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")# 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
- Xet hash:
- e6609497486af49ebe40467f7d9ea521022f4b9e0a0249ae72b00f8ba2296db4
- Size of remote file:
- 332 MB
- SHA256:
- 243160ef340771b129c94f3e0daee346d7be64dbf7efe63df68bb8ef1dcf356e
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