Instructions to use sumanthbhargava/mt5-base-encoding-correction-1k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sumanthbhargava/mt5-base-encoding-correction-1k with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-1k") model = AutoModelForSeq2SeqLM.from_pretrained("sumanthbhargava/mt5-base-encoding-correction-1k", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d43321eccf5c9af80ff2b2f7c00278d8c6271f15ada71676f859b674acb66374
- Size of remote file:
- 5.46 kB
- SHA256:
- 557f9438e020e63a6aee6f0241f6b31c470d790f781f3eedfe04f8e06b5db099
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