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bobbyw
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copilot_relex_v1

Text Classification
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Safetensors
deberta-v2
Generated from Trainer
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End of training

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  1. README.md +113 -32
  2. model.safetensors +1 -1
  3. runs/May01_02-33-22_93ad8adf7f6f/events.out.tfevents.1714530809.93ad8adf7f6f.2125.0 +2 -2
README.md CHANGED
@@ -20,11 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0374
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- - Accuracy: 0.0069
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- - F1: 0.0137
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- - Precision: 0.0069
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- - Recall: 1.0
 
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  ## Model description
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@@ -43,43 +44,123 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.002
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 20
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54
  ### Training results
55
 
56
- | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
57
- |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | No log | 1.0 | 39 | 0.0447 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 2.0 | 78 | 0.0401 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 3.0 | 117 | 0.0401 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 4.0 | 156 | 0.0395 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 5.0 | 195 | 0.0379 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 6.0 | 234 | 0.0374 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 7.0 | 273 | 0.0383 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 8.0 | 312 | 0.0378 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 9.0 | 351 | 0.0384 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 10.0 | 390 | 0.0371 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 11.0 | 429 | 0.0378 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | No log | 12.0 | 468 | 0.0371 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | 0.037 | 13.0 | 507 | 0.0373 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | 0.037 | 14.0 | 546 | 0.0380 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | 0.037 | 15.0 | 585 | 0.0390 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | 0.037 | 16.0 | 624 | 0.0373 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | 0.037 | 17.0 | 663 | 0.0376 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | 0.037 | 18.0 | 702 | 0.0371 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | 0.037 | 19.0 | 741 | 0.0375 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
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- | 0.037 | 20.0 | 780 | 0.0374 | 0.0069 | 0.0137 | 0.0069 | 1.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.40.0
83
  - Pytorch 2.2.1+cu121
84
  - Datasets 2.19.0
85
  - Tokenizers 0.19.1
 
20
 
21
  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.0134
24
+ - Accuracy: 0.0038
25
+ - F1: 0.0062
26
+ - Precision: 0.0031
27
+ - Recall: 0.625
28
+ - Learning Rate: 0.0
29
 
30
  ## Model description
31
 
 
44
  ### Training hyperparameters
45
 
46
  The following hyperparameters were used during training:
47
+ - learning_rate: 2e-05
48
+ - train_batch_size: 32
49
+ - eval_batch_size: 32
50
  - seed: 42
51
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
52
  - lr_scheduler_type: linear
53
+ - num_epochs: 100
54
 
