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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:wnli-2020-06-29-11:27/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: bert-base-uncased Tokenizing training data. (len: 635) Tokenizing eval data (len: 71) Loaded data and tokenized in 7.102111577987671s 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 = 5e-05 Eval accuracy: 43.66197183098591% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:wnli-2020-06-29-11:27/. Eval accuracy: 56.33802816901409% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:wnli-2020-06-29-11:27/. Eval accuracy: 23.943661971830984% Eval accuracy: 49.29577464788733% Eval accuracy: 50.70422535211267% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7fbf6aea2dc0> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:wnli-2020-06-29-11:27/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:wnli-2020-06-29-11:27/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:wnli-2020-06-29-11:27/train_args.json. |