SetSUMBT-dst-sgd
This model is a fine-tuned version SetSUMBT of roberta-base on Schema-Guided Dialog.
Refer to ConvLab-3 for model description and usage.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00001
- train_batch_size: 3
- eval_batch_size: 16
- seed: 0
- gradient_accumulation_steps: 1
- optimizer: AdamW
- lr_scheduler_type: linear
- num_epochs: 50.0
Framework versions
- Transformers 4.17.0
- Pytorch 1.8.0+cu110
- Datasets 2.3.2
- Tokenizers 0.12.1
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Inference Providers
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Dataset used to train ConvLab/setsumbt-dst-sgd
Evaluation results
- JGA on SGDtest set self-reported20.000
- Slot F1 on SGDtest set self-reported58.800