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-glue:mrpc-2020-06-29-14:01/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: roberta-base
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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 15.40734577178955s
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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 = 16
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Max sequence length = 256
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Num steps = 1145
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Num epochs = 5
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Learning rate = 3e-05
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Eval accuracy: 88.23529411764706%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:mrpc-2020-06-29-14:01/.
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Eval accuracy: 89.2156862745098%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:mrpc-2020-06-29-14:01/.
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Eval accuracy: 91.17647058823529%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:mrpc-2020-06-29-14:01/.
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Eval accuracy: 88.48039215686273%
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Eval accuracy: 89.4607843137255%
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Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f4f0a826d00> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:mrpc-2020-06-29-14:01/.
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Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:mrpc-2020-06-29-14:01/README.md.
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Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:mrpc-2020-06-29-14:01/train_args.json.
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