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 CLSPredictor
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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="KUAKE-QIC", type=str, help="Dataset for text classfication.")
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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=128, 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("--batch_size", default=200, type=int, help="Batch size per GPU/CPU for predicting.")
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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(
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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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    "kuake-qic": ["病情诊断", "治疗方案", "病因分析", "指标解读", "就医建议", "疾病表述", "后果表述", "注意事项", "功效作用", "医疗费用", "其他"],
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    "kuake-qtr": ["完全不匹配", "很少匹配，有一些参考价值", "部分匹配", "完全匹配"],
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    "kuake-qqr": ["B为A的语义父集，B指代范围大于A； 或者A与B语义毫无关联。", "B为A的语义子集，B指代范围小于A。", "表示A与B等价，表述完全一致。"],
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    "chip-ctc": [
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        "成瘾行为",
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        "居住情况",
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        "年龄",
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        "酒精使用",
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        "过敏耐受",
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        "睡眠",
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        "献血",
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        "能力",
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        "依存性",
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        "知情同意",
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        "数据可及性",
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        "设备",
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        "诊断",
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        "饮食",
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        "残疾群体",
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        "疾病",
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        "教育情况",
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        "病例来源",
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        "参与其它试验",
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        "伦理审查",
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        "种族",
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        "锻炼",
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        "性别",
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        "健康群体",
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        "实验室检查",
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        "预期寿命",
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        "读写能力",
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        "含有多类别的语句",
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        "肿瘤进展",
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        "疾病分期",
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        "护理",
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        "口腔相关",
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        "器官组织状态",
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        "药物",
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        "怀孕相关",
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        "受体状态",
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        "研究者决定",
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        "风险评估",
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        "性取向",
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        "体征(医生检测）",
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        " 吸烟状况",
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        "特殊病人特征",
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        "症状(患者感受)",
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        "治疗或手术",
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    ],
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    "chip-sts": ["语义不同", "语义相同"],
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    "chip-cdn-2c": ["否", "是"],
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}
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TEXT = {
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    "kuake-qic": ["心肌缺血如何治疗与调养呢？", "什么叫痔核脱出？什么叫外痔？"],
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    "kuake-qtr": [["儿童远视眼怎么恢复视力", "远视眼该如何保养才能恢复一些视力"], ["抗生素的药有哪些", "抗生素类的药物都有哪些？"]],
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    "kuake-qqr": [["茴香是发物吗", "茴香怎么吃？"], ["气的胃疼是怎么回事", "气到胃痛是什么原因"]],
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    "chip-ctc": ["(1)前牙结构发育不良：釉质发育不全、氟斑牙、四环素牙等；", "怀疑或确有酒精或药物滥用史；"],
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    "chip-sts": [["糖尿病能吃减肥药吗？能治愈吗？", "糖尿病为什么不能吃减肥药"], ["H型高血压的定义", "WHO对高血压的最新分类定义标准数值"]],
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    "chip-cdn-2c": [["1型糖尿病性植物神经病变", " 1型糖尿病肾病IV期"], ["髂腰肌囊性占位", "髂肌囊肿"]],
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}
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METRIC = {
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    "kuake-qic": "acc",
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    "kuake-qtr": "acc",
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    "kuake-qqr": "acc",
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    "chip-ctc": "macro",
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    "chip-sts": "macro",
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    "chip-cdn-2c": "macro",
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}
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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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    args.dataset = args.dataset.lower()
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    label_list = LABEL_LIST[args.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().split("\t") for x in fp.readlines()]
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            input_data = [x[0] if len(x) == 1 else x for x in input_data]
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    else:
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        input_data = TEXT[args.dataset]
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    predictor = CLSPredictor(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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