
checkpoint-884/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:acb7f3930feaeca11a54945463fe298db1ad6a0449ea14aa24e77a866f871390
3
+ size 13990
checkpoint-884/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:472e7088b090d418cf1a50a3ea1f58423524f7b2af2e0abb5273be0d15b293f0
3
+ size 1064
checkpoint-884/sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 512,
3
+ "do_lower_case": false
4
+ }
checkpoint-884/special_tokens_map.json ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": "[CLS]",
3
+ "cls_token": "[CLS]",
4
+ "eos_token": "[SEP]",
5
+ "mask_token": "[MASK]",
6
+ "pad_token": "<pad>",
7
+ "sep_token": "[SEP]",
8
+ "unk_token": {
9
+ "content": "<unk>",
10
+ "lstrip": false,
11
+ "normalized": true,
12
+ "rstrip": false,
13
+ "single_word": false
14
+ }
15
+ }
checkpoint-884/spm.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d6467857b4b0c77ded9bac7ad2fb5c16eb64e17e417ce46624dacac2bbb404fc
3
+ size 802713
checkpoint-884/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-884/tokenizer_config.json ADDED
@@ -0,0 +1,65 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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": true,
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": false
26
+ },
27
+ "3": {
28
+ "content": "[SEP]",
29
+ "lstrip": false,
30
+ "normalized": false,
31
+ "rstrip": false,
32
+ "single_word": false,
33
+ "special": false
34
+ },
35
+ "4": {
36
+ "content": "[MASK]",
37
+ "lstrip": false,
38
+ "normalized": false,
39
+ "rstrip": false,
40
+ "single_word": false,
41
+ "special": false
42
+ },
43
+ "32000": {
44
+ "content": "[PAD]",
45
+ "lstrip": false,
46
+ "normalized": true,
47
+ "rstrip": false,
48
+ "single_word": false,
49
+ "special": false
50
+ }
51
+ },
52
+ "bos_token": "[CLS]",
53
+ "clean_up_tokenization_spaces": true,
54
+ "cls_token": "[CLS]",
55
+ "do_lower_case": false,
56
+ "eos_token": "[SEP]",
57
+ "mask_token": "[MASK]",
58
+ "model_max_length": 512,
59
+ "pad_token": "<pad>",
60
+ "sep_token": "[SEP]",
61
+ "sp_model_kwargs": {},
62
+ "split_by_punct": false,
63
+ "tokenizer_class": "DebertaV2Tokenizer",
64
+ "unk_token": "<unk>"
65
+ }
checkpoint-884/trainer_state.json ADDED
@@ -0,0 +1,683 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 13.0,
5
+ "eval_steps": 50,
6
+ "global_step": 884,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 1.0,
13
+ "grad_norm": 37.9459228515625,
14
+ "learning_rate": 1.5280898876404495e-05,
15
+ "loss": 2.6802,
16
+ "step": 68
17
+ },
18
+ {
19
+ "epoch": 1.0,
20
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.8529411764705882,
21
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.7407679557800293,
22
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.8861687056931384,
23
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.863013698630137,
24
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.7407679557800293,
25
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.8873239436619719,
26
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.84,
27
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8529411764705882,
28
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 413.34197998046875,
29
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.887218522968793,
30
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.8648648648648648,
31
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 413.34197998046875,
32
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.8767123287671232,
33
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8533333333333334,
34
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.8602941176470589,
35
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 17.441856384277344,
36
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.8855166425710176,
37
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.87248322147651,
38
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 17.441856384277344,
39
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.8783783783783784,
40
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.8666666666666667,
41
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.8602941176470589,
42
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 382.9211730957031,
43
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.8854876210985647,
44
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.8741721854304636,
45
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 386.8717956542969,
46
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.868421052631579,
47
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.88,
48
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.8602941176470589,
49
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 413.34197998046875,
50
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.887218522968793,
51
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.8741721854304636,
52
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 413.34197998046875,
53
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.8873239436619719,
54
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.88,
55
+ "eval_loss": 1.7807204723358154,
56
+ "eval_runtime": 14.8133,
57
+ "eval_samples_per_second": 9.181,
58
+ "eval_steps_per_second": 1.148,
59
+ "step": 68
60
+ },
61
+ {
62
+ "epoch": 2.0,
63
+ "grad_norm": 101.59432983398438,
64
+ "learning_rate": 1.8817610062893082e-05,
65
+ "loss": 1.4014,
66
+ "step": 136
67
+ },
68
+ {
69
+ "epoch": 2.0,
70
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.8676470588235294,
71
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.656826376914978,
72
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.894049495028942,
73
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.8783783783783784,
74
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.656826376914978,
75
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.8904109589041096,
76
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.8666666666666667,
77
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8602941176470589,
78
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 367.9239501953125,
79
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.8915744113817081,
80
