Update README.md

README.md

@@ -1,3 +1,76 @@
 ---
+language:
+- en
+tags:
+- aspect-based-sentiment-analysis
+- PyABSA
 license: mit
+datasets:
+- laptop14 
+- restaurant14 
+- restaurant16 
+- ACL-Twitter 
+- MAMS 
+- Television 
+- TShirt 
+- Yelp 
+metrics:
+- accuracy
+- macro-f1
+
 ---
+# Note
+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!)
+
+# 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, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
+
+model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-base-absa-v1.1")
+```
+
+## 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 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}
+}
+```