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distilbert-base-uncased-finetuned-items-two
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: 0.8258
- Accuracy: 0.7212
- F1: 0.7198
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: 30
- eval_batch_size: 30
- 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 | F1 |
---|---|---|---|---|---|
1.8573 | 1.0 | 32 | 1.6788 | 0.4135 | 0.3052 |
1.6314 | 2.0 | 64 | 1.4137 | 0.5385 | 0.4754 |
1.3618 | 3.0 | 96 | 1.2564 | 0.5577 | 0.5178 |
1.1231 | 4.0 | 128 | 1.0664 | 0.6538 | 0.6454 |
0.9382 | 5.0 | 160 | 0.9553 | 0.6923 | 0.6864 |
0.7879 | 6.0 | 192 | 0.8792 | 0.6923 | 0.6891 |
0.6616 | 7.0 | 224 | 0.8642 | 0.7019 | 0.6978 |
0.5844 | 8.0 | 256 | 0.8376 | 0.7115 | 0.7092 |
0.5289 | 9.0 | 288 | 0.8349 | 0.7115 | 0.7074 |
0.4673 | 10.0 | 320 | 0.8258 | 0.7212 | 0.7198 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1