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.87248322147651,
81
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 363.2611999511719,
82
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.8783783783783784,
83
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8666666666666667,
84
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.8602941176470589,
85
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 19.78958511352539,
86
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.8925974243856872,
87
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.8819875776397516,
88
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 20.95208740234375,
89
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.8255813953488372,
90
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9466666666666667,
91
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.875,
92
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 456.61602783203125,
93
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.8944639878933996,
94
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.8930817610062893,
95
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 456.61602783203125,
96
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.8452380952380952,
97
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9466666666666667,
98
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.875,
99
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 456.61602783203125,
100
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.8944639878933996,
101
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.8930817610062893,
102
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 456.61602783203125,
103
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.8904109589041096,
104
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9466666666666667,
105
+ "eval_loss": 1.7683357000350952,
106
+ "eval_runtime": 13.9834,
107
+ "eval_samples_per_second": 9.726,
108
+ "eval_steps_per_second": 1.216,
109
+ "step": 136
110
+ },
111
+ {
112
+ "epoch": 3.0,
113
+ "grad_norm": 102.5489730834961,
114
+ "learning_rate": 1.7106918238993714e-05,
115
+ "loss": 0.7937,
116
+ "step": 204
117
+ },
118
+ {
119
+ "epoch": 3.0,
120
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.8897058823529411,
121
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5597226619720459,
122
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9038906301326961,
123
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.9006622516556291,
124
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5076813697814941,
125
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.8947368421052632,
126
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
127
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8823529411764706,
128
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 314.1024475097656,
129
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9012158574599884,
130
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.8904109589041095,
131
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 295.25762939453125,
132
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9154929577464789,
133
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8666666666666667,
134
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.8970588235294118,
135
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 22.511234283447266,
136
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9033993255493061,
137
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.9054054054054055,
138
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 22.511234283447266,
139
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9178082191780822,
140
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.8933333333333333,
141
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.8897058823529411,
142
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 480.63677978515625,
143
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9033851155012284,
144
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.8993288590604026,
145
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 502.66156005859375,
146
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9054054054054054,
147
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.8933333333333333,
148
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.8970588235294118,
149
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 480.63677978515625,
150
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9038906301326961,
151
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.9054054054054055,
152
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 502.66156005859375,
153
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9178082191780822,
154
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
155
+ "eval_loss": 1.9876813888549805,
156
+ "eval_runtime": 15.0797,
157
+ "eval_samples_per_second": 9.019,
158
+ "eval_steps_per_second": 1.127,
159
+ "step": 204
160
+ },
161
+ {
162
+ "epoch": 4.0,
163
+ "grad_norm": 54.64971160888672,
164
+ "learning_rate": 1.539622641509434e-05,
165
+ "loss": 0.5443,
166
+ "step": 272
167
+ },
168
+ {
169
+ "epoch": 4.0,
170
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9117647058823529,
171
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5411365032196045,
172
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9075251070302803,
173
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.918918918918919,
174
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5411365032196045,
175
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.9315068493150684,
176
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
177
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8897058823529411,
178
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 311.26678466796875,
179
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9040853015076152,
180
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9006622516556291,
181
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 285.96728515625,
182
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.8947368421052632,
183
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.9066666666666666,
184
