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BiBert-Subjectivity
This model is a fine-tuned version of nlptown/bert-base-multilingual-uncased-sentiment on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1481
- Accuracy: 0.9583
- F1: 0.9581
- Mae: 0.0417
- Accuracy 2: 0.9583
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 | Accuracy | F1 | Mae | Accuracy 2 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 112 | 0.1333 | 0.95 | 0.9508 | 0.05 | 0.95 |
| No log | 2.0 | 224 | 0.1517 | 0.953 | 0.9531 | 0.047 | 0.953 |
| No log | 3.0 | 336 | 0.2219 | 0.951 | 0.9505 | 0.049 | 0.951 |
| No log | 4.0 | 448 | 0.2327 | 0.947 | 0.9479 | 0.053 | 0.947 |
| 0.0865 | 5.0 | 560 | 0.2557 | 0.953 | 0.9528 | 0.047 | 0.953 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
