Token Classification
Transformers
PyTorch
JAX
Safetensors
Nepali
English
multilingual
bert
codeswitching
nepali-english
language-identification
Instructions to use sagorsarker/codeswitch-nepeng-lid-lince with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagorsarker/codeswitch-nepeng-lid-lince with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="sagorsarker/codeswitch-nepeng-lid-lince")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-nepeng-lid-lince") model = AutoModelForTokenClassification.from_pretrained("sagorsarker/codeswitch-nepeng-lid-lince", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sagorsarker/codeswitch-nepeng-lid-lince: direct link, hf CLI and curl.
- Browser
- Download file 712 MB
-
https://huggingface.co/sagorsarker/codeswitch-nepeng-lid-lince/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sagorsarker/codeswitch-nepeng-lid-lince/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sagorsarker/codeswitch-nepeng-lid-lince/resolve/main/pytorch_model.bin
712 MB
- Xet hash:
- 835d056094eb816ccee81d1588a522c7d3a48097541bbafc5f87dd1d3f33dfb8
- Size of remote file:
- 712 MB
- SHA256:
- 8af53c74be59c1abad5d6ab64c5beef37427303e0996d2b593fff9911d99cddc
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