bert-base-uncased-ag-news-finetuned-dwnews-categories
This model is a fine-tuned version of nateraw/bert-base-uncased-ag-news on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8659
- Accuracy: 0.74
- Precision Weighted: 0.7435
- Precision Macro: 0.7557
- Recall Weighted: 0.74
- Recall Macro: 0.7304
- F1 Weighted: 0.7294
- F1 Macro: 0.7250
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Weighted | Precision Macro | Recall Weighted | Recall Macro | F1 Weighted | F1 Macro |
---|---|---|---|---|---|---|---|---|---|---|
1.8428 | 0.25 | 50 | 1.5099 | 0.53 | 0.5008 | 0.4655 | 0.53 | 0.3544 | 0.4363 | 0.3246 |
1.3134 | 0.5 | 100 | 1.2200 | 0.605 | 0.4765 | 0.4247 | 0.605 | 0.4681 | 0.5207 | 0.4261 |
1.1562 | 0.75 | 150 | 1.0473 | 0.68 | 0.7007 | 0.6603 | 0.68 | 0.5534 | 0.6483 | 0.5507 |
1.0008 | 1.0 | 200 | 0.9491 | 0.67 | 0.6281 | 0.5935 | 0.67 | 0.5881 | 0.6390 | 0.5778 |
0.8173 | 1.25 | 250 | 0.9218 | 0.7 | 0.7028 | 0.6599 | 0.7 | 0.6694 | 0.6874 | 0.6417 |
0.8385 | 1.5 | 300 | 0.8900 | 0.715 | 0.7250 | 0.7131 | 0.715 | 0.6600 | 0.7059 | 0.6637 |
0.6988 | 1.75 | 350 | 0.9198 | 0.7 | 0.6941 | 0.6875 | 0.7 | 0.6825 | 0.6866 | 0.6704 |
0.6851 | 2.0 | 400 | 0.8607 | 0.72 | 0.7248 | 0.7310 | 0.72 | 0.6978 | 0.7067 | 0.6965 |
0.548 | 2.25 | 450 | 0.8659 | 0.74 | 0.7435 | 0.7557 | 0.74 | 0.7304 | 0.7294 | 0.7250 |
0.4898 | 2.5 | 500 | 0.9184 | 0.73 | 0.7379 | 0.7079 | 0.73 | 0.7599 | 0.7229 | 0.7203 |
0.5683 | 2.75 | 550 | 0.9207 | 0.72 | 0.7188 | 0.7089 | 0.72 | 0.7429 | 0.7150 | 0.7195 |
0.4971 | 3.0 | 600 | 0.9256 | 0.72 | 0.7257 | 0.7104 | 0.72 | 0.7384 | 0.7141 | 0.7126 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
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