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
185
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 22.630477905273438,
186
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.906411643380887,
187
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
188
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 22.630477905273438,
189
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
190
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
191
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
192
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 495.93194580078125,
193
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9068108312799359,
194
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
195
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 495.93194580078125,
196
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
197
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
198
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
199
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 495.93194580078125,
200
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9075251070302803,
201
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
202
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 495.93194580078125,
203
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9315068493150684,
204
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
205
+ "eval_loss": 1.910571813583374,
206
+ "eval_runtime": 14.3178,
207
+ "eval_samples_per_second": 9.499,
208
+ "eval_steps_per_second": 1.187,
209
+ "step": 272
210
+ },
211
+ {
212
+ "epoch": 5.0,
213
+ "grad_norm": 7.771420955657959,
214
+ "learning_rate": 1.368553459119497e-05,
215
+ "loss": 0.4225,
216
+ "step": 340
217
+ },
218
+ {
219
+ "epoch": 5.0,
220
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9117647058823529,
221
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5279971361160278,
222
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9104573720737034,
223
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.918918918918919,
224
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5279971361160278,
225
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.9315068493150684,
226
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
227
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9044117647058824,
228
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 319.6995849609375,
229
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.907606464463115,
230
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9115646258503401,
231
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 290.19317626953125,
232
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9305555555555556,
233
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8933333333333333,
234
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
235
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 22.626989364624023,
236
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9092245976210003,
237
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
238
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 22.626989364624023,
239
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
240
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
241
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
242
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 494.6650390625,
243
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9108555121538641,
244
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.92,
245
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 514.7510986328125,
246
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.92,
247
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.92,
248
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
249
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 494.6650390625,
250
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9108555121538641,
251
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.92,
252
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 514.7510986328125,
253
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9315068493150684,
254
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.92,
255
+ "eval_loss": 1.9418467283248901,
256
+ "eval_runtime": 14.4159,
257
+ "eval_samples_per_second": 9.434,
258
+ "eval_steps_per_second": 1.179,
259
+ "step": 340
260
+ },
261
+ {
262
+ "epoch": 6.0,
263
+ "grad_norm": 0.8858943581581116,
264
+ "learning_rate": 1.1974842767295597e-05,
265
+ "loss": 0.3347,
266
+ "step": 408
267
+ },
268
+ {
269
+ "epoch": 6.0,
270
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9117647058823529,
271
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5068449974060059,
272
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9088529074244793,
273
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.918918918918919,
274
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5068449974060059,
275
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.9315068493150684,
276
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
277
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.8970588235294118,
278
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 322.01324462890625,
279
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9107273300403762,
280
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9054054054054055,
281
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 276.34893798828125,
282
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9178082191780822,
283
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8933333333333333,
284
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
285
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.167011260986328,
286
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9088745561179659,
287
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
288
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.167011260986328,
289
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
290
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
291
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
292
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 506.7710266113281,
293
