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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-rotten_tomatoes-2020-06-28-16:10/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: xlnet-base-cased Tokenizing training data. (len: 8530) Tokenizing eval data (len: 1066) Loaded data and tokenized in 25.242671012878418s Training model across 1 GPUs ***** Running training ***** Num examples = 8530 Batch size = 8 Max sequence length = 128 Num steps = 5330 Num epochs = 5 Learning rate = 2e-05 Eval accuracy: 89.58724202626641% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-rotten_tomatoes-2020-06-28-16:10/. Eval accuracy: 89.58724202626641% Eval accuracy: 90.33771106941839% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-rotten_tomatoes-2020-06-28-16:10/. Eval accuracy: 89.8686679174484% Eval accuracy: 89.30581613508443% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7fe1c08b56d0> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-rotten_tomatoes-2020-06-28-16:10/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-rotten_tomatoes-2020-06-28-16:10/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-rotten_tomatoes-2020-06-28-16:10/train_args.json. |