Instructions to use pain/ArSL_AlphabetsClassificationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pain/ArSL_AlphabetsClassificationModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pain/ArSL_AlphabetsClassificationModel") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pain/ArSL_AlphabetsClassificationModel", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 709 Bytes
733ef1c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"architectures": [
"ResNetForImageClassification"
],
"id2label": {
"0": "Ain",
"1": "Al",
"2": "Alef",
"3": "Beh",
"4": "Dad",
"5": "Dal",
"6": "Feh",
"7": "Ghain",
"8": "Hah",
"9": "Heh",
"10": "Jeem",
"11": "Kaf",
"12": "Khah",
"13": "Laa",
"14": "Lam",
"15": "Meem",
"16": "Noon",
"17": "Qaf",
"18": "Reh",
"19": "Sad",
"20": "Seen",
"21": "Sheen",
"22": "Tah",
"23": "Teh",
"24": "Teh_Marbuta",
"25": "Theh",
"26": "Waw",
"27": "Yeh",
"28": "Zah",
"29": "Zain",
"30": "thal",
},
"model_type": "resnet",
"num_channels": 3,
"torch_dtype": "float32",
"transformers_version": "4.18.0.dev0"
}
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