情感分类任务,通常为输入一段句子或一段话,返回该段话正向/负向的情感极性,在用户评价,观点抽取,意图识别中往往起到重要作用。而在电商场景中,情感分类显得尤为重要,可以通过对商品评论情感极性的分析,作为对商品质量及相关服务质量把控的重要参考依据。StructBERT中文情感分类模型是基于百万电商评价数据训练出来的情感分类模型。
模型基于Structbert-base-chinese,基于百万电商评价数据fine-tune得来。
你可以使用StructBERT中文情感分类模型模型,对通用领域的中文情感分类任务进行推理。
输入自然语言文本,模型会给出该文本的情感分类标签(0,1),即(负面, 正面)以及相应的概率。
在安装完成ModelScope-lib之后即可使用
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
semantic_cls = pipeline(Tasks.text_classification, 'damo/nlp_structbert_sentiment-classification_chinese-ecommerce-base')
semantic_cls(input='启动的时候很大声音,然后就会听到1.2秒的卡察的声音,类似齿轮摩擦的声音')
import os.path as osp
from modelscope.trainers import build_trainer
from modelscope.msdatasets import MsDataset
from modelscope.utils.hub import read_config
from modelscope.metainfo import Metrics
model_id = 'damo/nlp_structbert_sentiment-classification_chinese-ecommerce-base'
dataset_id = 'jd'
WORK_DIR = 'workspace'
max_epochs = 2
def cfg_modify_fn(cfg):
cfg.train.max_epochs = max_epochs
cfg.train.hooks = cfg.train.hooks = [{
'type': 'TextLoggerHook',
'interval': 100
}]
cfg.evaluation.metrics = [Metrics.seq_cls_metric]
cfg['dataset'] = {
'train': {
'labels': ['负面', '正面'],
'first_sequence': 'sentence',
'label': 'label',
}
}
return cfg
train_dataset = MsDataset.load(dataset_id, namespace='DAMO_NLP', split='train').to_hf_dataset()
eval_dataset = MsDataset.load(dataset_id, namespace='DAMO_NLP', split='validation').to_hf_dataset()
# remove useless case
train_dataset = train_dataset.filter(lambda x: x["label"] != None and x["sentence"] != None)
eval_dataset = eval_dataset.filter(lambda x: x["label"] != None and x["sentence"] != None)
# map float to index
def map_labels(examples):
map_dict = {0: "负面", 1: "正面"}
examples['label'] = map_dict[int(examples['label'])]
return examples
train_dataset = train_dataset.map(map_labels)
eval_dataset = eval_dataset.map(map_labels)
kwargs = dict(
model=model_id,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
work_dir=WORK_DIR,
cfg_modify_fn=cfg_modify_fn)
trainer = build_trainer(name='nlp-base-trainer', default_args=kwargs)
print('===============================================================')
print('pre-trained model loaded, training started:')
print('===============================================================')
trainer.train()
print('===============================================================')
print('train success.')
print('===============================================================')
for i in range(max_epochs):
eval_results = trainer.evaluate(f'{WORK_DIR}/epoch_{i+1}.pth')
print(f'epoch {i} evaluation result:')
print(eval_results)
print('===============================================================')
print('evaluate success')
print('===============================================================')
模型训练数据有限,效果可能存在一定偏差。
数据来源于百万电商评价数据。
F1 92.17
@article{wang2019structbert,
title={Structbert: Incorporating language structures into pre-training for deep language understanding},
author={Wang, Wei and Bi, Bin and Yan, Ming and Wu, Chen and Bao, Zuyi and Xia, Jiangnan and Peng, Liwei and Si, Luo},
journal={arXiv preprint arXiv:1908.04577},
year={2019}
}