Instructions to use l3cube-pune/hindi-bert-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use l3cube-pune/hindi-bert-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="l3cube-pune/hindi-bert-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("l3cube-pune/hindi-bert-v2") model = AutoModelForMaskedLM.from_pretrained("l3cube-pune/hindi-bert-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 503e79f28aee5ba716da1962e72a0cca3897ecde3829d49635e6d25f0f4491c4
- Size of remote file:
- 951 MB
- SHA256:
- 68ceae133cf6465e30b8e182af97bf993b7ece94664c3311c727b1e8a4f55479
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