Token Classification
Transformers
Safetensors
deberta-v2
named-entity-recognition
hausa
african-language
pii-detection
Generated from Trainer
Eval Results (legacy)
Instructions to use Beijuka/deberta-v3-base-hausa-ner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Beijuka/deberta-v3-base-hausa-ner-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Beijuka/deberta-v3-base-hausa-ner-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Beijuka/deberta-v3-base-hausa-ner-v1") model = AutoModelForTokenClassification.from_pretrained("Beijuka/deberta-v3-base-hausa-ner-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d79a9820a97b11df8dc46074958d1ead920e669cbac62d328635c8bce08da067
- Size of remote file:
- 735 MB
- SHA256:
- fc81f4c94b38ee213add6a4d842455b0057a7e5229b31c5d3a8e11893dd7b222
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