distilbert-base-uncased-english-sentweet-targeted-insult
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8638
- Accuracy: 0.7917
- Precision: 0.7978
- Recall: 0.7985
- F1: 0.7917
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 81 | 0.4472 | 0.8160 | 0.8345 | 0.8275 | 0.8157 |
| No log | 2.0 | 162 | 0.4819 | 0.8160 | 0.8369 | 0.8282 | 0.8156 |
| No log | 3.0 | 243 | 0.4993 | 0.8090 | 0.8273 | 0.8205 | 0.8087 |
| No log | 4.0 | 324 | 0.5902 | 0.7674 | 0.7652 | 0.7662 | 0.7656 |
| No log | 5.0 | 405 | 0.8004 | 0.7604 | 0.7590 | 0.7612 | 0.7594 |
| No log | 6.0 | 486 | 0.8638 | 0.7917 | 0.7978 | 0.7985 | 0.7917 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu117
- Datasets 2.6.1
- Tokenizers 0.11.0