pytorch-textregression，中文文本回归实践，支持多值输出

pytorch-textregression，中文文本回归实践，支持多值输出

pytorch-textregression是一个以pytorch和transformers为基础，专注于中文文本回归的轻量级自然语言处理工具，支持多值回归等。

目录

• 数据
• 使用方式
• paper
• 参考

项目地址

• pytorch-textregression: https://github.com/yongzhuo/Pytorch-NLU/pytorch_textregression

数据格式

1. 文本回归  (txt格式, 每行为一个json):

1.1 单个得分格式:
{"text": "你安静!", "label": [1]}
{"text": "斗牛场是多么欢乐阿!", "label": [1]}
{"text": "今天你不必做作业。", "label": [0]}
{"text": "他醒来时，几乎无法说话。", "label": [0]}
{"text": "在那天边隐约闪亮的不就是黄河？", "label": [1]}

1.2 多个得分格式:
{"text": "你安静!", "label": [1,0]}
{"text": "斗牛场是多么欢乐阿!", "label": [1,0]}
{"text": "今天你不必做作业。", "label": [0,0]}
{"text": "他醒来时，几乎无法说话。", "label": [0,0]}
{"text": "在那天边隐约闪亮的不就是黄河？", "label": [1,0]}

使用方式

更多样例sample详情见test/tr目录

训练  python tet_tr_base_train.py
预测  python tet_tr_base_predict.py

• 1. 需要配置好预训练模型目录, 即变量 pretrained_model_dir、pretrained_model_name_or_path、idx等;
• 2. 需要配置好自己的语料地址, 即字典 model_config[“path_train”]、model_config[“path_dev”]
• 3. cd到该脚本目录下运行普通的命令行即可, 例如: python trRun.py , python trPredict.py

文本回归(TR), Text-Regression

# 适配linux
import platform
import json
import sys
import os
path_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
path_sys = os.path.join(path_root, "pytorch_nlu", "pytorch_textregression")
sys.path.append(path_sys)
print(path_root)
# 分类下的引入, pytorch_textclassification
from trConfig import model_config
from trTools import get_current_time

# 训练-验证语料地址, 可以只输入训练地址
path_corpus = path_root + "/corpus/text_regression/negative_sentence"
path_train = os.path.join(path_corpus, "train.json")
path_dev = os.path.join(path_corpus, "dev.json")
model_config["evaluate_steps"] = evaluate_steps  # 评估步数
model_config["save_steps"] = save_steps  # 存储步数
model_config["path_train"] = path_train
model_config["path_dev"] = path_dev

# 预训练模型适配的class
    model_type = ["BERT", "ERNIE", "BERT_WWM", "ALBERT", "ROBERTA", "XLNET", "ELECTRA"]
    pretrained_model_name_or_path = {
   
        "BERT_WWM":  "hfl/chinese-bert-wwm-ext",
        "ROBERTA":  "hfl/chinese-roberta-wwm-ext",
        "ALBERT":  "uer/albert-base-chinese-cluecorpussmall",
        "XLNET":  "hfl/chinese-xlnet-mid",
        "ERNIE":  "nghuyong/ernie-1.0-base-zh",
        # "ERNIE": "nghuyong/ernie-3.0-base-zh",
        "BERT":  "bert-base-chinese",
        # "BERT": "hfl/chinese-macbert-base",

    }
idx = 1  # 选择的预训练模型类型---model_type
model_config["pretrained_model_name_or_path"] = pretrained_model_name_or_path[model_type[idx]]
model_config["model_save_path"] = "../output/text_regression/model_{}".format(model_type[idx])
model_config["model_type"] = model_type[idx]

# os.environ["CUDA_VISIBLE_DEVICES"] = str(model_config["CUDA_VISIBLE_DEVICES"])

# main
lc = TextRegression(model_config)
lc.process()
lc.train()

Reference

For citing this work, you can refer to the present GitHub project. For example, with BibTeX:

@software{Pytorch-NLU,
    url = {https://github.com/yongzhuo/Pytorch-NLU},
    author = {Yongzhuo Mo},
    title = {Pytorch-NLU},
    year = {2021}
    

*希望对你有所帮助!