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| | license: mit |
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| | <h1>Transformer Encoder for Social Science (TESS)</h1> |
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| | 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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| | GitHub: [TESS](https://github.com/haosenge/TESS). |
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| | <h2>Training Corpus</h2> |
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| | | TEXT | SOURCE | |
| | | ------------- | ------------- | |
| | | Preferential Trade Agreements | ToTA | |
| | | Congressional Bills | Kornilova and Eidelman (2019) | |
| | |UNGA Resolutions | UN | |
| | |Firms' Annual Reports | Loughran and McDonald (2016)| |
| | | U.S. Court Opinions | Caselaw Access Project| |
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| | The model is trained on 4 NVIDIA A100 GPUs for 120K steps. |
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