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
| { | |
| "source": "artifacts/runs/dihya/best.pt", | |
| "source_bytes": 63439095, | |
| "dropped_training_only": [ | |
| "head.margins", | |
| "projection.0.weight", | |
| "projection.2.weight" | |
| ], | |
| "tensors": 69, | |
| "parameters": 5089408, | |
| "max_logit_delta_against_trained": 0.0, | |
| "files": [ | |
| { | |
| "name": "README.md", | |
| "bytes": 15452, | |
| "sha256": "8d230fd4847f15a5f431bf369fcd6054baec703369b717b755fc939322da93be" | |
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| { | |
| "name": "__init__.py", | |
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| "sha256": "5c3bd891c199287302180ab2a447de121dc11ca69309da252741c612ea055d0e" | |
| }, | |
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| "name": "calibration.json", | |
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| "sha256": "72ab22b431e1887e4c1dbaed14f08455a0096cc6a544accc689007e05280f1eb" | |
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| "sha256": "d0cc1ff28fba5f9d61ca08e7e091cf7898338005ec3661bb1ad0d066d4cb581d" | |
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| "name": "configuration_dihya.py", | |
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| "sha256": "b92c21f6e356261c286440799d4e3a114ae13b2e3204153cd6a41259d97378d5" | |
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| "sha256": "edfbe7115393fca0a18474fcaac641afdbe69b66bc74d9381a5b54f38444ddd7" | |
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| } | |