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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-ag_news-2020-07-03-06:26/log.txt. Loading [94mnlp[0m dataset [94mag_news[0m, split [94mtrain[0m. Loading [94mnlp[0m dataset [94mag_news[0m, split [94mtest[0m. Loaded dataset. Found: 4 labels: ([0, 1, 2, 3]) Loading transformers AutoModelForSequenceClassification: distilbert-base-uncased Tokenizing training data. (len: 120000) Tokenizing eval data (len: 7600) Loaded data and tokenized in 145.95597338676453s Training model across 4 GPUs ***** Running training ***** Num examples = 120000 Batch size = 32 Max sequence length = 128 Num steps = 18750 Num epochs = 5 Learning rate = 2e-05 Eval accuracy: 93.94736842105263% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-ag_news-2020-07-03-06:26/. Eval accuracy: 94.78947368421052% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-ag_news-2020-07-03-06:26/. Eval accuracy: 94.67105263157895% Eval accuracy: 94.72368421052632% Eval accuracy: 94.56578947368422% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f914302fc10> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-ag_news-2020-07-03-06:26/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-ag_news-2020-07-03-06:26/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/distilbert-base-uncased-ag_news-2020-07-03-06:26/train_args.json. |