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:
- 822a1ccfa701a6f90868c4af234cd46f7b6f3a9c5f33664e06a81485a80f58ff
- Size of remote file:
- 5.84 kB
- SHA256:
- 840f5d79e36cc16029e2ae3f19a50832fde3c2befc940b7dd13bd29e44560819
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.