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Update log.txt
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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-imdb-2020-06-28-20:07/log.txt.
Loading nlp dataset imdb, split train.
Loading nlp dataset imdb, split test.
Loaded dataset. Found: 2 labels: ([0, 1])
Loading transformers AutoModelForSequenceClassification: xlnet-base-cased
Tokenizing training data. (len: 25000)
Tokenizing eval data (len: 25000)
Loaded data and tokenized in 76.48014068603516s
Training model across 4 GPUs
***** Running training *****
Num examples = 25000
Batch size = 32
Max sequence length = 512
Num steps = 3905
Num epochs = 5
Learning rate = 2e-05
Eval accuracy: 94.98400000000001%
Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-imdb-2020-06-28-20:07/.
Eval accuracy: 95.024%
Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-imdb-2020-06-28-20:07/.
Eval accuracy: 95.352%
Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-imdb-2020-06-28-20:07/.
Eval accuracy: 95.00800000000001%
Eval accuracy: 95.268%
Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7efe433eb050> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-imdb-2020-06-28-20:07/.
Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-imdb-2020-06-28-20:07/README.md.
Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-imdb-2020-06-28-20:07/train_args.json.