Instructions to use benjamin/compoundpiece with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benjamin/compoundpiece with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("benjamin/compoundpiece") model = AutoModelForSeq2SeqLM.from_pretrained("benjamin/compoundpiece", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| language: | |
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| datasets: | |
| - benjamin/compoundpiece | |
| Compound normalization model from [CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models](https://arxiv.org/abs/2305.14214). | |
| ## Usage | |
| ``` | |
| from transformers import pipeline | |
| pipe = pipeline("text2text-generation", "benjamin/compoundpiece") | |
| pipe("Hauswirtschaftslehre", max_length=32) | |
| # [{'generated_text': 'Haus-Wirtschaft-Lehre'}] | |
| ``` | |
| # Citation | |
| ``` | |
| @article{minixhofer2023compoundpiece, | |
| title={CompoundPiece: Evaluating and Improving Decompounding Performance of Language Models}, | |
| author={Minixhofer, Benjamin and Pfeiffer, Jonas and Vuli{\'c}, Ivan}, | |
| journal={arXiv preprint arXiv:2305.14214}, | |
| year={2023} | |
| } | |
| ``` | |
| # License | |
| MIT |