Instructions to use l3cube-pune/marathi-tweets-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/marathi-tweets-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/marathi-tweets-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/marathi-tweets-bert") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/marathi-tweets-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7b8b40fc29158f0251f76ffecd07579f20af529f33e3179400fc04a4421e4bec
- Size of remote file:
- 951 MB
- SHA256:
- 7f7c5c88ccd932c5ef50ca195417a24c55e05183788c440e6e0b278f92d2e13d
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