bert-base-uncased-finetuned-emotion
This model is a fine-tuned version of bert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:
- Loss: 0.1651
- Accuracy: {'accuracy': 0.9355}
- F1: {'f1': 0.935388774713548}
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 0.2519 | 1.0 | 1000 | 0.1878 | {'accuracy': 0.9325} | {'f1': 0.9323540471733189} |
| 0.1434 | 2.0 | 2000 | 0.1799 | {'accuracy': 0.9335} | {'f1': 0.9341179573678701} |
| 0.0907 | 3.0 | 3000 | 0.1651 | {'accuracy': 0.9355} | {'f1': 0.935388774713548} |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
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Model tree for nikitakapitan/bert-base-uncased-finetuned-emotion
Base model
google-bert/bert-base-uncasedDataset used to train nikitakapitan/bert-base-uncased-finetuned-emotion
Evaluation results
- Accuracy on emotionvalidation set self-reported[object Object]
- F1 on emotionvalidation set self-reported[object Object]