Instructions to use nirantk/hinglish-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nirantk/hinglish-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nirantk/hinglish-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nirantk/hinglish-bert") model = AutoModelForMaskedLM.from_pretrained("nirantk/hinglish-bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7380a7b416229430f967800e050c0dba7a20df96f9d5aca63cee26ec84e376b
- Size of remote file:
- 1.26 kB
- SHA256:
- a69efafbf1ec72040528ee680913550d413151c4753e19d59a8ea76c921eed12
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