
# Powered by [PyABSA](https://github.com/yangheng95/PyABSA): An open source tool for aspect-based sentiment analysis
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!)


## Usage 
```python3
from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the ABSA model and tokenizer
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))
```

# 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).

## 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 papers:
```
@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}
}
```
{"language": ["en"], "license": "mit", "tags": ["aspect-based-sentiment-analysis", "PyABSA"], "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] "}]}
transformers pytorch safetensors deberta-v2 text-classification aspect-based-sentiment-analysis PyABSA en dataset:laptop14 dataset:restaurant14 dataset:restaurant16 dataset:ACL-Twitter dataset:MAMS dataset:Television dataset:TShirt dataset:Yelp arxiv:2110.08604 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #aspect-based-sentiment-analysis #PyABSA #en #dataset-laptop14 #dataset-restaurant14 #dataset-restaurant16 #dataset-ACL-Twitter #dataset-MAMS #dataset-Television #dataset-TShirt #dataset-Yelp #arxiv-2110.08604 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Powered by PyABSA: An open source tool for aspect-based sentiment analysis
This model is training with 30k+ 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!)


## Usage 


# 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.

## 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 papers:

# Powered by PyABSA: An open source tool for aspect-based sentiment analysis
This model is training with 30k+ 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!) ## Usage # 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. ## 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 papers:
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #aspect-based-sentiment-analysis #PyABSA #en #dataset-laptop14 #dataset-restaurant14 #dataset-restaurant16 #dataset-ACL-Twitter #dataset-MAMS #dataset-Television #dataset-TShirt #dataset-Yelp #arxiv-2110.08604 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Powered by PyABSA: An open source tool for aspect-based sentiment analysis
This model is training with 30k+ 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!) ## Usage # 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. ## 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 papers:
TAGS
#transformers #pytorch #safetensors #deberta-v2 #text-classification #aspect-based-sentiment-analysis #PyABSA #en #dataset-laptop14 #dataset-restaurant14 #dataset-restaurant16 #dataset-ACL-Twitter #dataset-MAMS #dataset-Television #dataset-TShirt #dataset-Yelp #arxiv-2110.08604 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# Powered by PyABSA: An open source tool for aspect-based sentiment analysis
This model is training with 30k+ 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!)## Usage# 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.## 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 papers: