Instructions to use sumanthbhargava/mt5-base-encoding-correction-100k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumanthbhargava/mt5-base-encoding-correction-100k with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-100k") model = AutoModelForSeq2SeqLM.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-100k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6a1e5678faef642c6ee051a29706901b9153bd2403bd4415d32ff28ec5b3942e
- Size of remote file:
- 5.46 kB
- SHA256:
- 9466a4ba61e0a5e214753ef22fd87b7ce290efa3ac706eb5ea76e6f8c3d83a86
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