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Update 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.
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+ Loading nlp dataset glue, subset wnli, split train.
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+ Loading nlp dataset glue, subset wnli, split validation.
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+ Loaded dataset. Found: 2 labels: ([0, 1])
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+ Loading transformers AutoModelForSequenceClassification: bert-base-uncased
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+ Tokenizing training data. (len: 635)
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+ Tokenizing eval data (len: 71)
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+ Loaded data and tokenized in 7.102111577987671s
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+ Training model across 4 GPUs
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+ ***** Running training *****
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+ Num examples = 635
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+ Batch size = 64
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+ Max sequence length = 256
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+ Num steps = 45
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+ Num epochs = 5
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+ Learning rate = 5e-05
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+ Eval accuracy: 43.66197183098591%
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+ 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/.
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+ Eval accuracy: 56.33802816901409%
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+ 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/.
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+ Eval accuracy: 23.943661971830984%
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+ Eval accuracy: 49.29577464788733%
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+ Eval accuracy: 50.70422535211267%
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+ 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/.
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+ Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-glue:wnli-2020-06-29-11:27/README.md.
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+ 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.