Instructions to use verloop/Hinglish-DistilBert-Class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use verloop/Hinglish-DistilBert-Class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="verloop/Hinglish-DistilBert-Class")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("verloop/Hinglish-DistilBert-Class") model = AutoModelForSequenceClassification.from_pretrained("verloop/Hinglish-DistilBert-Class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from verloop/Hinglish-DistilBert-Class: direct link, hf CLI and curl.
- Browser
- Download file 263 MB
-
https://huggingface.co/verloop/Hinglish-DistilBert-Class/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://verloop/Hinglish-DistilBert-Class/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/verloop/Hinglish-DistilBert-Class/resolve/main/pytorch_model.bin
263 MB
- Xet hash:
- 9f81039fac27f2bdb61181257f452f04f0752d3c50ed235558c2941e12bdc17a
- Size of remote file:
- 263 MB
- SHA256:
- b9cfcb4294f81284e080c718942f1b348add17b7619e7fff74bcf0b23cc004a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.