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log.txt

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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 nlp dataset yelp_polarity, split train.
+Loading nlp dataset yelp_polarity, split test.
+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.