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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:wnli-2020-06-29-11:34/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: roberta-base Tokenizing training data. (len: 635) Tokenizing eval data (len: 71) Loaded data and tokenized in 9.126316547393799s Training model across 4 GPUs ***** Running training ***** Num examples = 635 Batch size = 16 Max sequence length = 256 Num steps = 195 Num epochs = 5 Learning rate = 5e-05 Eval accuracy: 56.33802816901409% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:wnli-2020-06-29-11:34/. Eval accuracy: 54.929577464788736% Eval accuracy: 56.33802816901409% Eval accuracy: 38.028169014084504% Eval accuracy: 43.66197183098591% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f372d0dd970> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:wnli-2020-06-29-11:34/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:wnli-2020-06-29-11:34/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:wnli-2020-06-29-11:34/train_args.json. |