Instructions to use Llamacha/pos_quechua with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Llamacha/pos_quechua with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Llamacha/pos_quechua")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Llamacha/pos_quechua") model = AutoModelForTokenClassification.from_pretrained("Llamacha/pos_quechua", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Llamacha/pos_quechua: direct link, hf CLI and curl.
- Browser
- Download file 2.25 MB
-
https://huggingface.co/Llamacha/pos_quechua/resolve/main/tokenizer.json
- Command line
-
hf download hf://Llamacha/pos_quechua/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/Llamacha/pos_quechua/resolve/main/tokenizer.json
2.25 MB
File too large to display, you can check the raw version instead.