
**Note: please check [DeepKPG](https://github.com/uclanlp/DeepKPG#scibart) for using this model in huggingface, including setting up the newly trained tokenizer.**

Paper: [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://arxiv.org/abs/2212.10233)

```
@article{https://doi.org/10.48550/arxiv.2212.10233,
  doi = {10.48550/ARXIV.2212.10233},
  url = {https://arxiv.org/abs/2212.10233},
  author = {Wu, Di and Ahmad, Wasi Uddin and Chang, Kai-Wei},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study},
  publisher = {arXiv},
  year = {2022}, 
  copyright = {Creative Commons Attribution 4.0 International}
}
```

Pre-training Corpus: [S2ORC (titles and abstracts)](https://github.com/allenai/s2orc)

Pre-training Details:
- Pre-trained **from scratch** with a science vocabulary
- Batch size: 2048
- Total steps: 250k
- Learning rate: 3e-4
- LR schedule: polynomial with 10k warmup steps
- Masking ratio: 30%, Poisson lambda = 3.5
{"license": "mit"}
transformers pytorch bart feature-extraction arxiv:2212.10233 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #feature-extraction #arxiv-2212.10233 #license-mit #endpoints_compatible #region-us 


Note: please check DeepKPG for using this model in huggingface, including setting up the newly trained tokenizer.

Paper: Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study



Pre-training Corpus: S2ORC (titles and abstracts)

Pre-training Details:
- Pre-trained from scratch with a science vocabulary
- Batch size: 2048
- Total steps: 250k
- Learning rate: 3e-4
- LR schedule: polynomial with 10k warmup steps
- Masking ratio: 30%, Poisson lambda = 3.5
TAGS
#transformers #pytorch #bart #feature-extraction #arxiv-2212.10233 #license-mit #endpoints_compatible #region-us 
