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bert-base-cased-sentweet-profane
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: 0.4166
- Accuracy: 0.8194
- Precision: 0.8273
- Recall: 0.8249
- F1: 0.8194
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 | 0.4166 | 0.8194 | 0.8273 | 0.8249 | 0.8194 |
| No log | 2.0 | 162 | 0.4605 | 0.8021 | 0.8023 | 0.8038 | 0.8019 |
| No log | 3.0 | 243 | 0.4922 | 0.8021 | 0.8022 | 0.7994 | 0.8003 |
| No log | 4.0 | 324 | 0.5997 | 0.7882 | 0.7871 | 0.7874 | 0.7873 |
| No log | 5.0 | 405 | 0.8504 | 0.8056 | 0.8084 | 0.8090 | 0.8055 |
| No log | 6.0 | 486 | 0.9631 | 0.7951 | 0.7947 | 0.7929 | 0.7936 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.1+cu117
- Datasets 2.6.1
- Tokenizers 0.11.0
