pytorch deberta-v2 en dataset:laptop14 (w/ augmentation) dataset:restaurant14 (w/ augmentation) dataset:restaurant16 (w/ augmentation) dataset:ACL-Twitter (w/ augmentation) dataset:MAMS (w/ augmentation) dataset:Television (w/ augmentation) dataset:TShirt (w/ augmentation) dataset:Yelp (w/ augmentation) arxiv:2110.08604 transformers aspect-based-sentiment-analysis lcf-bert license:mit
---
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: integrated_datasets/apc_datasets/Television/1.cross_boost.fast_lcf_bert_Television_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/Television/2.cross_boost.fast_lcf_bert_Television_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/Television/3.cross_boost.fast_lcf_bert_Television_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/TShirt/Menstshirt_Train.xml.seg
loading: integrated_datasets/apc_datasets/TShirt/0.cross_boost.fast_lcf_bert_TShirt_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/TShirt/1.cross_boost.fast_lcf_bert_TShirt_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/TShirt/2.cross_boost.fast_lcf_bert_TShirt_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/TShirt/3.cross_boost.fast_lcf_bert_TShirt_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/Yelp/yelp.train.txt
loading: integrated_datasets/apc_datasets/Yelp/0.cross_boost.fast_lcf_bert_Yelp_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/Yelp/1.cross_boost.fast_lcf_bert_Yelp_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/Yelp/2.cross_boost.fast_lcf_bert_Yelp_deberta-v3-base.train.augment
loading: integrated_datasets/apc_datasets/Yelp/3.cross_boost.fast_lcf_bert_Yelp_deberta-v3-base.train.augment

```
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}
}
```
