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@@ -18,15 +18,15 @@ The difference between SQLNet and this baseline model is, Select-Number and Wher
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## Start to train
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-Firstly, download the provided datasets at ~/data_nl2sql/, which should include train.json, train.tables.json, dev.json, dev.tables.json and char_embedding, and divide them in following structure.
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+Firstly, download the provided datasets at ~/data_nl2sql/, which should include train.json, train.tables.json, val.json, val.tables.json and char_embedding, and divide them in following structure.
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```
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├── data
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│ ├── train
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│ │ ├── train.json
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│ │ ├── train.tables.json
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-│ ├── dev
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-│ │ ├── dev.json
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-│ │ ├── dev.tables.json
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+│ ├── val
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+│ │ ├── val.json
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+│ │ ├── val.tables.json
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├── char_embedding
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```
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and then
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@@ -44,7 +44,7 @@ while the first parameter 0 means gpu number, the second parameter means batch s
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## Start to evaluate
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-To evaluate on dev.json or test.json, make sure trained model is ready, then run
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+To evaluate on val.json or test.json, make sure trained model is ready, then run
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```
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cd ~/nl2sql/nl2sql_baseline/
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sh ./start_test.py 0 pred_example
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@@ -53,7 +53,7 @@ while the first parameter 0 means gpu number, the second parameter means the out
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## Experiment result
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-We have run experiments several times, achiving avegrage 27.5% logic form accuracy on the dev dataset.
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+We have run experiments several times, achiving avegrage 27.5% logic form accuracy on the val dataset.
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## Experiment analysis
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waynesun