Instructions to use murali1996/bert-base-cased-spell-correction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use murali1996/bert-base-cased-spell-correction with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="murali1996/bert-base-cased-spell-correction")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("murali1996/bert-base-cased-spell-correction") model = AutoModel.from_pretrained("murali1996/bert-base-cased-spell-correction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5a6c8bfe47c260d51c0c2cb23b5edb958a8d62ed8ab6fcfce0919caa7601fb68
- Size of remote file:
- 433 MB
- SHA256:
- cf6eb35e4d10d8187a77f83a1a1451215602b9585cfca268a3090f91b06d8308
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