由 PyABSA 提供支持：一款开源的方面级情感分析工具

该模型使用 30k+ ABSA 样本进行训练，详见 ABSADatasets。但测试集未包含在预训练中，因此您可以使用此模型在常见的 ABSA 数据集（如 Laptop14、Rest14 数据集）上进行训练和基准测试。（Rest15 数据集除外！）

用法

import argparse
import torch
from openmind import pipeline, is_torch_npu_available

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        default=None,
        type=str,
        help="Path to model",
        required=False,
    )

    args = parser.parse_args()

    return args

if __name__=="__main__":

    args = parse_args()

    if is_torch_npu_available():
        device = "npu:0"
    else:
        device = "cpu"

    #推理
    classifier = pipeline('text-classification', model=args.model_name_or_path, device=device)
    
    for aspect in ['camera', 'phone']:
        print(aspect, classifier('The camera quality of this phone is amazing.',  text_pair=aspect))

DeBERTa 在基于方面的情感分析中的应用

deberta-v3-base-absa 模型适用于基于方面的情感分析，它是利用 ABSADatasets 中的英文数据集进行训练的。

训练模型

该模型以 microsoft/deberta-v3-base 为基础，基于 FAST-LCF-BERT 模型进行训练，其源自 PyABSA。若想了解最先进的模型，请访问 PyASBA。

PyASBA 中的示例

在 PyASBA 数据集中使用 FAST-LCF-BERT 的示例。

数据集

此模型在 ABSA 数据集（包含增强数据）的 18 万示例上进行了微调。训练数据集文件如下：

loading: integrated_datasets/apc_datasets/SemEval/laptop14/Laptops_Train.xml.seg
loading: integrated_datasets/apc_datasets/SemEval/restaurant14/Restaurants_Train.xml.seg
loading: integrated_datasets/apc_datasets/SemEval/restaurant16/restaurant_train.raw
loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/train.raw
loading: integrated_datasets/apc_datasets/MAMS/train.xml.dat
loading: integrated_datasets/apc_datasets/Television/Television_Train.xml.seg
loading: integrated_datasets/apc_datasets/TShirt/Menstshirt_Train.xml.seg
loading: integrated_datasets/apc_datasets/Yelp/yelp.train.txt

如果您在研究中使用此模型，请引用我们的论文：

@inproceedings{DBLP:conf/cikm/0008ZL23,
  author       = {Heng Yang and
                  Chen Zhang and
                  Ke Li},
  editor       = {Ingo Frommholz and
                  Frank Hopfgartner and
                  Mark Lee and
                  Michael Oakes and
                  Mounia Lalmas and
                  Min Zhang and
                  Rodrygo L. T. Santos},
  title        = {PyABSA: {A} Modularized Framework for Reproducible Aspect-based Sentiment
                  Analysis},
  booktitle    = {Proceedings of the 32nd {ACM} International Conference on Information
                  and Knowledge Management, {CIKM} 2023, Birmingham, United Kingdom,
                  October 21-25, 2023},
  pages        = {5117--5122},
  publisher    = {{ACM}},
  year         = {2023},
  url          = {https://doi.org/10.1145/3583780.3614752},
  doi          = {10.1145/3583780.3614752},
  timestamp    = {Thu, 23 Nov 2023 13:25:05 +0100},
  biburl       = {https://dblp.org/rec/conf/cikm/0008ZL23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}
@article{YangZMT21,
  author    = {Heng Yang and
               Biqing Zeng and
               Mayi Xu and
               Tianxing Wang},
  title     = {Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable
               Sentiment Dependency Learning},
  journal   = {CoRR},
  volume    = {abs/2110.08604},
  year      = {2021},
  url       = {https://arxiv.org/abs/2110.08604},
  eprinttype = {arXiv},
  eprint    = {2110.08604},
  timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2110-08604.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}