| Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/log.txt. |
| Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mtrain[0m. |
| Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mvalidation[0m. |
| Loaded dataset. Found: 2 labels: ([0, 1]) |
| Loading transformers AutoModelForSequenceClassification: albert-base-v2 |
| Tokenizing training data. (len: 635) |
| Tokenizing eval data (len: 71) |
| Loaded data and tokenized in 4.413618564605713s |
| Training model across 4 GPUs |
| ***** Running training ***** |
| Num examples = 635 |
| Batch size = 64 |
| Max sequence length = 256 |
| Num steps = 45 |
| Num epochs = 5 |
| Learning rate = 2e-05 |
| Eval accuracy: 59.154929577464785% |
| Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/. |
| Eval accuracy: 47.88732394366197% |
| Eval accuracy: 45.07042253521127% |
| Eval accuracy: 47.88732394366197% |
| Eval accuracy: 50.70422535211267% |
| Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f9b70a4ba60> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/. |
| Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/README.md. |
| Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/train_args.json. |
| Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/log.txt. |
| Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mtrain[0m. |
| Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mvalidation[0m. |
| Loaded dataset. Found: 2 labels: ([0, 1]) |
| Loading transformers AutoModelForSequenceClassification: albert-base-v2 |
| Tokenizing training data. (len: 635) |
| Tokenizing eval data (len: 71) |
| Loaded data and tokenized in 4.476848840713501s |
| Training model across 4 GPUs |
| ***** Running training ***** |
| Num examples = 635 |
| Batch size = 128 |
| Max sequence length = 256 |
| Num steps = 20 |
| Num epochs = 5 |
| Learning rate = 2e-05 |
|
|
