Token Classification
Transformers
TensorBoard
Arabic
bert
fill-mask
Named Entity Recognition
Arabic NER
Nested NER
Instructions to use SinaLab/ArabicNER-Wojood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SinaLab/ArabicNER-Wojood with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SinaLab/ArabicNER-Wojood")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SinaLab/ArabicNER-Wojood") model = AutoModelForMaskedLM.from_pretrained("SinaLab/ArabicNER-Wojood", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": false, "max_len": 512, "do_basic_tokenize": true, "never_split": ["+ู", "+ูู ุง", "ู+", "+ูุง", "+ูู", "ู+", "+ูู", "+ุงู", "+ูู ", "+ุฉ", "[ุจุฑูุฏ]", "ูู+", "+ู", "+ุช", "+ู", "ุณ+", "ู+", "[ู ุณุชุฎุฏู ]", "+ูู ", "+ุง", "ุจ+", "ู+", "+ูุง", "+ูุง", "+ูู", "+ูู ุง", "ุงู+", "+ู", "+ูู", "+ุงุช", "[ุฑุงุจุท]"], "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "aubmindlab/bert-large-arabertv2"} |