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  1. README.md +10 -12
  2. checkpoint-100/model.safetensors +1 -1
  3. checkpoint-100/optimizer.pt +1 -1
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  37. runs/Sep14_17-06-50_ubumarcos/events.out.tfevents.1726327100.ubumarcos +3 -0
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README.md CHANGED
@@ -21,12 +21,12 @@ should probably proofread and complete it, then remove this comment. -->
21
 
22
  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset.
23
  It achieves the following results on the evaluation set:
24
- - Loss: 0.7837
25
- - Accuracy: 0.8498
26
- - F1: 0.8473
27
- - Precision: 0.8494
28
- - Recall: 0.8498
29
- - Confusion Matrix: [[76, 11, 0, 0], [7, 35, 17, 0], [0, 5, 59, 0], [1, 0, 0, 62]]
30
 
31
  ## Model description
32
 
@@ -46,24 +46,22 @@ More information needed
46
 
47
  The following hyperparameters were used during training:
48
  - learning_rate: 0.0003
49
- - train_batch_size: 64
50
- - eval_batch_size: 64
51
  - seed: 123
52
  - gradient_accumulation_steps: 2
53
- - total_train_batch_size: 128
54
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
55
  - lr_scheduler_type: cosine_with_restarts
56
  - lr_scheduler_warmup_ratio: 0.1
57
  - num_epochs: 30
58
- - mixed_precision_training: Native AMP
59
  - label_smoothing_factor: 0.1
60
 
61
  ### Training results
62
 
63
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Confusion Matrix |
64
  |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------:|
65
- | 0.5938 | 11.1111 | 100 | 0.7100 | 0.8278 | 0.8206 | 0.8212 | 0.8278 | [[79, 7, 0, 1], [11, 29, 19, 0], [0, 8, 56, 0], [1, 0, 0, 62]] |
66
- | 0.3511 | 22.2222 | 200 | 0.7837 | 0.8498 | 0.8473 | 0.8494 | 0.8498 | [[76, 11, 0, 0], [7, 35, 17, 0], [0, 5, 59, 0], [1, 0, 0, 62]] |
67
 
68
 
69
  ### Framework versions
 
21
 
22
  This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on an unknown dataset.
23
  It achieves the following results on the evaluation set:
24
+ - Loss: 0.7942
25
+ - Accuracy: 0.8242
26
+ - F1: 0.8278
27
+ - Precision: 0.8347
28
+ - Recall: 0.8242
29
+ - Confusion Matrix: [[51, 10, 0, 2], [5, 44, 9, 0], [1, 14, 67, 0], [7, 0, 0, 63]]
30
 
31
  ## Model description
32
 
 
46
 
47
  The following hyperparameters were used during training:
48
  - learning_rate: 0.0003
49
+ - train_batch_size: 128
50
+ - eval_batch_size: 128
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  - seed: 123
52
  - gradient_accumulation_steps: 2
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+ - total_train_batch_size: 256
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine_with_restarts
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  - lr_scheduler_warmup_ratio: 0.1
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  - num_epochs: 30
 
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  - label_smoothing_factor: 0.1
59
 
60
  ### Training results
61
 
62
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Confusion Matrix |
63
  |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:--------------------------------------------------------------:|
64
+ | 0.3691 | 22.2222 | 100 | 0.7942 | 0.8242 | 0.8278 | 0.8347 | 0.8242 | [[51, 10, 0, 2], [5, 44, 9, 0], [1, 14, 67, 0], [7, 0, 0, 63]] |
 
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  ### Framework versions
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