Comments (1)
Hi!
Yes, the config was used for the DG-MAML experiments where the number of training steps is controlled for comparison. To obtain a stronger base parser, you can either increase the number of training steps or increase num_batch_accumulated to 2 or 3, and activate label_smooth for loss_type.
Hope it helps
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Related Issues (17)
- Pre-trained models HOT 1
- a question on running CSpider dataset HOT 2
- Runtime of the base model
- spider processing
- where is the detail code for SQL generating by PCFG? HOT 1
- Perhaps a bug in the code HOT 2
- ERROR: Saving directory (...tensor2struct-public/.vector_cache/bert-base-multilingual-uncased) should be a directory HOT 1
- Segmentation Fault Issue Encountered in Spider Experiment HOT 1
- Problem with synthetic data having JOIN HOT 5
- Unable to pre-train for task sql2nl HOT 2
- cannot import name 'data_scheduler' from 'experiments.sql2nl' HOT 2
- Does the model sql2nl supports the wikiTable datasets? HOT 1
- A problem encountered while processing Spider data HOT 4
- a question on running MLCG-codes HOT 2
- Processed GeoQuery HOT 1
- Fitting a general PCFG for all the DBs HOT 1
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