readme: 
  ---
  language:
  - en
  tags:
  - aspect-based-sentiment-analysis
  - PyABSA
  license: mit
  datasets:
  - laptop14
  - restaurant14
  - restaurant16
  - ACL-Twitter
  - MAMS 
  - Television
  - TShirt 
  - Yelp 
  metrics:
  - accuracy
  - macro-f1
  widget: 
  - text: "[CLS] when tables opened up, the manager sat another party before us. [SEP] manager [SEP] "
  ---
  
  # Note
  This model is training with 30k+ ABSA samples, see [ABSADatasets](https://github.com/yangheng95/ABSADatasets). Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!)
  
  # DeBERTa for aspect-based sentiment analysis
  The `deberta-v3-base-absa` model for aspect-based sentiment analysis, trained with English datasets from [ABSADatasets](https://github.com/yangheng95/ABSADatasets).
  
  ## Training Model
  This model is trained based on the FAST-LCF-BERT model with `microsoft/deberta-v3-base`, which comes from [PyABSA](https://github.com/yangheng95/PyABSA).
  To track state-of-the-art models, please see [PyASBA](https://github.com/yangheng95/PyABSA).
  
  ## Usage 
  ```python3
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  
  tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
  
  model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
  ```
  
  ## Example in PyASBA
  An [example](https://github.com/yangheng95/PyABSA/blob/release/demos/aspect_polarity_classification/train_apc_multilingual.py) for using FAST-LCF-BERT in PyASBA datasets.
  
  ## Datasets 
  This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files:
  ```
  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
  
  ```
  If you use  this model in your research, please cite our paper:
  ```
  @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}
  }
  ```