Instructions to use Moazamzf/code-switching-si26-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Moazamzf/code-switching-si26-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Moazamzf/code-switching-si26-model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Moazamzf/code-switching-si26-model") model = AutoModelForTokenClassification.from_pretrained("Moazamzf/code-switching-si26-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Moazamzf/code-switching-si26-model: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Moazamzf/code-switching-si26-model/resolve/main/tokenizer.json
- Command line
-
hf download hf://Moazamzf/code-switching-si26-model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Moazamzf/code-switching-si26-model/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 225257d321afc4310eaa385dcde0200f73167a55fa970ecc618c8009bd008161
- Size of remote file:
- 17.1 MB
- SHA256:
- d1065c14bf5f3d26a369bb348eae791e137f4b69cc85f846cc14bde0e6c49aca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.