Instructions to use mselbach/distilbart-rehadat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mselbach/distilbart-rehadat with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mselbach/distilbart-rehadat") model = AutoModelForSeq2SeqLM.from_pretrained("mselbach/distilbart-rehadat") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8934e54cf0f359b9e03431cad657515e408864ce443987ffff40ecb25b1a56d
- Size of remote file:
- 1.22 GB
- SHA256:
- 0a7fcd8780ffd80224ab3cd9712645059ec1616ee143b2f2bc794efbbcc8a757
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