Instructions to use sumanthbhargava/byt5-base-encoding-correction-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumanthbhargava/byt5-base-encoding-correction-1k with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sumanthbhargava/byt5-base-encoding-correction-1k") model = AutoModelForSeq2SeqLM.from_pretrained("sumanthbhargava/byt5-base-encoding-correction-1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9644c653a4fe2f3d88648bba052f73454567f1dea5c3340fd93fa90fb4bda38e
- Size of remote file:
- 5.46 kB
- SHA256:
- 6fcdcde6e0300efdfd3a653bdcdb836115b625c798c642a7d6e7d139698c392b
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