bert-large-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr
This model is a fine-tuned version of bert-large-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1591
- Accuracy: 0.939
- F1: 0.9391
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: 5e-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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.5175 | 1.0 | 250 | 0.1803 | 0.9285 | 0.9295 |
| 0.1551 | 2.0 | 500 | 0.1425 | 0.932 | 0.9321 |
| 0.1112 | 3.0 | 750 | 0.1495 | 0.936 | 0.9366 |
| 0.0846 | 4.0 | 1000 | 0.1359 | 0.946 | 0.9457 |
| 0.0602 | 5.0 | 1250 | 0.1591 | 0.939 | 0.9391 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for Ioanaaaaaaa/bert-large-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr
Base model
google-bert/bert-large-uncasedDataset used to train Ioanaaaaaaa/bert-large-uncased-with-preprocess-finetuned-emotion-5-epochs-5e-05-lr
Evaluation results
- Accuracy on emotionvalidation set self-reported0.939
- F1 on emotionvalidation set self-reported0.939