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") - Notebooks
- Google Colab
- Kaggle
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- ACE2005
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# Arabic NER Model
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- NER BIO tagging model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English).
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- ACE2005 Training data: English + Arabic
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- [NER tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) including: PER, VEH, GPE, WEA, ORG, LOC, FAC
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- ACE2005
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# Arabic NER Model
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- [Github repo](https://github.com/edchengg/GigaBERT)
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- NER BIO tagging model based on [GigaBERTv4](https://huggingface.co/lanwuwei/GigaBERT-v4-Arabic-and-English).
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- ACE2005 Training data: English + Arabic
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- [NER tags](https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/english-entities-guidelines-v6.6.pdf) including: PER, VEH, GPE, WEA, ORG, LOC, FAC
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