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9092068556819233,
294
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
295
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 506.7710266113281,
296
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
297
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
298
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
299
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 506.7710266113281,
300
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9107273300403762,
301
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
302
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 506.7710266113281,
303
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9315068493150684,
304
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
305
+ "eval_loss": 2.012265920639038,
306
+ "eval_runtime": 13.9077,
307
+ "eval_samples_per_second": 9.779,
308
+ "eval_steps_per_second": 1.222,
309
+ "step": 408
310
+ },
311
+ {
312
+ "epoch": 7.0,
313
+ "grad_norm": 0.24478188157081604,
314
+ "learning_rate": 1.0264150943396227e-05,
315
+ "loss": 0.3425,
316
+ "step": 476
317
+ },
318
+ {
319
+ "epoch": 7.0,
320
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
321
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5215883255004883,
322
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9081430244026829,
323
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
324
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5215883255004883,
325
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
326
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
327
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9044117647058824,
328
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 313.0162353515625,
329
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9094090969210814,
330
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9090909090909091,
331
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 313.0162353515625,
332
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9558823529411765,
333
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.8666666666666667,
334
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9044117647058824,
335
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.342838287353516,
336
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.907981722850528,
337
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.912751677852349,
338
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.342838287353516,
339
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.918918918918919,
340
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
341
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9044117647058824,
342
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 511.61737060546875,
343
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9085688725632555,
344
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.912751677852349,
345
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 511.61737060546875,
346
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.918918918918919,
347
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
348
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9044117647058824,
349
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 511.61737060546875,
350
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9094090969210814,
351
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.912751677852349,
352
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 511.61737060546875,
353
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9558823529411765,
354
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
355
+ "eval_loss": 2.038686752319336,
356
+ "eval_runtime": 14.7627,
357
+ "eval_samples_per_second": 9.212,
358
+ "eval_steps_per_second": 1.152,
359
+ "step": 476
360
+ },
361
+ {
362
+ "epoch": 8.0,
363
+ "grad_norm": 0.09618318825960159,
364
+ "learning_rate": 8.553459119496857e-06,
365
+ "loss": 0.2427,
366
+ "step": 544
367
+ },
368
+ {
369
+ "epoch": 8.0,
370
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
371
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5501157641410828,
372
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9086817589029806,
373
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
374
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.5119773149490356,
375
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
376
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
377
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
378
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 294.31005859375,
379
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9096226292878296,
380
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
381
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 294.31005859375,
382
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
383
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
384
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
385
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.28342056274414,
386
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9089227316320502,
387
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
388
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.28342056274414,
389
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
390
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
391
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
392
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 509.5169677734375,
393
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9102569832574411,
394
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
395
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 509.5169677734375,
396
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
397
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
398
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
399
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 509.5169677734375,
400
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9102569832574411,
401
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
402
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 509.5169677734375,
403
