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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:rte-2020-06-29-13:42/log.txt. Loading [94mnlp[0m dataset [94mglue[0m, subset [94mrte[0m, split [94mtrain[0m. Loading [94mnlp[0m dataset [94mglue[0m, subset [94mrte[0m, split [94mvalidation[0m. Loaded dataset. Found: 2 labels: ([0, 1]) Loading transformers AutoModelForSequenceClassification: albert-base-v2 Tokenizing training data. (len: 2490) Tokenizing eval data (len: 277) Loaded data and tokenized in 11.145107507705688s Training model across 1 GPUs ***** Running training ***** Num examples = 2490 Batch size = 64 Max sequence length = 128 Num steps = 190 Num epochs = 5 Learning rate = 3e-05 Eval accuracy: 59.92779783393502% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:rte-2020-06-29-13:42/. Eval accuracy: 70.03610108303249% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:rte-2020-06-29-13:42/. Eval accuracy: 76.53429602888086% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:rte-2020-06-29-13:42/. Eval accuracy: 76.53429602888086% Eval accuracy: 77.6173285198556% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:rte-2020-06-29-13:42/. Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7fc7ec15ae80> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:rte-2020-06-29-13:42/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:rte-2020-06-29-13:42/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:rte-2020-06-29-13:42/train_args.json. |