Update README.md

README.md

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 <h1>Transformer Encoder for Social Science (TESS)</h1>
 
-TESS is a deep neural network model intended for social science related NLP tasks. The model is developed by Haosen Ge, In Young Park, Xuancheng Qian, and Grace Zeng.
+# Transformer Encoder for Social Science (TESS)
 
+TESS is a deep neural network model intended for social science related NLP tasks. The model is developed by Haosen Ge, In Young Park, Xuancheng Qian, and Grace Zeng. 
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+We demonstrate in two validation tests that TESS outperforms BERT and RoBERTa by 16.7\% on average, especially when the number of training samples is limited (<1,000 training instances). The results display the superiority of TESS on social science text processing tasks. 
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+The pretrained model weights can be found on Hugging Face: [TESS_768_v1](https://huggingface.co/hsge/TESS_768_v1).
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+Working paper coming soon ...
 
 <h2>Training Corpus</h2>
 
 |     TEXT      |    SOURCE     |
 | ------------- | ------------- |
 | Preferential Trade Agreements  | ToTA  |
-| Congressional Bills  | BillSum  |
+| Congressional Bills  | Kornilova and Eidelman (2019)  |
 |UNGA Resolutions | UN |
 |Firms' Annual Reports | Loughran and McDonald (2016)|
 | U.S. Court Opinions | Caselaw Access Project|
 
 The model is trained on 4 NVIDIA A100 GPUs for 120K steps.
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