Instructions to use mrm8488/HindiBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/HindiBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mrm8488/HindiBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mrm8488/HindiBERTa") model = AutoModelForMaskedLM.from_pretrained("mrm8488/HindiBERTa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 368b8bbc317ee6199062fc2be7987248b4ee3dc624ea7923c539d9fcd11c2591
- Size of remote file:
- 334 MB
- SHA256:
- 2b825127a6ee6440f9fe6dd0f6b30d7ef604058acb1ec6841d4580e03d579775
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