1. use `paddlepalm.reader` to create a *reader* for dataset loading and input features generation, then call `reader.load_data` method to load your training data.
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@@ -193,14 +195,8 @@ For more implementation details, see following demos:
To save models/checkpoints and logs during training, just call `trainer.set_saver` method. More implementation details see [this](https://github.com/PaddlePaddle/PALM/tree/master/examples).
### do prediction
To do predict/evaluation after a training stage, just create another three reader, backbone and head instance with `phase='predict'` (repeat step 1~4 above). Then do predicting with `predict` method in trainer (no need to create another trainer). More implementation details see [this](https://github.com/PaddlePaddle/PALM/tree/master/examples/predict).
### multi-task learning
#### Multi-task Learning
To run with multi-task learning mode:
1. repeatedly create components (i.e., reader, backbone and head) for each task followed with step 1~5 above.
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@@ -216,6 +212,12 @@ For more implementation details with `multi_head_trainer`, see
-[ATIS: joint training of dialogue intent recognition and slot filling](https://github.com/PaddlePaddle/PALM/tree/master/examples/multi-task)
#### Save models
To save models/checkpoints and logs during training, just call `trainer.set_saver` method. More implementation details see [this](https://github.com/PaddlePaddle/PALM/tree/master/examples).
#### Evaluation/Inference
To do predict/evaluation after a training stage, just create another three reader, backbone and head instance with `phase='predict'` (repeat step 1~4 above). Then do predicting with `predict` method in trainer (no need to create another trainer). More implementation details see [this](https://github.com/PaddlePaddle/PALM/tree/master/examples/predict).
