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best_model-sst-2-16-13

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
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@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6710
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- - Accuracy: 0.7188
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  ## Model description
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@@ -37,69 +37,169 @@ 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: 2e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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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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  - lr_scheduler_warmup_steps: 500
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- - num_epochs: 50
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 1 | 0.6895 | 0.5312 |
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- | No log | 2.0 | 2 | 0.6895 | 0.5312 |
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- | No log | 3.0 | 3 | 0.6894 | 0.5312 |
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- | No log | 4.0 | 4 | 0.6894 | 0.5312 |
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- | No log | 5.0 | 5 | 0.6894 | 0.5312 |
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- | No log | 6.0 | 6 | 0.6893 | 0.5312 |
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- | No log | 7.0 | 7 | 0.6893 | 0.5312 |
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- | No log | 8.0 | 8 | 0.6892 | 0.5312 |
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- | No log | 9.0 | 9 | 0.6891 | 0.5312 |
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- | 0.7006 | 10.0 | 10 | 0.6890 | 0.5312 |
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- | 0.7006 | 11.0 | 11 | 0.6889 | 0.5312 |
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- | 0.7006 | 12.0 | 12 | 0.6888 | 0.5312 |
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- | 0.7006 | 13.0 | 13 | 0.6887 | 0.5312 |
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- | 0.7006 | 14.0 | 14 | 0.6886 | 0.5312 |
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- | 0.7006 | 15.0 | 15 | 0.6884 | 0.5312 |
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- | 0.7006 | 16.0 | 16 | 0.6883 | 0.5312 |
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- | 0.7006 | 17.0 | 17 | 0.6881 | 0.5312 |
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- | 0.7006 | 18.0 | 18 | 0.6879 | 0.5312 |
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- | 0.7006 | 19.0 | 19 | 0.6877 | 0.5312 |
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- | 0.6992 | 20.0 | 20 | 0.6875 | 0.5312 |
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- | 0.6992 | 21.0 | 21 | 0.6872 | 0.5312 |
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- | 0.6992 | 22.0 | 22 | 0.6870 | 0.5312 |
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- | 0.6992 | 23.0 | 23 | 0.6867 | 0.5 |
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- | 0.6992 | 24.0 | 24 | 0.6864 | 0.5 |
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- | 0.6992 | 25.0 | 25 | 0.6861 | 0.5 |
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- | 0.6992 | 26.0 | 26 | 0.6857 | 0.5 |
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- | 0.6992 | 27.0 | 27 | 0.6854 | 0.5 |
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- | 0.6992 | 28.0 | 28 | 0.6850 | 0.5 |
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- | 0.6992 | 29.0 | 29 | 0.6846 | 0.5 |
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- | 0.68 | 30.0 | 30 | 0.6842 | 0.5 |
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- | 0.68 | 31.0 | 31 | 0.6838 | 0.5 |
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- | 0.68 | 32.0 | 32 | 0.6833 | 0.5 |
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- | 0.68 | 33.0 | 33 | 0.6829 | 0.5 |
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- | 0.68 | 34.0 | 34 | 0.6824 | 0.5 |
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- | 0.68 | 35.0 | 35 | 0.6819 | 0.5 |
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- | 0.68 | 36.0 | 36 | 0.6814 | 0.5312 |
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- | 0.68 | 37.0 | 37 | 0.6808 | 0.5625 |
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- | 0.68 | 38.0 | 38 | 0.6802 | 0.5625 |
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- | 0.68 | 39.0 | 39 | 0.6796 | 0.5938 |
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- | 0.6655 | 40.0 | 40 | 0.6789 | 0.5938 |
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- | 0.6655 | 41.0 | 41 | 0.6783 | 0.5938 |
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- | 0.6655 | 42.0 | 42 | 0.6776 | 0.5938 |
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- | 0.6655 | 43.0 | 43 | 0.6769 | 0.6562 |
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- | 0.6655 | 44.0 | 44 | 0.6762 | 0.7188 |
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- | 0.6655 | 45.0 | 45 | 0.6754 | 0.7188 |
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- | 0.6655 | 46.0 | 46 | 0.6746 | 0.7188 |
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- | 0.6655 | 47.0 | 47 | 0.6737 | 0.75 |
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- | 0.6655 | 48.0 | 48 | 0.6728 | 0.75 |
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- | 0.6655 | 49.0 | 49 | 0.6719 | 0.75 |
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- | 0.6452 | 50.0 | 50 | 0.6710 | 0.7188 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
103
 
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  ### Framework versions
 
17
 
18
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 0.9572
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+ - Accuracy: 0.4688
22
 
