sentiment_bert
This model is a fine-tuned version of google-bert/bert-base-multilingual-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6797
- Accuracy: 0.7147
- F1: 0.6617
- Precision: 0.6425
- Recall: 0.7137
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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
---|---|---|---|---|---|---|---|
0.6964 | 1.0 | 94 | 0.8219 | 0.6114 | 0.5957 | 0.6032 | 0.6955 |
0.6425 | 2.0 | 188 | 0.6180 | 0.7423 | 0.6769 | 0.6575 | 0.7106 |
0.5451 | 3.0 | 282 | 0.7182 | 0.6865 | 0.6484 | 0.6330 | 0.7224 |
0.4736 | 4.0 | 376 | 0.6797 | 0.7147 | 0.6617 | 0.6425 | 0.7137 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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