Update log.txt
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log.txt
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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-imdb-2020-06-30-17:56/log.txt.
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Loading [94mnlp[0m dataset [94mimdb[0m, split [94mtrain[0m.
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Loading [94mnlp[0m dataset [94mimdb[0m, split [94mtest[0m.
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Loaded dataset. Found: 2 labels: ([0, 1])
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Loading transformers AutoModelForSequenceClassification: roberta-base
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Tokenizing training data. (len: 25000)
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Tokenizing eval data (len: 25000)
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Loaded data and tokenized in 79.24297642707825s
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Training model across 4 GPUs
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***** Running training *****
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Num examples = 25000
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Batch size = 64
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Max sequence length = 128
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Num steps = 1950
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Num epochs = 5
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Learning rate = 3e-05
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Eval accuracy: 90.776%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-imdb-2020-06-30-17:56/.
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Eval accuracy: 91.35600000000001%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-imdb-2020-06-30-17:56/.
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Eval accuracy: 91.43599999999999%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-imdb-2020-06-30-17:56/.
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Eval accuracy: 91.172%
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Eval accuracy: 91.408%
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Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f1c2495c070> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-imdb-2020-06-30-17:56/.
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Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-imdb-2020-06-30-17:56/README.md.
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Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-imdb-2020-06-30-17:56/train_args.json.
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