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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cbb207e14ba14b96b6555f79145c64404c0bf5dfd560b0be35a0f7dfa542de9f
- Size of remote file:
- 714 MB
- SHA256:
- ea89e8be02ac6f1b8c6cca48f49e819d30ced48a878ac0673b96d07adc58ffed
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