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
File size: 1,451 Bytes
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"architectures": [
"DihyaForSequenceClassification"
],
"auto_map": {
"AutoConfig": "configuration_dihya.DihyaConfig",
"AutoModel": "modeling_dihya.DihyaForSequenceClassification",
"AutoModelForSequenceClassification": "modeling_dihya.DihyaForSequenceClassification"
},
"byte_offset": 2,
"classes": [
"kab_Latn",
"shi_Latn",
"rif_Latn",
"taq_Latn",
"tzm_Latn",
"shy_Latn",
"NOT_AMAZIGH"
],
"conv_dim": 128,
"conv_kernels": [
3,
5,
7
],
"dropout_prob": 0.1,
"hidden_size": 256,
"id2label": {
"0": "kab_Latn",
"1": "shi_Latn",
"2": "rif_Latn",
"3": "taq_Latn",
"4": "tzm_Latn",
"5": "shy_Latn",
"6": "NOT_AMAZIGH"
},
"intermediate_size": 704,
"label2id": {
"NOT_AMAZIGH": 6,
"kab_Latn": 0,
"rif_Latn": 2,
"shi_Latn": 1,
"shy_Latn": 5,
"taq_Latn": 3,
"tzm_Latn": 4
},
"logit_scale": 24.0,
"max_position_embeddings": 256,
"model_type": "dihya",
"num_attention_heads": 8,
"num_hidden_layers": 6,
"pad_token_id": 0,
"prior_shift": [
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-0.5855244994163513,
-1.1106044054031372,
-1.2838202714920044,
-2.21260666847229,
-2.5694897174835205,
-0.8174624443054199
],
"q_hat": 0.997931957244873,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"target_coverage": 0.99,
"transformers_version": "5.12.1",
"unk_token_id": 1,
"vocab_size": 258
}
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