23
  ## Model description
24
 
 
37
  ### Training hyperparameters
38
 
39
  The following hyperparameters were used during training:
40
+ - learning_rate: 1e-05
41
  - train_batch_size: 32
42
  - eval_batch_size: 32
43
  - seed: 42
44
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
45
  - lr_scheduler_type: linear
46
  - lr_scheduler_warmup_steps: 500
47
+ - num_epochs: 150
48
 
49
  ### Training results
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
+ | No log | 1.0 | 1 | 0.7460 | 0.5 |
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+ | No log | 2.0 | 2 | 0.7459 | 0.5 |
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+ | No log | 3.0 | 3 | 0.7459 | 0.5 |
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+ | No log | 4.0 | 4 | 0.7457 | 0.5 |
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+ | No log | 5.0 | 5 | 0.7456 | 0.5 |
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+ | No log | 6.0 | 6 | 0.7454 | 0.5 |
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+ | No log | 7.0 | 7 | 0.7452 | 0.5 |
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+ | No log | 8.0 | 8 | 0.7449 | 0.5 |
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+ | No log | 9.0 | 9 | 0.7446 | 0.5 |
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+ | 0.7277 | 10.0 | 10 | 0.7443 | 0.5 |
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+ | 0.7277 | 11.0 | 11 | 0.7439 | 0.5 |
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+ | 0.7277 | 12.0 | 12 | 0.7435 | 0.5 |
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+ | 0.7277 | 13.0 | 13 | 0.7430 | 0.5 |
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+ | 0.7277 | 14.0 | 14 | 0.7426 | 0.5 |
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+ | 0.7277 | 15.0 | 15 | 0.7420 | 0.5 |
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+ | 0.7277 | 16.0 | 16 | 0.7415 | 0.5 |
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+ | 0.7277 | 17.0 | 17 | 0.7409 | 0.5 |
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+ | 0.7277 | 18.0 | 18 | 0.7403 | 0.5 |
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+ | 0.7277 | 19.0 | 19 | 0.7397 | 0.5 |
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+ | 0.7081 | 20.0 | 20 | 0.7390 | 0.5 |
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+ | 0.7081 | 21.0 | 21 | 0.7382 | 0.5 |
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+ | 0.7081 | 22.0 | 22 | 0.7375 | 0.5 |
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+ | 0.7081 | 23.0 | 23 | 0.7367 | 0.5 |
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+ | 0.7081 | 24.0 | 24 | 0.7359 | 0.5 |
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+ | 0.7081 | 25.0 | 25 | 0.7351 | 0.5 |
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+ | 0.7081 | 26.0 | 26 | 0.7342 | 0.5 |
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+ | 0.7081 | 27.0 | 27 | 0.7334 | 0.5 |
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+ | 0.7081 | 28.0 | 28 | 0.7325 | 0.5 |
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+ | 0.7081 | 29.0 | 29 | 0.7316 | 0.5 |
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+ | 0.7107 | 30.0 | 30 | 0.7306 | 0.5 |
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+ | 0.7107 | 31.0 | 31 | 0.7297 | 0.5 |
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+ | 0.7107 | 32.0 | 32 | 0.7287 | 0.5 |
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+ | 0.7107 | 33.0 | 33 | 0.7277 | 0.5 |
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+ | 0.7107 | 34.0 | 34 | 0.7266 | 0.5 |
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+ | 0.7107 | 35.0 | 35 | 0.7256 | 0.5 |
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+ | 0.7107 | 36.0 | 36 | 0.7246 | 0.5 |
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+ | 0.7107 | 37.0 | 37 | 0.7235 | 0.5 |
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+ | 0.7107 | 38.0 | 38 | 0.7225 | 0.5 |
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+ | 0.7107 | 39.0 | 39 | 0.7214 | 0.5 |
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+ | 0.6761 | 40.0 | 40 | 0.7204 | 0.5 |
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+ | 0.6761 | 41.0 | 41 | 0.7193 | 0.5 |
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+ | 0.6761 | 42.0 | 42 | 0.7182 | 0.4688 |
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+ | 0.6761 | 43.0 | 43 | 0.7172 | 0.4688 |
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+ | 0.6761 | 44.0 | 44 | 0.7161 | 0.4688 |
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+ | 0.6761 | 45.0 | 45 | 0.7150 | 0.4688 |
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+ | 0.6761 | 46.0 | 46 | 0.7140 | 0.4688 |
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+ | 0.6761 | 47.0 | 47 | 0.7130 | 0.4688 |
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+ | 0.6761 | 48.0 | 48 | 0.7119 | 0.4688 |
