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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a9f79341e59a7f49fb1ea3af37914ec4875ab847390b657b7e9f6bf51edc938
- Size of remote file:
- 16.3 MB
- SHA256:
- 65c2d7defb6472fada8a935bb364ae3433f7451780c8a59ab6b3cfbaadb32608
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.