Instructions to use l3cube-pune/malayalam-bert-scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/malayalam-bert-scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/malayalam-bert-scratch")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/malayalam-bert-scratch") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/malayalam-bert-scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a92d8f42d46d1ecd4781af3969be7c86655b3ca701c994897ac5b358fa97b74b
- Size of remote file:
- 504 MB
- SHA256:
- d56f35ef7e7b0867dd4049c9ad66bbcbb658b2166648690fc6e429e2037a38e5
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