Instructions to use Narasimha/hinglish-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narasimha/hinglish-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Narasimha/hinglish-distilbert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Narasimha/hinglish-distilbert") model = AutoModelForSequenceClassification.from_pretrained("Narasimha/hinglish-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fab41bff8cf9dfce4ba596e69e3f2585f631b2abc69564e9251058e1901e56b6
- Size of remote file:
- 263 MB
- SHA256:
- 99e7db511627667ef5db5b611c4f523d276804ab4d7c006f75db32d0ba9ba045
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.