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TESS_768_v1
hsge/TESS_768_v1
TESS_768_v1 is a machine learning model from hsge. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.
<h1Transformer Encoder for Social Science (TESS)</h1
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At a glance
- Library
- transformers
- License
- mit
- Model type
- albert
- Access
- Public
- Created
- Aug 12, 2022
- Updated
- Aug 18, 2022
- SHA
- be7c034b
Model card
From the Hugging Face model README
<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.
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.
GitHub: TESS.
<h2>Training Corpus</h2>| 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 |
The model is trained on 4 NVIDIA A100 GPUs for 120K steps.
- Library
- transformers
- Type
- albert
- License
- mit
- Created
- Aug 12, 2022
- Updated
- Aug 18, 2022