Deberta V3 Base Absa V1.1

🚀 基於方面的情感分析模型

本模型由 PyABSA 驅動，是一個用於基於方面的情感分析的開源工具。該模型使用 30k+ 的基於方面的情感分析（ABSA）樣本進行訓練，詳情見 ABSADatasets。測試集未包含在預訓練中，因此你可以使用此模型在常見的 ABSA 數據集（如 Laptop14、Rest14 數據集）上進行訓練和基準測試（Rest15 數據集除外！）

🚀 快速開始

模型信息

模型使用

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# 加載 ABSA 模型和分詞器
model_name = "yangheng/deberta-v3-base-absa-v1.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

for aspect in ['camera', 'phone']:
   print(aspect, classifier('The camera quality of this phone is amazing.',  text_pair=aspect))

✨ 主要特性

• 本模型是用於基於方面的情感分析的 deberta - v3 - base - absa 模型，使用來自 ABSADatasets 的英文數據集進行訓練。
• 該模型基於 microsoft/deberta-v3-base 和 FAST - LCF - BERT 模型進行訓練，相關代碼來自 PyABSA。

📚 詳細文檔

訓練模型

此模型基於 microsoft/deberta-v3-base 和 FAST - LCF - BERT 模型進行訓練，相關代碼來自 PyABSA。若要跟蹤最先進的模型，請參閱 PyASBA。

PyASBA 中的示例

示例展示瞭如何在 PyASBA 數據集中使用 FAST - LCF - BERT。

數據集

該模型使用 180k 個 ABSA 數據集示例（包括增強數據）進行微調。訓練數據集文件如下：

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

引用

如果你在研究中使用此模型，請引用我們的論文：

@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}
}

📄 許可證

本項目採用 MIT 許可證。