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
404
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
405
+ "eval_loss": 1.9878324270248413,
406
+ "eval_runtime": 13.7764,
407
+ "eval_samples_per_second": 9.872,
408
+ "eval_steps_per_second": 1.234,
409
+ "step": 544
410
+ },
411
+ {
412
+ "epoch": 9.0,
413
+ "grad_norm": 0.046123113483190536,
414
+ "learning_rate": 6.842767295597485e-06,
415
+ "loss": 0.2412,
416
+ "step": 612
417
+ },
418
+ {
419
+ "epoch": 9.0,
420
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
421
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5531296730041504,
422
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9088232956862466,
423
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
424
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.49595877528190613,
425
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
426
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
427
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
428
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 295.7380676269531,
429
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9178358242971421,
430
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
431
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 295.7380676269531,
432
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
433
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
434
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
435
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.565818786621094,
436
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9092338106583002,
437
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
438
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.565818786621094,
439
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
440
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
441
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
442
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 515.80322265625,
443
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9102278723159124,
444
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
445
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 515.80322265625,
446
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
447
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
448
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
449
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 515.80322265625,
450
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9178358242971421,
451
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
452
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 515.80322265625,
453
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
454
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
455
+ "eval_loss": 2.0423502922058105,
456
+ "eval_runtime": 18.8817,
457
+ "eval_samples_per_second": 7.203,
458
+ "eval_steps_per_second": 0.9,
459
+ "step": 612
460
+ },
461
+ {
462
+ "epoch": 10.0,
463
+ "grad_norm": 0.029908249154686928,
464
+ "learning_rate": 5.1320754716981136e-06,
465
+ "loss": 0.1623,
466
+ "step": 680
467
+ },
468
+ {
469
+ "epoch": 10.0,
470
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
471
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5516438484191895,
472
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9085774598037802,
473
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.9103448275862068,
474
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.519783079624176,
475
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.9428571428571428,
476
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.88,
477
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
478
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 294.6772766113281,
479
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.918847565389113,
480
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
481
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 294.6772766113281,
482
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
483
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
484
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9044117647058824,
485
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 21.938934326171875,
486
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9089511554768472,
487
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.912751677852349,
488
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.713035583496094,
489
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.918918918918919,
490
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
491
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9044117647058824,
492
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 482.1662292480469,
493
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.910254731797207,
494
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.912751677852349,
495
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 524.188232421875,
496
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.918918918918919,
497
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
498
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
499
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 482.1662292480469,
500
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.918847565389113,
501
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.9166666666666666,
502
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 524.188232421875,
503
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
504
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
505
+ "eval_loss": 2.0273122787475586,
506
+ "eval_runtime": 20.5658,
507
+ "eval_samples_per_second": 6.613,
508
+ "eval_steps_per_second": 0.827,
509
+ "step": 680
510
+ },
511
+ {
512
+ "epoch": 11.0,
513
+ "grad_norm": 0.007907501421868801,
514
+ "learning_rate": 3.4213836477987424e-06,
515
+ "loss": 0.1909,
516
+ "step": 748
517
+ },
518
+ {
519
+ "epoch": 11.0,
520
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
521
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.550028383731842,
522
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9090819039672458,
523
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
524
