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--- |
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license: mit |
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--- |
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<span style="color:blue">**Note: please check [DeepKPG](https://github.com/uclanlp/DeepKPG#scibart) for using this model in huggingface, including setting up the newly trained tokenizer.**</span>. |
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Paper: [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://arxiv.org/abs/2212.10233) |
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``` |
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@article{https://doi.org/10.48550/arxiv.2212.10233, |
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doi = {10.48550/ARXIV.2212.10233}, |
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url = {https://arxiv.org/abs/2212.10233}, |
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author = {Wu, Di and Ahmad, Wasi Uddin and Chang, Kai-Wei}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
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Pre-training Corpus: [S2ORC (titles and abstracts)](https://github.com/allenai/s2orc) |
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Pre-training Details: |
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- **Pre-trained from scratch with science vocabulary** |
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- Batch size: 2048 |
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- Total steps: 250k |
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- Learning rate: 3e-4 |
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- LR schedule: polynomial with 10k warmup steps |
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- Masking ratio: 30%, Poisson lambda = 3.5 |
