Instructions to use meghanabhange/hinglish-sbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meghanabhange/hinglish-sbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="meghanabhange/hinglish-sbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("meghanabhange/hinglish-sbert") model = AutoModelForMaskedLM.from_pretrained("meghanabhange/hinglish-sbert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6dbb1bd2d7761bc83c2247d78d340e85955339172cabf5515c9b8c516cd27195
- Size of remote file:
- 438 MB
- SHA256:
- 3d4cc9b4aa328715e2d94dabfe9b7b3737c14c483a8c1a8bb69c3f563f407ce6
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