Instructions to use Conrad747/luganda-ner-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Conrad747/luganda-ner-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Conrad747/luganda-ner-v3")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Conrad747/luganda-ner-v3") model = AutoModelForTokenClassification.from_pretrained("Conrad747/luganda-ner-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a4b73bc1f1bc9a69d014a68c32a055108be06a1e6e52e902a5ddc1fb1cd4c74
- Size of remote file:
- 2.24 GB
- SHA256:
- 0d10dd77b8e55ed86a7209c5bb3972f2a7d46302d40c4a2aaf434b47c897d9e2
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