Instructions to use abdullah0kq/ARACD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abdullah0kq/ARACD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="abdullah0kq/ARACD")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("abdullah0kq/ARACD") model = AutoModelForQuestionAnswering.from_pretrained("abdullah0kq/ARACD") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("abdullah0kq/ARACD")
model = AutoModelForQuestionAnswering.from_pretrained("abdullah0kq/ARACD")Quick Links
ARACD
This model is a fine-tuned version of aubmindlab/bert-base-arabertv2 on an Arabic-SQuAD-v2.0 dataset. It achieves the following results on the evaluation set:
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Model tree for abdullah0kq/ARACD
Base model
aubmindlab/bert-base-arabertv2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="abdullah0kq/ARACD")