paddlenlp

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# Copyright (c) 2022 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 psutil
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from predictor import SPOPredictor
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from paddlenlp.utils.log import logger
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def parse_args():
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    parser = argparse.ArgumentParser()
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    parser.add_argument(
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        "--model_path_prefix", type=str, required=True, help="The path prefix of inference model to be used."
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    )
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    parser.add_argument(
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        "--model_name_or_path", default="ernie-health-chinese", type=str, help="The directory or name of model."
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    )
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    parser.add_argument("--dataset", default="CMeIE", type=str, help="Dataset for named entity recognition.")
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    parser.add_argument("--data_file", default=None, type=str, help="The data to predict with one sample per line.")
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    parser.add_argument(
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        "--max_seq_length", default=300, type=int, help="The maximum total input sequence length after tokenization."
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    )
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    parser.add_argument(
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        "--use_fp16",
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        action="store_true",
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        help="Whether to use fp16 inference, only takes effect when deploying on gpu.",
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    )
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    parser.add_argument(
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        "--num_threads", default=psutil.cpu_count(logical=False), type=int, help="num_threads for cpu."
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    )
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    parser.add_argument("--batch_size", default=20, type=int, help="Batch size per GPU/CPU for predicting.")
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    parser.add_argument(
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        "--device", choices=["cpu", "gpu"], default="gpu", help="Select which device to train model, defaults to gpu."
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    )
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    parser.add_argument("--device_id", default=0, help="Select which gpu device to train model.")
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    args = parser.parse_args()
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    return args
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LABEL_LIST = {
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    "cmeie": [
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        "预防",
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        "阶段",
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        "就诊科室",
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        "辅助治疗",
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        "化疗",
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        "放射治疗",
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        "手术治疗",
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        "实验室检查",
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        "影像学检查",
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        "辅助检查",
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        "组织学检查",
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        "内窥镜检查",
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        "筛查",
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        "多发群体",
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        "发病率",
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        "发病年龄",
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        "多发地区",
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        "发病性别倾向",
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        "死亡率",
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        "多发季节",
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        "传播途径",
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        "并发症",
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        "病理分型",
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        "相关（导致）",
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        "鉴别诊断",
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        "相关（转化）",
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        "相关（症状）",
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        "临床表现",
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        "治疗后症状",
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        "侵及周围组织转移的症状",
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        "病因",
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        "高危因素",
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        "风险评估因素",
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        "病史",
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        "遗传因素",
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        "发病机制",
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        "病理生理",
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        "药物治疗",
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        "发病部位",
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        "转移部位",
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        "外侵部位",
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        "预后状况",
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        "预后生存率",
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        "同义词",
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    ]
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}
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TEXT = {"cmeie": ["骶髂关节炎是明确诊断JAS的关键条件。若有肋椎关节病变会使胸部扩张度减小。", "稳定型缺血性心脏疾病@肥胖与缺乏活动也导致高血压增多。"]}
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def main():
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    args = parse_args()
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    for arg_name, arg_value in vars(args).items():
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        logger.info("{:20}: {}".format(arg_name, arg_value))
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    dataset = args.dataset.lower()
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    label_list = LABEL_LIST[dataset]
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    if args.data_file is not None:
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        with open(args.data_file, "r") as fp:
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            input_data = [x.strip() for x in fp.readlines()]
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    else:
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        input_data = TEXT[dataset]
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    predictor = SPOPredictor(args, label_list)
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    predictor.predict(input_data)
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if __name__ == "__main__":
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    main()
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