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clincolnoz
/
bert-base-uncased-edos

Text Classification
Transformers
PyTorch
English
bert
Generated from Trainer
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2
clincolnoz commited on
Commit
0347211
·
1 Parent(s): 4fb0ffe

correct weights

Browse files
Files changed (8) hide show
  1. README.md +42 -41
  2. all_results.json +15 -15
  3. eval_results.json +11 -11
  4. predict_results_None.txt +0 -0
  5. pytorch_model.bin +1 -1
  6. train_results.json +4 -4
  7. trainer_state.json +49 -49
  8. training_args.bin +2 -2
README.md CHANGED
@@ -6,32 +6,32 @@ metrics:
6
  - f1
7
  - accuracy
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  model-index:
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- - name: final-lr2e-5-bs16-fullprecision
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  results: []
11
  ---
12
 
13
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
  should probably proofread and complete it, then remove this comment. -->
15
 
16
- # final-lr2e-5-bs16-fullprecision
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: 0.4633
21
- - F1 Macro: 0.8276
22
- - F1 Weighted: 0.8754
23
- - F1: 0.7348
24
- - Accuracy: 0.8775
25
- - Confusion Matrix: [[2831 199]
26
- [ 291 679]]
27
- - Confusion Matrix Norm: [[0.93432343 0.06567657]
28
- [0.3 0.7 ]]
29
- - Classification Report: precision recall f1-score support
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- 0 0.906791 0.934323 0.920351 3030.0000
31
- 1 0.773349 0.700000 0.734848 970.0000
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- accuracy 0.877500 0.877500 0.877500 0.8775
33
- macro avg 0.840070 0.817162 0.827600 4000.0000
34
- weighted avg 0.874431 0.877500 0.875367 4000.0000
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  ## Model description
37
 
@@ -57,35 +57,36 @@ The following hyperparameters were used during training:
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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: 3.0
 
60
 
61
  ### Training results
62
 
63
  | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report |
64
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
65
- | 0.3362 | 1.0 | 1000 | 0.3034 | 0.8182 | 0.8693 | 0.7191 | 0.8722 | [[2835 195]
66
- [ 316 654]] | [[0.93564356 0.06435644]
67
- [0.3257732 0.6742268 ]] | precision recall f1-score support
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- 0 0.899714 0.935644 0.917327 3030.00000
69
- 1 0.770318 0.674227 0.719076 970.00000
70
- accuracy 0.872250 0.872250 0.872250 0.87225
71
- macro avg 0.835016 0.804935 0.818202 4000.00000
72
- weighted avg 0.868336 0.872250 0.869251 4000.00000 |
73
- | 0.2352 | 2.0 | 2000 | 0.3730 | 0.8270 | 0.8730 | 0.7374 | 0.8732 | [[2781 249]
74
- [ 258 712]] | [[0.91782178 0.08217822]
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- [0.26597938 0.73402062]] | precision recall f1-score support
76
- 0 0.915104 0.917822 0.916461 3030.00000
77
- 1 0.740895 0.734021 0.737442 970.00000
78
- accuracy 0.873250 0.873250 0.873250 0.87325
79
- macro avg 0.827999 0.825921 0.826951 4000.00000
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- weighted avg 0.872858 0.873250 0.873049 4000.00000 |
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- | 0.1566 | 3.0 | 3000 | 0.4633 | 0.8276 | 0.8754 | 0.7348 | 0.8775 | [[2831 199]
82
- [ 291 679]] | [[0.93432343 0.06567657]
83
- [0.3 0.7 ]] | precision recall f1-score support
84
- 0 0.906791 0.934323 0.920351 3030.0000
85
- 1 0.773349 0.700000 0.734848 970.0000
86
- accuracy 0.877500 0.877500 0.877500 0.8775
87
- macro avg 0.840070 0.817162 0.827600 4000.0000
88
- weighted avg 0.874431 0.877500 0.875367 4000.0000 |
89
 
90
 
91
  ### Framework versions
 
6
  - f1
7
  - accuracy
8
  model-index:
9
+ - name: final-lr2e-5-bs16-fp16-2
10
  results: []
11
  ---
12
 
13
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
  should probably proofread and complete it, then remove this comment. -->
15
 
16
+ # final-lr2e-5-bs16-fp16-2
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: 0.4823
21
+ - F1 Macro: 0.8301
22
+ - F1 Weighted: 0.8772
23
+ - F1: 0.7388
24
+ - Accuracy: 0.8792
25
+ - Confusion Matrix: [[2834 196]
26
+ [ 287 683]]
27
+ - Confusion Matrix Norm: [[0.93531353 0.06468647]
28
+ [0.29587629 0.70412371]]
29
+ - Classification Report: precision recall f1-score support
30
+ 0 0.908042 0.935314 0.921476 3030.00000
31
+ 1 0.777019 0.704124 0.738778 970.00000
32
+ accuracy 0.879250 0.879250 0.879250 0.87925
33
+ macro avg 0.842531 0.819719 0.830127 4000.00000
34
+ weighted avg 0.876269 0.879250 0.877172 4000.00000
35
 
36
  ## Model description
37
 
 
57
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
58
  - lr_scheduler_type: linear
59
  - num_epochs: 3.0
60
+ - mixed_precision_training: Native AMP
61
 
62
  ### Training results
63
 
64
  | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | F1 | Accuracy | Confusion Matrix | Confusion Matrix Norm | Classification Report |
65
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:------:|:--------:|:--------------------------:|:--------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
66
+ | 0.3333 | 1.0 | 1000 | 0.3064 | 0.8165 | 0.8672 | 0.7181 | 0.8692 | [[2811 219]
67
+ [ 304 666]] | [[0.92772277 0.07227723]
68
+ [0.31340206 0.68659794]] | precision recall f1-score support
69
+ 0 0.902408 0.927723 0.914890 3030.00000
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+ 1 0.752542 0.686598 0.718059 970.00000
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+ accuracy 0.869250 0.869250 0.869250 0.86925
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+ macro avg 0.827475 0.807160 0.816475 4000.00000
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+ weighted avg 0.866065 0.869250 0.867159 4000.00000 |
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+ | 0.2271 | 2.0 | 2000 | 0.3905 | 0.8238 | 0.8708 | 0.7326 | 0.871 | [[2777 253]
75
+ [ 263 707]] | [[0.91650165 0.08349835]
76
+ [0.27113402 0.72886598]] | precision recall f1-score support
77
+ 0 0.913487 0.916502 0.914992 3030.000
78
+ 1 0.736458 0.728866 0.732642 970.000
79
+ accuracy 0.871000 0.871000 0.871000 0.871
80
+ macro avg 0.824973 0.822684 0.823817 4000.000
81
+ weighted avg 0.870557 0.871000 0.870772 4000.000 |
82
+ | 0.1435 | 3.0 | 3000 | 0.4823 | 0.8301 | 0.8772 | 0.7388 | 0.8792 | [[2834 196]
83
+ [ 287 683]] | [[0.93531353 0.06468647]
84
+ [0.29587629 0.70412371]] | precision recall f1-score support
85
+ 0 0.908042 0.935314 0.921476 3030.00000
86
+ 1 0.777019 0.704124 0.738778 970.00000
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+ accuracy 0.879250 0.879250 0.879250 0.87925
88
+ macro avg 0.842531 0.819719 0.830127 4000.00000
89
+ weighted avg 0.876269 0.879250 0.877172 4000.00000 |
90
 
91
 
92
  ### Framework versions
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