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Kjøretid:
{'train_runtime': 432.2459, 'train_samples_per_second': 5.784, 'train_steps_per_second': 0.012, 'train_loss': 0.6640925884246827, 'epoch': 5.0}
Time: 432.25
Samples/second: 5.78
GPU memory occupied: 11314 MB.
norbert2_sentiment_norec_to_gpu_500_rader_8
This model is a fine-tuned version of bert-large-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6252
- Compute Metrics: :
- Accuracy: 0.692
- Balanced Accuracy: 0.4971
- F1 Score: 0.8180
- Recall: 0.9943
- Precision: 0.6948
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: 64
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 512
- 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 | Compute Metrics | Accuracy | Balanced Accuracy | F1 Score | Recall | Precision |
|---|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 1 | 0.6370 | : | 0.696 | 0.5 | 0.8208 | 1.0 | 0.696 |
| No log | 2.0 | 2 | 0.6319 | : | 0.684 | 0.4932 | 0.8119 | 0.9799 | 0.6931 |
| No log | 3.0 | 3 | 0.6415 | : | 0.692 | 0.4971 | 0.8180 | 0.9943 | 0.6948 |
| No log | 4.0 | 4 | 0.6299 | : | 0.692 | 0.4971 | 0.8180 | 0.9943 | 0.6948 |
| No log | 5.0 | 5 | 0.6252 | : | 0.692 | 0.4971 | 0.8180 | 0.9943 | 0.6948 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
