Instructions to use l3cube-pune/marathi-albert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-albert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/marathi-albert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-albert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/marathi-albert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b6420fe4c57cd7d814fd8a7f77a69250a08b46b16aa5306c66ab5b115c228fa
- Size of remote file:
- 45.9 MB
- SHA256:
- 4b4a694e962a7b2fd9ae33d659ea421324991d3e47bce639606a6082fa365f6b
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