Instructions to use sumanthbhargava/mt5-base-encoding-correction-10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumanthbhargava/mt5-base-encoding-correction-10k with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-10k") model = AutoModelForSeq2SeqLM.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-10k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac2767c2fd86eab51c1b8050796e70890f2f9d721c66e70a26138b602dfbbb3e
- Size of remote file:
- 5.46 kB
- SHA256:
- 32a451f2ab2d1899b0ce20242b90c4dc69d53245f6a60d71f3c5b40dc2d5cb0d
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