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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-imdb-2020-06-29-16:55/log.txt. Loading [94mnlp[0m dataset [94mimdb[0m, split [94mtrain[0m. Loading [94mnlp[0m dataset [94mimdb[0m, split [94mtest[0m. Loaded dataset. Found: 2 labels: ([0, 1]) Loading transformers AutoModelForSequenceClassification: albert-base-v2 Tokenizing training data. (len: 25000) Tokenizing eval data (len: 25000) Loaded data and tokenized in 121.69637775421143s Training model across 4 GPUs ***** Running training ***** Num examples = 25000 Batch size = 32 Max sequence length = 128 Num steps = 3905 Num epochs = 5 Learning rate = 2e-05 Eval accuracy: 86.512% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-imdb-2020-06-29-16:55/. Eval accuracy: 88.024% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-imdb-2020-06-29-16:55/. Eval accuracy: 89.19200000000001% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-imdb-2020-06-29-16:55/. Eval accuracy: 89.236% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-imdb-2020-06-29-16:55/. Eval accuracy: 88.956% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f23881f8400> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-imdb-2020-06-29-16:55/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-imdb-2020-06-29-16:55/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-imdb-2020-06-29-16:55/train_args.json. |