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distilbert-base-uncased-finetuned-Multi_classification
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5588
- Accuracy: 0.7266
- Macro Averaged Precision: 0.6830
- Micro Averaged Precision: 0.7266
- Macro Averaged Recall: 0.5652
- Micro Averaged Recall: 0.7266
- Macro Averaged F1: 0.5513
- Micro Averaged F1: 0.7266
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro Averaged Precision | Micro Averaged Precision | Macro Averaged Recall | Micro Averaged Recall | Macro Averaged F1 | Micro Averaged F1 |
---|---|---|---|---|---|---|---|---|---|---|
0.5811 | 1.0 | 635 | 0.5745 | 0.7055 | 0.3527 | 0.7055 | 0.5 | 0.7055 | 0.4137 | 0.7055 |
0.5467 | 2.0 | 1270 | 0.5588 | 0.7266 | 0.6830 | 0.7266 | 0.5652 | 0.7266 | 0.5513 | 0.7266 |
0.4724 | 3.0 | 1905 | 0.6347 | 0.7109 | 0.6328 | 0.7109 | 0.5873 | 0.7109 | 0.5906 | 0.7109 |
0.2379 | 4.0 | 2540 | 0.9110 | 0.7078 | 0.6281 | 0.7078 | 0.5874 | 0.7078 | 0.5910 | 0.7078 |
0.1511 | 5.0 | 3175 | 1.2270 | 0.6953 | 0.6168 | 0.6953 | 0.5963 | 0.6953 | 0.6011 | 0.6953 |
0.1074 | 6.0 | 3810 | 1.6106 | 0.7188 | 0.6470 | 0.7188 | 0.5859 | 0.7188 | 0.5875 | 0.7188 |
0.0935 | 7.0 | 4445 | 1.8533 | 0.7070 | 0.6266 | 0.7070 | 0.5861 | 0.7070 | 0.5895 | 0.7070 |
0.037 | 8.0 | 5080 | 2.0315 | 0.6875 | 0.6082 | 0.6875 | 0.5923 | 0.6875 | 0.5964 | 0.6875 |
0.0294 | 9.0 | 5715 | 2.0726 | 0.7078 | 0.6295 | 0.7078 | 0.5928 | 0.7078 | 0.5975 | 0.7078 |
0.0238 | 10.0 | 6350 | 2.1236 | 0.7086 | 0.6303 | 0.7086 | 0.5918 | 0.7086 | 0.5963 | 0.7086 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 1.18.4
- Tokenizers 0.12.1