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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from murali1996/bert-base-cased-spell-correction: direct link, hf CLI and curl.
- Browser
- Download file 433 MB
-
https://huggingface.co/murali1996/bert-base-cased-spell-correction/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://murali1996/bert-base-cased-spell-correction/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/murali1996/bert-base-cased-spell-correction/resolve/main/flax_model.msgpack
433 MB
- Xet hash:
- 5a6c8bfe47c260d51c0c2cb23b5edb958a8d62ed8ab6fcfce0919caa7601fb68
- Size of remote file:
- 433 MB
- SHA256:
- cf6eb35e4d10d8187a77f83a1a1451215602b9585cfca268a3090f91b06d8308
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.