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+ | 0.6761 | 49.0 | 49 | 0.7110 | 0.4688 |
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+ | 0.657 | 50.0 | 50 | 0.7100 | 0.4688 |
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+ | 0.657 | 51.0 | 51 | 0.7091 | 0.4375 |
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+ | 0.657 | 52.0 | 52 | 0.7083 | 0.4688 |
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+ | 0.657 | 53.0 | 53 | 0.7074 | 0.4688 |
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+ | 0.657 | 54.0 | 54 | 0.7067 | 0.4688 |
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+ | 0.657 | 55.0 | 55 | 0.7059 | 0.4688 |
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+ | 0.657 | 56.0 | 56 | 0.7054 | 0.4375 |
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+ | 0.657 | 57.0 | 57 | 0.7049 | 0.4688 |
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+ | 0.657 | 58.0 | 58 | 0.7045 | 0.4688 |
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+ | 0.657 | 59.0 | 59 | 0.7042 | 0.4688 |
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+ | 0.621 | 60.0 | 60 | 0.7041 | 0.4688 |
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+ | 0.621 | 61.0 | 61 | 0.7040 | 0.4688 |
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+ | 0.621 | 62.0 | 62 | 0.7041 | 0.4688 |
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+ | 0.621 | 63.0 | 63 | 0.7043 | 0.5 |
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+ | 0.621 | 64.0 | 64 | 0.7047 | 0.5 |
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+ | 0.621 | 65.0 | 65 | 0.7054 | 0.4688 |
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+ | 0.621 | 66.0 | 66 | 0.7063 | 0.4688 |
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+ | 0.621 | 67.0 | 67 | 0.7072 | 0.4688 |
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+ | 0.621 | 68.0 | 68 | 0.7082 | 0.4688 |
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+ | 0.621 | 69.0 | 69 | 0.7092 | 0.4688 |
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+ | 0.5793 | 70.0 | 70 | 0.7102 | 0.4688 |
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+ | 0.5793 | 71.0 | 71 | 0.7112 | 0.4688 |
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+ | 0.5793 | 72.0 | 72 | 0.7124 | 0.4688 |
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+ | 0.5793 | 73.0 | 73 | 0.7137 | 0.4688 |
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+ | 0.5793 | 74.0 | 74 | 0.7151 | 0.4688 |
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+ | 0.5793 | 75.0 | 75 | 0.7167 | 0.4688 |
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+ | 0.5793 | 76.0 | 76 | 0.7184 | 0.4688 |
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+ | 0.5793 | 77.0 | 77 | 0.7202 | 0.5 |
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+ | 0.5793 | 78.0 | 78 | 0.7220 | 0.5 |
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+ | 0.5793 | 79.0 | 79 | 0.7238 | 0.5 |
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+ | 0.524 | 80.0 | 80 | 0.7257 | 0.5 |
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+ | 0.524 | 81.0 | 81 | 0.7276 | 0.5 |
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+ | 0.524 | 82.0 | 82 | 0.7295 | 0.5 |
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+ | 0.524 | 83.0 | 83 | 0.7315 | 0.5 |
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+ | 0.524 | 84.0 | 84 | 0.7336 | 0.4688 |
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+ | 0.524 | 85.0 | 85 | 0.7358 | 0.4688 |
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+ | 0.524 | 86.0 | 86 | 0.7381 | 0.4688 |
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+ | 0.524 | 87.0 | 87 | 0.7406 | 0.4688 |
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+ | 0.524 | 88.0 | 88 | 0.7431 | 0.4688 |
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+ | 0.524 | 89.0 | 89 | 0.7458 | 0.4688 |
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+ | 0.4597 | 90.0 | 90 | 0.7488 | 0.4688 |
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+ | 0.4597 | 91.0 | 91 | 0.7520 | 0.4688 |
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+ | 0.4597 | 92.0 | 92 | 0.7549 | 0.4688 |
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+ | 0.4597 | 93.0 | 93 | 0.7574 | 0.4375 |
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+ | 0.4597 | 94.0 | 94 | 0.7599 | 0.4375 |
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+ | 0.4597 | 95.0 | 95 | 0.7627 | 0.4375 |
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+ | 0.4597 | 96.0 | 96 | 0.7659 | 0.4375 |
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+ | 0.4597 | 97.0 | 97 | 0.7694 | 0.4375 |
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+ | 0.4597 | 98.0 | 98 | 0.7730 | 0.4375 |
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+ | 0.4597 | 99.0 | 99 | 0.7765 | 0.4375 |
