distilbert-base-uncased-finetuned-sentiment
This model is a fine-tuned version of distilbert-base-uncased on the tweet_eval dataset. It achieves the following results on the evaluation set:
- Loss: 0.6208
- Accuracy: 0.7285
- F1: 0.7289
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 |
|---|---|---|---|---|---|
| 0.6922 | 1.0 | 713 | 0.6267 | 0.7195 | 0.7208 |
| 0.5571 | 2.0 | 1426 | 0.6208 | 0.7285 | 0.7289 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
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Model tree for Dangurangu/distilbert-base-uncased-finetuned-sentiment
Base model
distilbert/distilbert-base-uncasedDataset used to train Dangurangu/distilbert-base-uncased-finetuned-sentiment
Evaluation results
- Accuracy on tweet_evalvalidation set self-reported0.729
- F1 on tweet_evalvalidation set self-reported0.729