# 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-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, AutoModel

tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-base-absa")
model = AutoModel.from_pretrained("yangheng/deberta-v3-base-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-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/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}
}
```

{"language": ["en"], "license": "mit", "tags": ["aspect-based-sentiment-analysis", "lcf-bert"], "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"]}
transformers pytorch deberta-v2 aspect-based-sentiment-analysis lcf-bert en arxiv:2110.08604 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #aspect-based-sentiment-analysis #lcf-bert #en #arxiv-2110.08604 #license-mit #endpoints_compatible #region-us 

# Note
This model is training with 180k+ ABSA samples, see 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.

## Training Model
This model is trained based on the FAST-LCF-BERT model with 'microsoft/deberta-v3-base', which comes from PyABSA.
To track state-of-the-art models, please see PyASBA.

## Usage 


## Example in PyASBA
An example 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:

If you use  this model in your research, please cite our paper:


# Note
This model is training with 180k+ ABSA samples, see 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. ## Training Model
This model is trained based on the FAST-LCF-BERT model with 'microsoft/deberta-v3-base', which comes from PyABSA.
To track state-of-the-art models, please see PyASBA. ## Usage ## Example in PyASBA
An example 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:

If you use  this model in your research, please cite our paper:
TAGS
#transformers #pytorch #deberta-v2 #aspect-based-sentiment-analysis #lcf-bert #en #arxiv-2110.08604 #license-mit #endpoints_compatible #region-us 
 # Note
This model is training with 180k+ ABSA samples, see 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. ## Training Model
This model is trained based on the FAST-LCF-BERT model with 'microsoft/deberta-v3-base', which comes from PyABSA.
To track state-of-the-art models, please see PyASBA. ## Usage ## Example in PyASBA
An example 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:

If you use  this model in your research, please cite our paper:
TAGS
#transformers #pytorch #deberta-v2 #aspect-based-sentiment-analysis #lcf-bert #en #arxiv-2110.08604 #license-mit #endpoints_compatible #region-us 
# Note
This model is training with 180k+ ABSA samples, see 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.## Training Model
This model is trained based on the FAST-LCF-BERT model with 'microsoft/deberta-v3-base', which comes from PyABSA.
To track state-of-the-art models, please see PyASBA.## Usage## Example in PyASBA
An example 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:

If you use  this model in your research, please cite our paper: