Update log.txt
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log.txt
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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-yelp_polarity-2020-07-02-10:35/log.txt.
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Loading [94mnlp[0m dataset [94myelp_polarity[0m, split [94mtrain[0m.
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Loading [94mnlp[0m dataset [94myelp_polarity[0m, split [94mtest[0m.
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Loaded dataset. Found: 2 labels: ([0, 1])
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Loading transformers AutoModelForSequenceClassification: albert-base-v2
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Tokenizing training data. (len: 560000)
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Tokenizing eval data (len: 38000)
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Loaded data and tokenized in 1196.1114358901978s
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Training model across 4 GPUs
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***** Running training *****
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Num examples = 560000
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Batch size = 16
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Max sequence length = 512
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Num steps = 175000
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Num epochs = 5
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Learning rate = 3e-05
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Eval accuracy: 96.92368421052632%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-yelp_polarity-2020-07-02-10:35/.
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Eval accuracy: 97.23947368421054%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-yelp_polarity-2020-07-02-10:35/.
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Eval accuracy: 97.18947368421053%
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Eval accuracy: 97.5078947368421%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-yelp_polarity-2020-07-02-10:35/.
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Eval accuracy: 97.42894736842105%
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Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f7e11b25fd0> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-yelp_polarity-2020-07-02-10:35/.
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Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-yelp_polarity-2020-07-02-10:35/README.md.
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Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-yelp_polarity-2020-07-02-10:35/train_args.json.
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