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+ | 0.3918 | 100.0 | 100 | 0.7799 | 0.4375 |
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+ | 0.3918 | 101.0 | 101 | 0.7834 | 0.4375 |
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+ | 0.3918 | 102.0 | 102 | 0.7867 | 0.4375 |
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+ | 0.3918 | 103.0 | 103 | 0.7898 | 0.4375 |
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+ | 0.3918 | 104.0 | 104 | 0.7931 | 0.4375 |
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+ | 0.3918 | 105.0 | 105 | 0.7963 | 0.4375 |
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+ | 0.3918 | 106.0 | 106 | 0.7996 | 0.4375 |
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+ | 0.3918 | 107.0 | 107 | 0.8029 | 0.4375 |
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+ | 0.3918 | 108.0 | 108 | 0.8060 | 0.4375 |
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+ | 0.3918 | 109.0 | 109 | 0.8090 | 0.4375 |
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+ | 0.3216 | 110.0 | 110 | 0.8121 | 0.4688 |
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+ | 0.3216 | 111.0 | 111 | 0.8155 | 0.4375 |
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+ | 0.3216 | 112.0 | 112 | 0.8191 | 0.4375 |
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+ | 0.3216 | 113.0 | 113 | 0.8227 | 0.4375 |
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+ | 0.3216 | 114.0 | 114 | 0.8260 | 0.4375 |
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+ | 0.3216 | 115.0 | 115 | 0.8293 | 0.4375 |
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+ | 0.3216 | 116.0 | 116 | 0.8326 | 0.4688 |
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+ | 0.3216 | 117.0 | 117 | 0.8356 | 0.4688 |
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+ | 0.3216 | 118.0 | 118 | 0.8387 | 0.4375 |
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+ | 0.3216 | 119.0 | 119 | 0.8420 | 0.4375 |
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+ | 0.267 | 120.0 | 120 | 0.8454 | 0.4375 |
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+ | 0.267 | 121.0 | 121 | 0.8488 | 0.4375 |
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+ | 0.267 | 122.0 | 122 | 0.8525 | 0.4375 |
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+ | 0.267 | 123.0 | 123 | 0.8563 | 0.4375 |
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+ | 0.267 | 124.0 | 124 | 0.8601 | 0.4375 |
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+ | 0.267 | 125.0 | 125 | 0.8639 | 0.4375 |
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+ | 0.267 | 126.0 | 126 | 0.8677 | 0.4375 |
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+ | 0.267 | 127.0 | 127 | 0.8716 | 0.4375 |
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+ | 0.267 | 128.0 | 128 | 0.8762 | 0.4375 |
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+ | 0.267 | 129.0 | 129 | 0.8807 | 0.4375 |
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+ | 0.2376 | 130.0 | 130 | 0.8853 | 0.4375 |
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+ | 0.2376 | 131.0 | 131 | 0.8898 | 0.4375 |
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+ | 0.2376 | 132.0 | 132 | 0.8943 | 0.4375 |
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+ | 0.2376 | 133.0 | 133 | 0.8988 | 0.4375 |
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+ | 0.2376 | 134.0 | 134 | 0.9029 | 0.4375 |
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+ | 0.2376 | 135.0 | 135 | 0.9061 | 0.4375 |
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+ | 0.2376 | 136.0 | 136 | 0.9092 | 0.4062 |
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+ | 0.2376 | 137.0 | 137 | 0.9113 | 0.4062 |
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+ | 0.2376 | 138.0 | 138 | 0.9130 | 0.4375 |
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+ | 0.2376 | 139.0 | 139 | 0.9146 | 0.4375 |
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+ | 0.2042 | 140.0 | 140 | 0.9163 | 0.4375 |
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+ | 0.2042 | 141.0 | 141 | 0.9178 | 0.4375 |
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+ | 0.2042 | 142.0 | 142 | 0.9193 | 0.4375 |
195
+ | 0.2042 | 143.0 | 143 | 0.9206 | 0.4375 |
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+ | 0.2042 | 144.0 | 144 | 0.9222 | 0.4375 |
197
+ | 0.2042 | 145.0 | 145 | 0.9268 | 0.4375 |
198
+ | 0.2042 | 146.0 | 146 | 0.9325 | 0.4375 |
199
+ | 0.2042 | 147.0 | 147 | 0.9385 | 0.4375 |
200
+ | 0.2042 | 148.0 | 148 | 0.9448 | 0.4375 |
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+ | 0.2042 | 149.0 | 149 | 0.9509 | 0.4375 |
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+ | 0.1738 | 150.0 | 150 | 0.9572 | 0.4688 |
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  ### Framework versions