Comments (3)
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reformulator_and_selector_training should plateau at ~47.4 F1 score after 1 epoch (or ~5k minibatch updates). Also, you can follow the training progress by lauching tensorboard:
tensorboard --logdir=$OUT_DIR/tensorboard
Then navigate your web browser to http://localhost:6006 -
We will provide code to save/load selector checkpoints soon, along with a fully trained selector checkpoint. If you don't want to wait, you can call load() and save() functions in the px/selector/selector_keras.py accordingly.
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@rodrigonogueira4 Did you happen to use 1 epoch for reformulator training too?
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No, the reformulator takes 20-100 epochs to reach 42.4 F1 on the dev set, which is about a week of training on a single P100 GPU.
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Related Issues (20)
- odd gprc status reporting during selector training HOT 2
- ImportError: cannot import name 'aqa_pb2' HOT 8
- How do i use Selector for a custom document? HOT 6
- Getting a a grpc.FutureTimeoutError while using Reformulator from the checkpoint HOT 2
- Nonsensical reformed queries from Reformulator HOT 1
- Model pretrained on UN and Paralex datasets
- Are you running the environment server before running the reformulator code? HOT 10
- Getting a grpc.FutureTimeoutError while using Reformulator from the checkpoint HOT 1
- ValueError from the run environment step
- Have not found Monte Carlo Sampling in the code HOT 3
- Setup on Windows
- Docker Image?
- Installation Error
- Assertion Error
- The Process is killed in the middle
- Getting an error while running the reformulator_training file
- How to test with own txt file or a document file
- Problem in download of reformulator pretrained model. HOT 3
- Problem in download the selector pretrained model.
- reformulator and selector links are invalid HOT 2
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