Token Classification
Transformers
Safetensors
Arabic
bert
hadith
sanad
matn
hadith-separator
hadith_separator
islam
hadithBERT
Instructions to use SHK4K/hadith-segmentation-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SHK4K/hadith-segmentation-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SHK4K/hadith-segmentation-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SHK4K/hadith-segmentation-bert") model = AutoModelForTokenClassification.from_pretrained("SHK4K/hadith-segmentation-bert") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": false, | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "max_len": 512, | |
| "model_max_length": 512, | |
| "never_split": [ | |
| "[بريد]", | |
| "[مستخدم]", | |
| "[رابط]" | |
| ], | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |