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bert-base-cased-sentweet-targetedinsult
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0418
- Accuracy: 0.7917
- Precision: 0.7911
- Recall: 0.7922
- F1: 0.7913
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 | 0.4277 | 0.8299 | 0.8375 | 0.8346 | 0.8298 |
| No log | 2.0 | 162 | 0.5315 | 0.7326 | 0.7412 | 0.7253 | 0.7253 |
| No log | 3.0 | 243 | 0.6488 | 0.7396 | 0.7472 | 0.7327 | 0.7331 |
| No log | 4.0 | 324 | 0.8324 | 0.7431 | 0.7432 | 0.7399 | 0.7406 |
| No log | 5.0 | 405 | 0.9038 | 0.7917 | 0.7924 | 0.7935 | 0.7916 |
| No log | 6.0 | 486 | 1.0418 | 0.7917 | 0.7911 | 0.7922 | 0.7913 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu117
- Datasets 2.6.1
- Tokenizers 0.11.0