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.48157936334609985,
525
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
526
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
527
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
528
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 293.80010986328125,
529
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9219827879602106,
530
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
531
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 293.80010986328125,
532
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
533
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
534
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
535
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.849559783935547,
536
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.90922024992935,
537
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
538
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.849559783935547,
539
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
540
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
541
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9044117647058824,
542
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 482.9535827636719,
543
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.910689468531078,
544
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.912751677852349,
545
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 527.540771484375,
546
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.918918918918919,
547
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
548
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
549
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 482.9535827636719,
550
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9219827879602106,
551
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
552
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 527.540771484375,
553
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
554
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
555
+ "eval_loss": 2.0955376625061035,
556
+ "eval_runtime": 20.4117,
557
+ "eval_samples_per_second": 6.663,
558
+ "eval_steps_per_second": 0.833,
559
+ "step": 748
560
+ },
561
+ {
562
+ "epoch": 12.0,
563
+ "grad_norm": 0.3338662087917328,
564
+ "learning_rate": 1.7106918238993712e-06,
565
+ "loss": 0.1507,
566
+ "step": 816
567
+ },
568
+ {
569
+ "epoch": 12.0,
570
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
571
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5501536726951599,
572
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9084179566135925,
573
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
574
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.4790937304496765,
575
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
576
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
577
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
578
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 294.13421630859375,
579
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.915716305189008,
580
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
581
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 294.13421630859375,
582
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
583
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
584
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
585
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.818954467773438,
586
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9093211275077335,
587
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
588
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.818954467773438,
589
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
590
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
591
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9044117647058824,
592
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 482.6566162109375,
593
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9104676924615509,
594
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.913907284768212,
595
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 532.9744262695312,
596
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9078947368421053,
597
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.92,
598
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
599
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 482.6566162109375,
600
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.915716305189008,
601
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
602
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 532.9744262695312,
603
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
604
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.92,
605
+ "eval_loss": 2.2124059200286865,
606
+ "eval_runtime": 20.6137,
607
+ "eval_samples_per_second": 6.598,
608
+ "eval_steps_per_second": 0.825,
609
+ "step": 816
610
+ },
611
+ {
612
+ "epoch": 13.0,
613
+ "grad_norm": 0.01023839507251978,
614
+ "learning_rate": 0.0,
615
+ "loss": 0.1406,
616
+ "step": 884
617
+ },
618
+ {
619
+ "epoch": 13.0,
620
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy": 0.9044117647058824,
621
+ "eval_custom-arc-semantics-data-jp_cosine_accuracy_threshold": 0.5485918521881104,
622
+ "eval_custom-arc-semantics-data-jp_cosine_ap": 0.9088999169341241,
623
+ "eval_custom-arc-semantics-data-jp_cosine_f1": 0.912751677852349,
624
+ "eval_custom-arc-semantics-data-jp_cosine_f1_threshold": 0.47659817337989807,
625
+ "eval_custom-arc-semantics-data-jp_cosine_precision": 0.918918918918919,
626
+ "eval_custom-arc-semantics-data-jp_cosine_recall": 0.9066666666666666,
627
+ "eval_custom-arc-semantics-data-jp_dot_accuracy": 0.9117647058823529,
628
+ "eval_custom-arc-semantics-data-jp_dot_accuracy_threshold": 293.22845458984375,
629
+ "eval_custom-arc-semantics-data-jp_dot_ap": 0.9171086358892895,
630
+ "eval_custom-arc-semantics-data-jp_dot_f1": 0.9166666666666666,
631
+ "eval_custom-arc-semantics-data-jp_dot_f1_threshold": 293.22845458984375,
632
+ "eval_custom-arc-semantics-data-jp_dot_precision": 0.9565217391304348,
633
+ "eval_custom-arc-semantics-data-jp_dot_recall": 0.88,
634
