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:wnli-2020-06-29-11:34/log.txt.
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Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mtrain[0m.
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Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[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: 635)
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Tokenizing eval data (len: 71)
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Loaded data and tokenized in 9.126316547393799s
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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 = 16
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Max sequence length = 256
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Num steps = 195
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Num epochs = 5
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Learning rate = 5e-05
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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/roberta-base-glue:wnli-2020-06-29-11:34/.
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Eval accuracy: 54.929577464788736%
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Eval accuracy: 56.33802816901409%
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Eval accuracy: 38.028169014084504%
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Eval accuracy: 43.66197183098591%
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Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f372d0dd970> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:wnli-2020-06-29-11:34/.
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Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:wnli-2020-06-29-11:34/README.md.
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Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/roberta-base-glue:wnli-2020-06-29-11:34/train_args.json.
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