Improving Black-box Robustness with In-Context Rewriting
Collection
24 items
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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:
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The following hyperparameters were used during training:
Training Loss | Epoch | Step | F1 | Acc | Validation Loss |
---|---|---|---|---|---|
0.2328 | 1.0 | 4800 | 0.9289 | 0.9289 | 0.2082 |
0.2061 | 2.0 | 9600 | 0.9366 | 0.9367 | 0.2154 |
0.1488 | 3.0 | 14400 | 0.9401 | 0.9401 | 0.2181 |
0.114 | 4.0 | 19200 | 0.9280 | 0.9275 | 0.3199 |
0.0818 | 5.0 | 24000 | 0.9399 | 0.94 | 0.2953 |
0.051 | 6.0 | 28800 | 0.9402 | 0.9403 | 0.3828 |
0.0413 | 7.0 | 33600 | 0.9404 | 0.9403 | 0.4327 |
0.0342 | 8.0 | 38400 | 0.9395 | 0.9395 | 0.4291 |
0.0192 | 9.0 | 43200 | 0.9422 | 0.9422 | 0.4170 |
0.0204 | 10.0 | 48000 | 0.9374 | 0.9374 | 0.4761 |
0.0125 | 11.0 | 52800 | 0.9358 | 0.9359 | 0.5126 |
0.0124 | 12.0 | 57600 | 0.9415 | 0.9416 | 0.5192 |
Base model
google-bert/bert-base-uncased