55
  ### Training results
56
 
57
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Rate |
58
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
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+ | No log | 1.0 | 20 | 0.5469 | 0.0994 | 0.0093 | 0.0047 | 0.8438 | 0.0000 |
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+ | No log | 2.0 | 40 | 0.3859 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 3.0 | 60 | 0.2662 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 4.0 | 80 | 0.1781 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 5.0 | 100 | 0.1183 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 6.0 | 120 | 0.0823 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 7.0 | 140 | 0.0614 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 8.0 | 160 | 0.0494 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 9.0 | 180 | 0.0423 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 10.0 | 200 | 0.0379 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 11.0 | 220 | 0.0350 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 12.0 | 240 | 0.0331 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 13.0 | 260 | 0.0318 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 14.0 | 280 | 0.0307 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 15.0 | 300 | 0.0300 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 16.0 | 320 | 0.0294 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 17.0 | 340 | 0.0290 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 18.0 | 360 | 0.0286 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 19.0 | 380 | 0.0283 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 20.0 | 400 | 0.0300 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 21.0 | 420 | 0.0290 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 22.0 | 440 | 0.0252 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 23.0 | 460 | 0.0246 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | No log | 24.0 | 480 | 0.0242 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1127 | 25.0 | 500 | 0.0239 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1127 | 26.0 | 520 | 0.0233 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1127 | 27.0 | 540 | 0.0226 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1127 | 28.0 | 560 | 0.0224 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1127 | 29.0 | 580 | 0.0217 | 0.0050 | 0.0100 | 0.0050 | 1.0 | 0.0000 |
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+ | 0.1127 | 30.0 | 600 | 0.0211 | 0.0047 | 0.0093 | 0.0047 | 0.9375 | 0.0000 |
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+ | 0.1127 | 31.0 | 620 | 0.0206 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 32.0 | 640 | 0.0207 | 0.0047 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 33.0 | 660 | 0.0198 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 34.0 | 680 | 0.0205 | 0.0047 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 35.0 | 700 | 0.0193 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 36.0 | 720 | 0.0198 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 37.0 | 740 | 0.0190 | 0.0047 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 38.0 | 760 | 0.0197 | 0.0049 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 39.0 | 780 | 0.0185 | 0.0047 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 40.0 | 800 | 0.0184 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 41.0 | 820 | 0.0188 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 42.0 | 840 | 0.0179 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 43.0 | 860 | 0.0178 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 44.0 | 880 | 0.0174 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 45.0 | 900 | 0.0182 | 0.0041 | 0.0081 | 0.0041 | 0.8125 | 0.0000 |
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+ | 0.1127 | 46.0 | 920 | 0.0171 | 0.0045 | 0.0090 | 0.0045 | 0.9062 | 0.0000 |
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+ | 0.1127 | 47.0 | 940 | 0.0168 | 0.0044 | 0.0087 | 0.0044 | 0.875 | 0.0000 |
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+ | 0.1127 | 48.0 | 960 | 0.0167 | 0.0041 | 0.0081 | 0.0041 | 0.8125 | 0.0000 |
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+ | 0.1127 | 49.0 | 980 | 0.0165 | 0.0039 | 0.0078 | 0.0039 | 0.7812 | 0.0000 |
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+ | 0.0253 | 50.0 | 1000 | 0.0162 | 0.0039 | 0.0078 | 0.0039 | 0.7812 | 1e-05 |
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+ | 0.0253 | 51.0 | 1020 | 0.0160 | 0.0041 | 0.0081 | 0.0041 | 0.8125 | 0.0000 |
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+ | 0.0253 | 52.0 | 1040 | 0.0159 | 0.0038 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
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+ | 0.0253 | 53.0 | 1060 | 0.0158 | 0.0038 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
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+ | 0.0253 | 54.0 | 1080 | 0.0163 | 0.0041 | 0.0075 | 0.0038 | 0.75 | 0.0000 |
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+ | 0.0253 | 55.0 | 1100 | 0.0160 | 0.0039 | 0.0072 | 0.0036 | 0.7188 | 9e-06 |
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+ | 0.0253 | 56.0 | 1120 | 0.0161 | 0.0034 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 57.0 | 1140 | 0.0156 | 0.0036 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 58.0 | 1160 | 0.0154 | 0.0041 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 59.0 | 1180 | 0.0155 | 0.0039 | 0.0072 | 0.0036 | 0.7188 | 0.0000 |
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+ | 0.0253 | 60.0 | 1200 | 0.0155 | 0.0036 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 61.0 | 1220 | 0.0154 | 0.0038 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 62.0 | 1240 | 0.0156 | 0.0041 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 63.0 | 1260 | 0.0152 | 0.0038 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 64.0 | 1280 | 0.0146 | 0.0036 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 65.0 | 1300 | 0.0147 | 0.0041 | 0.0069 | 0.0034 | 0.6875 | 7e-06 |
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+ | 0.0253 | 66.0 | 1320 | 0.0149 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0253 | 67.0 | 1340 | 0.0148 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.0253 | 68.0 | 1360 | 0.0148 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0253 | 69.0 | 1380 | 0.0143 | 0.0041 | 0.0069 | 0.0034 | 0.6875 | 0.0000 |
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+ | 0.0253 | 70.0 | 1400 | 0.0144 | 0.0039 | 0.0062 | 0.0031 | 0.625 | 6e-06 |
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+ | 0.0253 | 71.0 | 1420 | 0.0145 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0253 | 72.0 | 1440 | 0.0141 | 0.0038 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0253 | 73.0 | 1460 | 0.0144 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.0253 | 74.0 | 1480 | 0.0144 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.019 | 75.0 | 1500 | 0.0142 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 5e-06 |
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+ | 0.019 | 76.0 | 1520 | 0.0140 | 0.0041 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.019 | 77.0 | 1540 | 0.0139 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.019 | 78.0 | 1560 | 0.0140 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.019 | 79.0 | 1580 | 0.0139 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.019 | 80.0 | 1600 | 0.0139 | 0.0039 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.019 | 81.0 | 1620 | 0.0139 | 0.0042 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.019 | 82.0 | 1640 | 0.0136 | 0.0036 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.019 | 83.0 | 1660 | 0.0138 | 0.0041 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.019 | 84.0 | 1680 | 0.0136 | 0.0039 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.019 | 85.0 | 1700 | 0.0136 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 3e-06 |
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+ | 0.019 | 86.0 | 1720 | 0.0136 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.019 | 87.0 | 1740 | 0.0133 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.019 | 88.0 | 1760 | 0.0137 | 0.0039 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.019 | 89.0 | 1780 | 0.0134 | 0.0036 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
148
+ | 0.019 | 90.0 | 1800 | 0.0133 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.019 | 91.0 | 1820 | 0.0137 | 0.0041 | 0.0066 | 0.0033 | 0.6562 | 0.0000 |
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+ | 0.019 | 92.0 | 1840 | 0.0134 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
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+ | 0.019 | 93.0 | 1860 | 0.0135 | 0.0038 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
152
+ | 0.019 | 94.0 | 1880 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
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+ | 0.019 | 95.0 | 1900 | 0.0136 | 0.0039 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
154
+ | 0.019 | 96.0 | 1920 | 0.0135 | 0.0039 | 0.0059 | 0.0030 | 0.5938 | 0.0000 |
155
+ | 0.019 | 97.0 | 1940 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
156
+ | 0.019 | 98.0 | 1960 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
157
+ | 0.019 | 99.0 | 1980 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0000 |
158
+ | 0.0156 | 100.0 | 2000 | 0.0134 | 0.0038 | 0.0062 | 0.0031 | 0.625 | 0.0 |
159
 
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  ### Framework versions
162
 
163
+ - Transformers 4.40.1
164
  - Pytorch 2.2.1+cu121
165
  - Datasets 2.19.0
166
  - Tokenizers 0.19.1
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