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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-rotten_tomatoes-2020-06-28-14:54/log.txt. Loading [94mnlp[0m dataset [94mrotten_tomatoes[0m, split [94mtrain[0m. Loading [94mnlp[0m dataset [94mrotten_tomatoes[0m, split [94mvalidation[0m. Loaded dataset. Found: 2 labels: ([0, 1]) Loading transformers AutoModelForSequenceClassification: albert-base-v2 Tokenizing training data. (len: 8530) Tokenizing eval data (len: 1066) Loaded data and tokenized in 16.212775945663452s Training model across 4 GPUs ***** Running training ***** Num examples = 8530 Batch size = 64 Max sequence length = 128 Num steps = 665 Num epochs = 5 Learning rate = 2e-05 Eval accuracy: 85.92870544090057% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-rotten_tomatoes-2020-06-28-14:54/. Eval accuracy: 88.08630393996248% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-rotten_tomatoes-2020-06-28-14:54/. Eval accuracy: 86.67917448405254% Eval accuracy: 86.39774859287056% Eval accuracy: 86.21013133208255% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f19d4bf0880> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-rotten_tomatoes-2020-06-28-14:54/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-rotten_tomatoes-2020-06-28-14:54/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-rotten_tomatoes-2020-06-28-14:54/train_args.json. |