Comments (5)
And is there a way to run single test case inference on the cross-encoder alone? How do I input a question and the corresponding columns and get the reduced input sequence to the decoder.
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Hey, I would like to know what to do, to train the model with data so that it supports timestamps functionality as like in Druid SQL interface for example. Is there a way to do add timestamps functionality as well.
Maybe you have to train RESDSQL on your dataset with SQL queries that use the timestamp function. The checkpoints we released were trained on Spider and it only supports the following SQL keywords: ['SELECT', 'FROM', 'WHERE', 'GROUP', 'ORDER', 'LIMIT', 'INTERSECT', 'UNION', 'EXCEPT', 'JOIN', 'ON', 'AS', 'NOT', 'BETWEEN', 'IN', 'LIKE', 'IS', 'EXISTS', 'MAX', 'MIN', 'COUNT', 'SUM', 'AVG', 'AND', 'OR', 'DESC', 'ASC'].
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And is there a way to run single test case inference on the cross-encoder alone? How do I input a question and the corresponding columns and get the reduced input sequence to the decoder.
No, the input of the cross-encoder is a preprocessed dataset, so you should run preprocessing.py
on your dataset before running the cross-encoder.
from resdsql.
Hey, I would like to know what to do, to train the model with data so that it supports timestamps functionality as like in Druid SQL interface for example. Is there a way to do add timestamps functionality as well.
Maybe you have to train RESDSQL on your dataset with SQL queries that use the timestamp function. The checkpoints we released were trained on Spider and it only supports the following SQL keywords: ['SELECT', 'FROM', 'WHERE', 'GROUP', 'ORDER', 'LIMIT', 'INTERSECT', 'UNION', 'EXCEPT', 'JOIN', 'ON', 'AS', 'NOT', 'BETWEEN', 'IN', 'LIKE', 'IS', 'EXISTS', 'MAX', 'MIN', 'COUNT', 'SUM', 'AVG', 'AND', 'OR', 'DESC', 'ASC'].
To do training with your code for such a case, should I just add the functions for which I need support in the sql keywords list in the preprocessing.py file or are there more changes that are needed to be done?
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Hi, the sql_keywords recorded in preprocessing.py is just to remind me which SQL keywords are involved in the Spider dataset, it is not used by other files, so you don't need to make any changes.
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Related Issues (20)
- Execuse me. What happened to paper CodeS? Isn't this article open source before? HOT 9
- Low training metrics HOT 14
- Support for Historical Conversation in RESDSQL HOT 4
- Question about evaluation scripts HOT 2
- 请问推理方法 HOT 2
- 最低支持的GPU内存是多少,我怎么跑不起来。
- Dev result file?
- 部分带有别名的sql在经过normalization处理后出现错误 HOT 2
- Inference script not working HOT 5
- CoSQL HOT 1
- 训练Cross-Encoder的时候为什么24G的显存还不够用? HOT 1
- 关于RESDSQL在BIRD上的运行时间 HOT 2
- Training cross-coder error HOT 1
- xlm_roberta_text2natsql_schema_item_classifier HOT 3
- Evaluation detail on CSpider HOT 1
- 你好,请问如何将自己的数据集处理成CSpider的形式? HOT 3
- 你好,请问如何SQL2NatSQL?我想用自己的数据集跑text2NatSQL的方法。 HOT 2
- 请问模型训练有多gpu并行支持吗 HOT 1
- Can the ranking-filter successfully choose all the right schema items? HOT 1
- 为什么我使用对bird训练的classifier时出现了truncated_dataset.json文件,而且陷入了循环无法结束运行 HOT 1
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