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norbert2_sentiment_norec_en_gpu_3000_rader_2_test
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: 0.6243
- Compute Metrics: :
- Accuracy: 0.6887
- Balanced Accuracy: 0.5020
- F1 Score: 0.8149
- Recall: 0.9932
- Precision: 0.6909
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 |
|---|---|---|---|---|---|---|---|---|---|
| 0.6753 | 0.94 | 11 | 0.6527 | : | 0.669 | 0.5064 | 0.7957 | 0.9343 | 0.6929 |
| 0.7261 | 1.94 | 22 | 0.6292 | : | 0.6813 | 0.5032 | 0.8080 | 0.9720 | 0.6914 |
| 0.7124 | 2.94 | 33 | 0.6263 | : | 0.688 | 0.5012 | 0.8145 | 0.9928 | 0.6905 |
| 0.7036 | 3.94 | 44 | 0.6271 | : | 0.686 | 0.5015 | 0.8126 | 0.9870 | 0.6907 |
| 0.7035 | 4.94 | 55 | 0.6243 | : | 0.6887 | 0.5020 | 0.8149 | 0.9932 | 0.6909 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu117
- Datasets 2.9.0
- Tokenizers 0.13.2
