wehub-resource-sync
62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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def parse_args():
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--learning_rate", type=float, default=0.001, help="learning rate for optimizer")
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parser.add_argument("--num_layers", type=int, default=1, help="layers number of encoder and decoder")
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parser.add_argument("--hidden_size", type=int, default=100, help="hidden size of encoder and decoder")
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parser.add_argument("--batch_size", type=int, help="batch size of each step")
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parser.add_argument("--max_epoch", type=int, default=12, help="max epoch for the training")
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parser.add_argument("--max_len", type=int, default=50, help="max length for source and target sentence")
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parser.add_argument("--dropout", type=float, default=0.2, help="drop probability")
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parser.add_argument("--init_scale", type=float, default=0.0, help="init scale for parameter")
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parser.add_argument("--max_grad_norm", type=float, default=5.0, help="max grad norm for global norm clip")
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parser.add_argument("--log_freq", type=int, default=100, help="The frequency to print training logs")
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parser.add_argument("--model_path", type=str, default="model", help="model path for model to save")
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parser.add_argument("--infer_output_file", type=str, default="infer_output", help="file name for inference output")
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parser.add_argument("--beam_size", type=int, default=10, help="file name for inference")
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parser.add_argument(
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"--device", default="gpu", choices=["gpu", "cpu", "xpu"], help="Device selected for inference."
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)
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parser.add_argument("--init_from_ckpt", type=str, default=None, help="The path of checkpoint to be loaded.")
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parser.add_argument(
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"--export_path",
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type=str,
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default=None,
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help="The output file prefix used to save the exported inference model.",
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)
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args = parser.parse_args()
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return args
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