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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
#SBATCH --nodes=1 #SBATCH --ntasks-per-node=1 #SBATCH --gres=gpu:A100m40:1
norbert2_sentiment_norec_en_gpu_500_rader_max_1
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: 0.6269
- Compute Metrics: :
- Accuracy: 0.682
- Balanced Accuracy: 0.5048
- F1 Score: 0.8073
- Recall: 0.9569
- Precision: 0.6981
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 | 0.6311 | : | 0.688 | 0.4943 | 0.8152 | 0.9885 | 0.6935 |
| No log | 2.0 | 4 | 0.6316 | : | 0.674 | 0.5268 | 0.7939 | 0.9023 | 0.7088 |
| No log | 3.0 | 6 | 0.6199 | : | 0.686 | 0.5002 | 0.8120 | 0.9741 | 0.6961 |
| No log | 4.0 | 8 | 0.6475 | : | 0.652 | 0.5277 | 0.7717 | 0.8448 | 0.7101 |
| 0.6559 | 5.0 | 10 | 0.6269 | : | 0.682 | 0.5048 | 0.8073 | 0.9569 | 0.6981 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
