Token Classification
Transformers
Safetensors
mt5
named-entity-recognition
lumasaba
african-language
pii-detection
Generated from Trainer
Eval Results (legacy)
Instructions to use Beijuka/mt5-base-lumasaba-ner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Beijuka/mt5-base-lumasaba-ner-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Beijuka/mt5-base-lumasaba-ner-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Beijuka/mt5-base-lumasaba-ner-v1") model = AutoModelForTokenClassification.from_pretrained("Beijuka/mt5-base-lumasaba-ner-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09be218fa92e8e93a687e5bc0773f9310aa12a7b9ec7914c8ebabe2f61849a57
- Size of remote file:
- 5.84 kB
- SHA256:
- 4c41986514b2c45206eb39b837c9c464cfa77038fbca08bbc148f3fb093f7ae0
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