Instructions to use Mahmoud22/AraClassificationModel2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud22/AraClassificationModel2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud22/AraClassificationModel2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud22/AraClassificationModel2") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud22/AraClassificationModel2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd047bb5404b49ece13c49b583edbee890e764a5378702e8f305a61cc5174da2
- Size of remote file:
- 541 MB
- SHA256:
- 10ee9bc707127c4d927e2a53146880da814877a15bf9ab9db7b1b6388fc6c4e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.