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 training_args.bin from sagorsarker/codeswitch-nepeng-lid-lince: direct link, hf CLI and curl.
- Browser
- Download file 1.52 kB
-
https://huggingface.co/sagorsarker/codeswitch-nepeng-lid-lince/resolve/main/training_args.bin
- Command line
-
hf download hf://sagorsarker/codeswitch-nepeng-lid-lince/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sagorsarker/codeswitch-nepeng-lid-lince/resolve/main/training_args.bin
1.52 kB
- Xet hash:
- b3ce7f6c3662f7e98edcff24ebdec2bdaf4d942c6dacddba79dab980e0780994
- Size of remote file:
- 1.52 kB
- SHA256:
- a01f5819b7e170c07bfd327eb49b88300a30408b0c4cefff861ff156f58c027f
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