Token Classification
Transformers
Safetensors
mt5
named-entity-recognition
luganda
african-language
pii-detection
Generated from Trainer
Eval Results (legacy)
Instructions to use Beijuka/mt5-base-luganda-ner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Beijuka/mt5-base-luganda-ner-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Beijuka/mt5-base-luganda-ner-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Beijuka/mt5-base-luganda-ner-v1") model = AutoModelForTokenClassification.from_pretrained("Beijuka/mt5-base-luganda-ner-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 706ef0553226e39a5eeaef8646db3782db68ca6ed184b22edc29cb68ed6f7d31
- Size of remote file:
- 5.78 kB
- SHA256:
- 48f59c0acdfae445b2ae3acf38c01acec47ed6fad70370a46a7f5a6381c39f0e
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