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]
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
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.
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:
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:
@article{YangL22,
author = {Heng Yang and
Ke Li},
title = {A Modularized Framework for Reproducible Aspect-based Sentiment Analysis},
journal = {CoRR},
volume = {abs/2208.01368},
year = {2022},
url = {https://doi.org/10.48550/arXiv.2208.01368},
doi = {10.48550/arXiv.2208.01368},
eprinttype = {arXiv},
eprint = {2208.01368},
timestamp = {Tue, 08 Nov 2022 21:46:32 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2208-01368.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}
}