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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/log.txt. Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mtrain[0m. Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mvalidation[0m. Loaded dataset. Found: 2 labels: ([0, 1]) Loading transformers AutoModelForSequenceClassification: xlnet-base-cased Tokenizing training data. (len: 635) Tokenizing eval data (len: 71) Loaded data and tokenized in 8.763058185577393s Training model across 1 GPUs ***** Running training ***** Num examples = 635 Batch size = 16 Max sequence length = 256 Num steps = 195 Num epochs = 5 Learning rate = 3e-05 Eval accuracy: 57.74647887323944% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/. Eval accuracy: 56.33802816901409% Eval accuracy: 45.07042253521127% Eval accuracy: 45.07042253521127% Eval accuracy: 42.25352112676056% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f23225c6af0> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/xlnet-base-cased-glue:wnli-2020-06-29-10:28/train_args.json. |