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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:sst2-2020-06-30-00:28/log.txt. Loading [94mnlp[0m dataset [94mglue[0m, subset [94msst2[0m, split [94mtrain[0m. Loading [94mnlp[0m dataset [94mglue[0m, subset [94msst2[0m, split [94mvalidation[0m. Loaded dataset. Found: 2 labels: ([0, 1]) Loading transformers AutoModelForSequenceClassification: albert-base-v2 Tokenizing training data. (len: 67349) Tokenizing eval data (len: 872) Loaded data and tokenized in 100.11767911911011s Training model across 1 GPUs ***** Running training ***** Num examples = 67349 Batch size = 32 Max sequence length = 64 Num steps = 10520 Num epochs = 5 Learning rate = 3e-05 Eval accuracy: 91.74311926605505% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:sst2-2020-06-30-00:28/. Eval accuracy: 91.74311926605505% Eval accuracy: 92.54587155963303% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:sst2-2020-06-30-00:28/. Eval accuracy: 91.62844036697247% Eval accuracy: 91.97247706422019% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7fa8880a6d60> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:sst2-2020-06-30-00:28/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:sst2-2020-06-30-00:28/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:sst2-2020-06-30-00:28/train_args.json. |