Update README.md

README.md

@@ -1,8 +1,29 @@
+---
+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
+---
+
 # 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).
+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 
@@ -17,7 +38,7 @@ 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
+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
 This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files:
@@ -84,4 +105,3 @@ If you use  this model in your research, please cite our paper:
   bibsource = {dblp computer science bibliography, https://dblp.org}
 }
 ```
-