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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:mrpc-2020-06-29-12:04/log.txt. Loading [94mnlp[0m dataset [94mglue[0m, subset [94mmrpc[0m, split [94mtrain[0m. Loading [94mnlp[0m dataset [94mglue[0m, subset [94mmrpc[0m, split [94mvalidation[0m. Loaded dataset. Found: 2 labels: ([0, 1]) Loading transformers AutoModelForSequenceClassification: bert-base-uncased Tokenizing training data. (len: 3668) Tokenizing eval data (len: 408) Loaded data and tokenized in 12.476295709609985s Training model across 4 GPUs ***** Running training ***** Num examples = 3668 Batch size = 16 Max sequence length = 256 Num steps = 1145 Num epochs = 5 Learning rate = 2e-05 Eval accuracy: 84.31372549019608% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:mrpc-2020-06-29-12:04/. Eval accuracy: 87.74509803921569% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:mrpc-2020-06-29-12:04/. Eval accuracy: 86.02941176470588% Eval accuracy: 85.7843137254902% Eval accuracy: 85.04901960784314% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f3a1d1a5d00> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:mrpc-2020-06-29-12:04/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:mrpc-2020-06-29-12:04/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:mrpc-2020-06-29-12:04/train_args.json. |