Update README to remove blank line at top

this blank line completely invalidates the markdown metadata schema. We have some internal code that checks at markdown metadata to get the license and the blank line will lead to markdown's `Meta` being absent

README.md

@@ -1,79 +1,78 @@
-
----
-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] "
----
-
-# 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-large-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-large`, 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-large-absa-v1.1")
-
-model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-large-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}
-}
+---
+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] "
+---
+
+# 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-large-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-large`, 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-large-absa-v1.1")
+
+model = AutoModelForSequenceClassification.from_pretrained("yangheng/deberta-v3-large-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}
+}
 ```