| 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 | |