readme: ---
  language:
  - en
  tags:
  - aspect-based-sentiment-analysis
  - lcf-bert
  license: mit
  datasets:
  - laptop14 (w/ augmentation)
  - restaurant14 (w/ augmentation)
  - restaurant16 (w/ augmentation)
  - ACL-Twitter (w/ augmentation)
  - MAMS (w/ augmentation)
  - Television (w/ augmentation)
  - TShirt (w/ augmentation)
  - Yelp (w/ augmentation)
  metrics:
  - accuracy
  - macro-f1
  
  ---
  # Note
  This model is training with 180k+ 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-large-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-LSA-T model with `microsoft/deberta-v3-large`, 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, AutoModel
  
  tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-large-absa")
  model = AutoModel.from_pretrained("yangheng/deberta-v3-large-absa")
  
  inputs = tokenizer("good product especially video and audio quality fantastic.", return_tensors="pt")
  outputs = model(**inputs)
  ```
  
  ## Example in PyASBA
  An [example](https://github.com/yangheng95/PyABSA/blob/release/demos/aspect_polarity_classification/train_apc_multilingual.py) for using FAST-LSA-T in PyASBA
  
  ## 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/laptop14/0.cross_boost.fast_lcf_bert_Laptop14_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/laptop14/1.cross_boost.fast_lcf_bert_Laptop14_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/laptop14/2.cross_boost.fast_lcf_bert_Laptop14_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/laptop14/3.cross_boost.fast_lcf_bert_Laptop14_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/restaurant14/Restaurants_Train.xml.seg
  loading: integrated_datasets/apc_datasets/SemEval/restaurant14/0.cross_boost.fast_lcf_bert_Restaurant14_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/restaurant14/1.cross_boost.fast_lcf_bert_Restaurant14_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/restaurant14/2.cross_boost.fast_lcf_bert_Restaurant14_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/restaurant14/3.cross_boost.fast_lcf_bert_Restaurant14_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/restaurant16/restaurant_train.raw
  loading: integrated_datasets/apc_datasets/SemEval/restaurant16/0.cross_boost.fast_lcf_bert_Restaurant16_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/restaurant16/1.cross_boost.fast_lcf_bert_Restaurant16_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/restaurant16/2.cross_boost.fast_lcf_bert_Restaurant16_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/SemEval/restaurant16/3.cross_boost.fast_lcf_bert_Restaurant16_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/train.raw
  loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/0.cross_boost.fast_lcf_bert_Twitter_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/1.cross_boost.fast_lcf_bert_Twitter_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/2.cross_boost.fast_lcf_bert_Twitter_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/3.cross_boost.fast_lcf_bert_Twitter_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/MAMS/train.xml.dat
  loading: integrated_datasets/apc_datasets/MAMS/0.cross_boost.fast_lcf_bert_MAMS_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/MAMS/1.cross_boost.fast_lcf_bert_MAMS_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/MAMS/2.cross_boost.fast_lcf_bert_MAMS_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/MAMS/3.cross_boost.fast_lcf_bert_MAMS_deberta-v3-base.train.augment
  loading: integrated_datasets/apc_datasets/Television/Television_Train.xml.seg
  loading: integrated_datasets/apc_datasets/Television/0.cross_boost.fast_lcf_bert_Television_deberta-v3-base.train.augment
  loading: int