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
Download flax_model.msgpack from nirantk/hinglish-bert: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/nirantk/hinglish-bert/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://nirantk/hinglish-bert/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/nirantk/hinglish-bert/resolve/main/flax_model.msgpack
712 MB
- Xet hash:
- 430677c04230a4fb688105c7db446e4dabfae7857a885fd5a4bda58807b18255
- Size of remote file:
- 712 MB
- SHA256:
- ce41025487e441b96819ef547504525ec714784ec5cc262b1d5509cbfad92b68
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.