Text Classification
Transformers
Safetensors
dihya
feature-extraction
berber
amazigh
kabyle
tashelhit
tarifit
tamasheq
tamazight
shawiya
language-identification
conformal-prediction
low-resource
custom_code
Eval Results (legacy)
Instructions to use agbalu/Dihya-5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agbalu/Dihya-5M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="agbalu/Dihya-5M", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("agbalu/Dihya-5M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "model_max_length": 256, | |
| "pad_token": "[PAD]", | |
| "tokenizer_class": "TokenizersBackend", | |
| "unk_token": "[UNK]" | |
| } | |