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy": 0.9117647058823529,
635
+ "eval_custom-arc-semantics-data-jp_euclidean_accuracy_threshold": 23.82940673828125,
636
+ "eval_custom-arc-semantics-data-jp_euclidean_ap": 0.9094221163568814,
637
+ "eval_custom-arc-semantics-data-jp_euclidean_f1": 0.918918918918919,
638
+ "eval_custom-arc-semantics-data-jp_euclidean_f1_threshold": 23.82940673828125,
639
+ "eval_custom-arc-semantics-data-jp_euclidean_precision": 0.9315068493150684,
640
+ "eval_custom-arc-semantics-data-jp_euclidean_recall": 0.9066666666666666,
641
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy": 0.9117647058823529,
642
+ "eval_custom-arc-semantics-data-jp_manhattan_accuracy_threshold": 524.0676879882812,
643
+ "eval_custom-arc-semantics-data-jp_manhattan_ap": 0.9111567321590129,
644
+ "eval_custom-arc-semantics-data-jp_manhattan_f1": 0.918918918918919,
645
+ "eval_custom-arc-semantics-data-jp_manhattan_f1_threshold": 524.0676879882812,
646
+ "eval_custom-arc-semantics-data-jp_manhattan_precision": 0.9315068493150684,
647
+ "eval_custom-arc-semantics-data-jp_manhattan_recall": 0.9066666666666666,
648
+ "eval_custom-arc-semantics-data-jp_max_accuracy": 0.9117647058823529,
649
+ "eval_custom-arc-semantics-data-jp_max_accuracy_threshold": 524.0676879882812,
650
+ "eval_custom-arc-semantics-data-jp_max_ap": 0.9171086358892895,
651
+ "eval_custom-arc-semantics-data-jp_max_f1": 0.918918918918919,
652
+ "eval_custom-arc-semantics-data-jp_max_f1_threshold": 524.0676879882812,
653
+ "eval_custom-arc-semantics-data-jp_max_precision": 0.9565217391304348,
654
+ "eval_custom-arc-semantics-data-jp_max_recall": 0.9066666666666666,
655
+ "eval_loss": 2.2125871181488037,
656
+ "eval_runtime": 20.4613,
657
+ "eval_samples_per_second": 6.647,
658
+ "eval_steps_per_second": 0.831,
659
+ "step": 884
660
+ }
661
+ ],
662
+ "logging_steps": 500,
663
+ "max_steps": 884,
664
+ "num_input_tokens_seen": 0,
665
+ "num_train_epochs": 13,
666
+ "save_steps": 100,
667
+ "stateful_callbacks": {
668
+ "TrainerControl": {
669
+ "args": {
670
+ "should_epoch_stop": false,
671
+ "should_evaluate": false,
672
+ "should_log": false,
673
+ "should_save": true,
674
+ "should_training_stop": true
675
+ },
676
+ "attributes": {}
677
+ }
678
+ },
679
+ "total_flos": 0.0,
680
+ "train_batch_size": 8,
681
+ "trial_name": null,
682
+ "trial_params": null
683
+ }
checkpoint-884/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cb4f571730ddebdbfd409aa41c36173377c8d33a0e18444875f0d792c4278e37
3
+ size 5432
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:0900bde2010bc7e2818d70b31e4bbe7106bdb90d812cf99c8f8921b69fb1d8f2
3
  size 442491744
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6ea971648a5f2a8763054e8e39824b1f5acc795ea1e4e11c3565210e0f89f56c
3
  size 442491744
runs/Sep03_22-46-20_default/events.out.tfevents.1725403583.default.1138.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bb8fd5bfc896f65a3d434039684a6cabfaddb87865aa16c2b2d8090cac014f6f
3
+ size 49331
runs/Sep04_17-30-25_default/events.out.tfevents.1725471030.default.394.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:46d31cb7eb223ba8dc96326c2462b6eef63d993f82bd3fbc703df4590623056d
3
+ size 35129
runs/Sep04_21-08-57_default/events.out.tfevents.1725484141.default.793.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:85bf0744933646bd101ae62fb2710a14a3b11e7fc0d0f26abbda6ff31e213948
3
+ size 49463
runs/Sep11_17-50-24_default/events.out.tfevents.1726077038.default.828.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d9830d26f5e7833d13cb1631f7af76dd8bfd56db34468f72bb2301fb046d8cba
3
+ size 25624
runs/Sep11_18-02-35_default/events.out.tfevents.1726077764.default.959.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7514d733eb7e908dde3f2aa6f1950bc7038fe646195029a27f454b5854ef6347
3
+ size 4184
runs/Sep11_18-05-21_default/events.out.tfevents.1726077928.default.1078.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f5639a4eda642b609a41df68e6a6b5bb880730593b76c8a871b949b91bd5a0b5
3
+ size 50916
runs/Sep11_23-48-08_default/events.out.tfevents.1726098512.default.5852.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:38a5c6822acc517b03e1f08527ee0bad0e8f50e86d9bbcf3e82141840d2633b0
3
+ size 4278
runs/Sep12_00-21-44_default/events.out.tfevents.1726100510.default.6560.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:12e163434007f9913a647fafb29bb7d6b8c4945795162667798c8c263ed84815
3
+ size 15289
runs/Sep12_00-34-34_default/events.out.tfevents.1726101282.default.6715.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5661cb6e830be489024cb91ad77b592057ebd50e5239353a37af89bbb476e611
3
+ size 50960
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d6467857b4b0c77ded9bac7ad2fb5c16eb64e17e417ce46624dacac2bbb404fc
3
+ size 802713
tokenizer_config.json CHANGED
@@ -1,65 +1,15 @@
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": true,
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": false
26
- },
27
- "3": {
28
- "content": "[SEP]",
29
- "lstrip": false,
30
- "normalized": false,
31
- "rstrip": false,
32
- "single_word": false,
33
- "special": false
34
- },
35
- "4": {
36
- "content": "[MASK]",
37
- "lstrip": false,
38
- "normalized": false,
39
- "rstrip": false,
40
- "single_word": false,
41
- "special": false
42
- },
43
- "32000": {
44
- "content": "[PAD]",
45
- "lstrip": false,
46
- "normalized": true,
47
- "rstrip": false,
48
- "single_word": false,
49
- "special": false
50
- }
51
- },
52
- "bos_token": "[CLS]",
53
- "clean_up_tokenization_spaces": true,
54
- "cls_token": "[CLS]",
55
- "do_lower_case": false,
56
- "eos_token": "[SEP]",
57
- "mask_token": "[MASK]",
58
- "model_max_length": 512,
59
- "pad_token": "<pad>",
60
- "sep_token": "[SEP]",
61
- "sp_model_kwargs": {},
62
- "split_by_punct": false,
63
- "tokenizer_class": "DebertaV2Tokenizer",
64
- "unk_token": "<unk>"
65
- }
 
1
  {
2
+ "bos_token": "[CLS]",
3
+ "clean_up_tokenization_spaces": true,
4
+ "cls_token": "[CLS]",
5
+ "do_lower_case": false,
6
+ "eos_token": "[SEP]",
7
+ "mask_token": "[MASK]",
8
+ "model_max_length": 512,
9
+ "pad_token": "<pad>",
10
+ "sep_token": "[SEP]",
11
+ "sp_model_kwargs": {},
12
+ "split_by_punct": false,
13
+ "tokenizer_class": "DebertaV2Tokenizer",
14
+ "unk_token": "<unk>"
15
+ }