Token Classification
Transformers
PyTorch
TensorFlow
Arabic
English
bert
text-classification
BERT
sequence-tagger-model
Instructions to use ychenNLP/arabic-ner-ace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ychenNLP/arabic-ner-ace with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ychenNLP/arabic-ner-ace")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ychenNLP/arabic-ner-ace") model = AutoModelForSequenceClassification.from_pretrained("ychenNLP/arabic-ner-ace", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "mask_token": "[MASK]", | |
| "name_or_path": "lanwuwei/GigaBERT-v4-Arabic-and-English", | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "special_tokens_map_file": "/srv/share5/ychen3411/huggingface_cache/c8a919731ad7551df88a744a8399190ea80a9961eaf96b8a59f86ef617e5144f.6a644b330fad284f98393c5832f71bce43df6855fa6ac7c9e44ed6271b708170", | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "unk_token": "[UNK]" | |
| } | |