Instructions to use l3cube-pune/hindi-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hindi-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hindi-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-roberta") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hindi-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4dc76773224c50fea346f0910a831f4331406d4b2cdc2d940d8a02f229e51ad
- Size of remote file:
- 1.11 GB
- SHA256:
- dd3102aeb6d8e196a34aca5af2f3a39f7be14e1a53c77efb040b725b5438c6ab
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