albert-base-v2-finetuned-emotion
This model is a fine-tuned version of albert/albert-base-v2 on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.2451
- Accuracy: 0.912
- F1: 0.9118
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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.9011 | 1.0 | 250 | 0.4077 | 0.877 | 0.8776 |
| 0.2633 | 2.0 | 500 | 0.2451 | 0.912 | 0.9118 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for bandi2716/albert-base-v2-finetuned-emotion
Base model
albert/albert-base-v2Dataset used to train bandi2716/albert-base-v2-finetuned-emotion
Evaluation results
- Accuracy on emotionvalidation set self-reported0.912
- F1 on emotionvalidation set self-reported0.912