Improving Black-box Robustness with In-Context Rewriting
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boss-sentiment-3000-bert-base-uncased
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- F1: 0.6539
- Acc: 0.8012
- Loss: 1.0668
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 |
|---|---|---|---|---|---|
| No log | 1.0 | 188 | 0.5948 | 0.8309 | 0.5611 |
| No log | 2.0 | 376 | 0.6500 | 0.7876 | 0.5856 |
| 0.6906 | 3.0 | 564 | 0.7083 | 0.8627 | 0.3858 |
| 0.6906 | 4.0 | 752 | 0.6588 | 0.7697 | 0.8185 |
| 0.6906 | 5.0 | 940 | 0.6687 | 0.8142 | 0.8388 |
| 0.1956 | 6.0 | 1128 | 0.6539 | 0.8012 | 1.0668 |
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/boss-sentiment-3000-bert-base-uncased
Base model
google-bert/bert-base-uncased