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log.txt

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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 nlp dataset glue, subset wnli, split train.
+Loading nlp dataset glue, subset wnli, split validation.
+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.