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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/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: distilbert-base-uncased Tokenizing training data. (len: 3668) Tokenizing eval data (len: 408) Loaded data and tokenized in 10.926565647125244s Training model across 4 GPUs ***** Running training ***** Num examples = 3668 Batch size = 32 Max sequence length = 256 Num steps = 570 Num epochs = 5 Learning rate = 2e-05 Eval accuracy: 80.88235294117648% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/. Eval accuracy: 85.7843137254902% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/. Eval accuracy: 85.04901960784314% Eval accuracy: 85.5392156862745% Eval accuracy: 85.29411764705883% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f81f8485160> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/train_args.json. |