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/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/log.txt.
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Loading [94mnlp[0m dataset [94mglue[0m, subset [94mmrpc[0m, split [94mtrain[0m.
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Loading [94mnlp[0m dataset [94mglue[0m, subset [94mmrpc[0m, split [94mvalidation[0m.
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Loaded dataset. Found: 2 labels: ([0, 1])
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Loading transformers AutoModelForSequenceClassification: distilbert-base-uncased
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Tokenizing training data. (len: 3668)
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Tokenizing eval data (len: 408)
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Loaded data and tokenized in 10.926565647125244s
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Training model across 4 GPUs
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***** Running training *****
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Num examples = 3668
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Batch size = 32
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Max sequence length = 256
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Num steps = 570
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Num epochs = 5
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Learning rate = 2e-05
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Eval accuracy: 80.88235294117648%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/.
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Eval accuracy: 85.7843137254902%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/.
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Eval accuracy: 85.04901960784314%
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Eval accuracy: 85.5392156862745%
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Eval accuracy: 85.29411764705883%
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Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f81f8485160> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/.
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Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/README.md.
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Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-glue:mrpc-2020-06-29-13:11/train_args.json.
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