Token Classification
Transformers
Safetensors
Malay
Arabic
bert
arabic
tashkeel
diacritization
bahasa-melayu
arabic-nlp
pondok-melawati
Instructions to use melawati/pondok-melawati-tashkeel-utama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use melawati/pondok-melawati-tashkeel-utama with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="melawati/pondok-melawati-tashkeel-utama")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("melawati/pondok-melawati-tashkeel-utama") model = AutoModelForTokenClassification.from_pretrained("melawati/pondok-melawati-tashkeel-utama", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,092 Bytes
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"_name_or_path": "koleksi-sorof/tashkeel-utama",
"architectures": [
"BertForTokenClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"finetuning_task": "ner",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "X",
"1": "\u062a\u0637\u0648\u064a\u0644",
"2": "\u0633\u0643\u0648\u0646",
"3": "\u0634\u062f\u0629",
"4": "\u0634\u062f\u0629 \u0636\u0645\u0629",
"5": "\u0634\u062f\u0629 \u0636\u0645\u062a\u0627\u0646",
"6": "\u0634\u062f\u0629 \u0641\u062a\u062d\u0629",
"7": "\u0634\u062f\u0629 \u0641\u062a\u062d\u062a\u0627\u0646",
"8": "\u0634\u062f\u0629 \u0643\u0633\u0631\u0629",
"9": "\u0634\u062f\u0629 \u0643\u0633\u0631\u062a\u0627\u0646",
"10": "\u0636\u0645\u0629",
"11": "\u0636\u0645\u062a\u0627\u0646",
"12": "\u0641\u062a\u062d\u0629",
"13": "\u0641\u062a\u062d\u062a\u0627\u0646",
"14": "\u0643\u0633\u0631\u0629",
"15": "\u0643\u0633\u0631\u062a\u0627\u0646"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"X": 0,
"\u062a\u0637\u0648\u064a\u0644": 1,
"\u0633\u0643\u0648\u0646": 2,
"\u0634\u062f\u0629": 3,
"\u0634\u062f\u0629 \u0636\u0645\u0629": 4,
"\u0634\u062f\u0629 \u0636\u0645\u062a\u0627\u0646": 5,
"\u0634\u062f\u0629 \u0641\u062a\u062d\u0629": 6,
"\u0634\u062f\u0629 \u0641\u062a\u062d\u062a\u0627\u0646": 7,
"\u0634\u062f\u0629 \u0643\u0633\u0631\u0629": 8,
"\u0634\u062f\u0629 \u0643\u0633\u0631\u062a\u0627\u0646": 9,
"\u0636\u0645\u0629": 10,
"\u0636\u0645\u062a\u0627\u0646": 11,
"\u0641\u062a\u062d\u0629": 12,
"\u0641\u062a\u062d\u062a\u0627\u0646": 13,
"\u0643\u0633\u0631\u0629": 14,
"\u0643\u0633\u0631\u062a\u0627\u0646": 15
},
"label_descriptions_ms": {
"X": "Tiada label / abaikan",
"\u062a\u0637\u0648\u064a\u0644": "Tatwil",
"\u0633\u0643\u0648\u0646": "Sukun",
"\u0634\u062f\u0629": "Syaddah",
"\u0634\u062f\u0629 \u0636\u0645\u0629": "Syaddah + dammah",
"\u0634\u062f\u0629 \u0636\u0645\u062a\u0627\u0646": "Syaddah + dammatan",
"\u0634\u062f\u0629 \u0641\u062a\u062d\u0629": "Syaddah + fathah",
"\u0634\u062f\u0629 \u0641\u062a\u062d\u062a\u0627\u0646": "Syaddah + fathatan",
"\u0634\u062f\u0629 \u0643\u0633\u0631\u0629": "Syaddah + kasrah",
"\u0634\u062f\u0629 \u0643\u0633\u0631\u062a\u0627\u0646": "Syaddah + kasratan",
"\u0636\u0645\u0629": "Dammah",
"\u0636\u0645\u062a\u0627\u0646": "Dammatan",
"\u0641\u062a\u062d\u0629": "Fathah",
"\u0641\u062a\u062d\u062a\u0627\u0646": "Fathatan",
"\u0643\u0633\u0631\u0629": "Kasrah",
"\u0643\u0633\u0631\u062a\u0627\u0646": "Kasratan"
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"torch_dtype": "float32",
"transformers_version": "4.38.1",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 64000
}
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