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 flax_model.msgpack from sagorsarker/codeswitch-nepeng-lid-lince: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/sagorsarker/codeswitch-nepeng-lid-lince/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sagorsarker/codeswitch-nepeng-lid-lince/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sagorsarker/codeswitch-nepeng-lid-lince/resolve/main/flax_model.msgpack
709 MB
- Xet hash:
- 6001189221499f5dd9ce4cb65c5e6898ae4e2a8b4c8e4c3460d8a6e6b1b6e0eb
- Size of remote file:
- 709 MB
- SHA256:
- 29b900a3f555b4bcc336390424a78cb4a4ccdf07a9313b73285436d9c6705487
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