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Bert-Sentiment-Fa
This model is a fine-tuned version of HooshvareLab/bert-fa-base-uncased on the Neshan Sentiment dataset. It achieves the following results on the evaluation set:
- Loss: 0.4558
- Accuracy: 0.8549
- F1: 0.7877
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 143 | 0.6143 | 0.8078 | 0.6887 |
| No log | 2.0 | 286 | 0.4310 | 0.8392 | 0.7556 |
| No log | 3.0 | 429 | 0.4002 | 0.8627 | 0.8037 |
| 0.5214 | 4.0 | 572 | 0.3933 | 0.8588 | 0.7984 |
| 0.5214 | 5.0 | 715 | 0.4046 | 0.8588 | 0.7945 |
| 0.5214 | 6.0 | 858 | 0.4169 | 0.8588 | 0.7990 |
| 0.2054 | 7.0 | 1001 | 0.4345 | 0.8549 | 0.7848 |
| 0.2054 | 8.0 | 1144 | 0.4277 | 0.8627 | 0.8001 |
| 0.2054 | 9.0 | 1287 | 0.4509 | 0.8588 | 0.7918 |
| 0.2054 | 10.0 | 1430 | 0.4558 | 0.8549 | 0.7877 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
