wehub-resource-sync
62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import paddle
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from paddlenlp.transformers import SkepForSequenceClassification
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--ckpt_dir",
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type=str,
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required=True,
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default="./checkpoint/model_100",
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help="The directory of saved model checkpoint.",
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)
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parser.add_argument(
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"--output_path",
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type=str,
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default="./static_graph_params",
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help="The path of model parameter in static graph to be saved.",
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)
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parser.add_argument(
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"--model_name",
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choices=["skep_ernie_1.0_large_ch", "skep_ernie_2.0_large_en"],
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default="skep_ernie_1.0_large_ch",
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help="Select which model to train, defaults to skep_ernie_1.0_large_ch.",
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)
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args = parser.parse_args()
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if __name__ == "__main__":
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# The number of labels should be in accordance with the training dataset.
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label_map = {0: "negative", 1: "positive"}
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model = SkepForSequenceClassification.from_pretrained(args.ckpt_dir, num_labels=len(label_map))
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print("Loaded model from %s" % args.ckpt_dir)
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model.eval()
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# Convert to static graph with specific input description
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model = paddle.jit.to_static(
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model,
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input_spec=[
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paddle.static.InputSpec(shape=[None, None], dtype="int64"), # input_ids
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paddle.static.InputSpec(shape=[None, None], dtype="int64"), # segment_ids
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],
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)
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# Save in static graph model.
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paddle.jit.save(model, args.output_path)
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print("Static Model has been saved to: {}".format(args.output_path))
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