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norbert2_sentiment_norec_to_gpu_3000_rader_2
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: 0.6214
- Compute Metrics: :
- Accuracy: 0.69
- Balanced Accuracy: 0.5
- F1 Score: 0.8166
- Recall: 1.0
- Precision: 0.69
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 | 0.85 | 5 | 0.6279 | : | 0.6827 | 0.4994 | 0.8102 | 0.9816 | 0.6897 |
| 0.719 | 1.85 | 10 | 0.6217 | : | 0.6897 | 0.4998 | 0.8163 | 0.9995 | 0.6899 |
| 0.719 | 2.85 | 15 | 0.6225 | : | 0.69 | 0.5 | 0.8166 | 1.0 | 0.69 |
| 0.7646 | 3.85 | 20 | 0.6186 | : | 0.69 | 0.5 | 0.8166 | 1.0 | 0.69 |
| 0.7646 | 4.85 | 25 | 0.6214 | : | 0.69 | 0.5 | 0.8166 | 1.0 | 0.69 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
