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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-yelp_polarity-2020-06-30-16:01/log.txt. Loading [94mnlp[0m dataset [94myelp_polarity[0m, split [94mtrain[0m. Loading [94mnlp[0m dataset [94myelp_polarity[0m, split [94mtest[0m. Loaded dataset. Found: 2 labels: ([0, 1]) Loading transformers AutoModelForSequenceClassification: bert-base-uncased Tokenizing training data. (len: 560000) Tokenizing eval data (len: 38000) Loaded data and tokenized in 1064.7807202339172s Training model across 1 GPUs ***** Running training ***** Num examples = 560000 Batch size = 8 Max sequence length = 512 Num steps = 350000 Num epochs = 5 Learning rate = 5e-05 Eval accuracy: 50.0% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-yelp_polarity-2020-06-30-16:01/. Eval accuracy: 50.00526315789474% Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-yelp_polarity-2020-06-30-16:01/. Eval accuracy: 50.0% Eval accuracy: 50.0% Eval accuracy: 50.0% Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f6bcb56cd00> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-yelp_polarity-2020-06-30-16:01/. Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-yelp_polarity-2020-06-30-16:01/README.md. Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/bert-base-uncased-yelp_polarity-2020-06-30-16:01/train_args.json. |