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Kjøretid
{'train_runtime': 291.2967, 'train_samples_per_second': 51.494, 'train_steps_per_second': 0.189, 'train_loss': 0.6998663252050227, 'epoch': 4.94}
Time: 291.30
Samples/second: 51.49
GPU memory occupied: 6267 MB.
norbert2_sentiment_norec_en_gpu_500_rader_9
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: 0.6255
- Compute Metrics: :
- Accuracy: 0.694
- Balanced Accuracy: 0.5023
- F1 Score: 0.8185
- Recall: 0.9914
- Precision: 0.6970
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: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- 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 | 2 | 0.6208 | : | 0.684 | 0.5006 | 0.8101 | 0.9684 | 0.6963 |
| No log | 2.0 | 4 | 0.6262 | : | 0.692 | 0.4990 | 0.8175 | 0.9914 | 0.6956 |
| No log | 3.0 | 6 | 0.6338 | : | 0.672 | 0.4846 | 0.8034 | 0.9626 | 0.6893 |
| No log | 4.0 | 8 | 0.6240 | : | 0.698 | 0.5051 | 0.8213 | 0.9971 | 0.6982 |
| 0.6681 | 5.0 | 10 | 0.6255 | : | 0.694 | 0.5023 | 0.8185 | 0.9914 | 0.6970 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
