Improving Black-box Robustness with In-Context Rewriting
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ag-news-twitter-38400-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the ag_news dataset. It achieves the following results on the evaluation set:
- F1: 0.9306
- Acc: 0.9308
- Loss: 0.4724
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | F1 | Acc | Validation Loss |
|---|---|---|---|---|---|
| 0.2511 | 1.0 | 2400 | 0.9219 | 0.9222 | 0.2514 |
| 0.2172 | 2.0 | 4800 | 0.9259 | 0.9261 | 0.2275 |
| 0.1631 | 3.0 | 7200 | 0.9375 | 0.9374 | 0.2335 |
| 0.1118 | 4.0 | 9600 | 0.9276 | 0.9275 | 0.3648 |
| 0.0661 | 5.0 | 12000 | 0.9280 | 0.9280 | 0.3757 |
| 0.0548 | 6.0 | 14400 | 0.9306 | 0.9308 | 0.4724 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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Model tree for Kyle1668/ag-news-38400-bert-base-uncased
Base model
google-bert/bert-base-uncased