Update log.txt
Browse files
log.txt
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| 1 |
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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/log.txt.
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Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mtrain[0m.
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Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mvalidation[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: 635)
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Tokenizing eval data (len: 71)
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Loaded data and tokenized in 4.413618564605713s
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Training model across 4 GPUs
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***** Running training *****
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Num examples = 635
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Batch size = 64
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Max sequence length = 256
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Num steps = 45
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Num epochs = 5
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Learning rate = 2e-05
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Eval accuracy: 59.154929577464785%
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Best acc found. Saved model to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/.
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Eval accuracy: 47.88732394366197%
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Eval accuracy: 45.07042253521127%
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Eval accuracy: 47.88732394366197%
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Eval accuracy: 50.70422535211267%
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Saved tokenizer <textattack.models.tokenizers.auto_tokenizer.AutoTokenizer object at 0x7f9b70a4ba60> to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/.
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Wrote README to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/README.md.
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Wrote training args to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/train_args.json.
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Writing logs to /p/qdata/jm8wx/research/text_attacks/textattack/outputs/training/albert-base-v2-glue:wnli-2020-06-29-11:21/log.txt.
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Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mtrain[0m.
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Loading [94mnlp[0m dataset [94mglue[0m, subset [94mwnli[0m, split [94mvalidation[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: 635)
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Tokenizing eval data (len: 71)
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Loaded data and tokenized in 4.476848840713501s
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Training model across 4 GPUs
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***** Running training *****
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Num examples = 635
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Batch size = 128
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Max sequence length = 256
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Num steps = 20
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Num epochs = 5
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Learning rate = 2e-05
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