Instructions to use MayBashendy/Arabic_CrossPrompt_FineTuningAraBERT_noAug_TestTask2_development with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayBashendy/Arabic_CrossPrompt_FineTuningAraBERT_noAug_TestTask2_development with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MayBashendy/Arabic_CrossPrompt_FineTuningAraBERT_noAug_TestTask2_development")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MayBashendy/Arabic_CrossPrompt_FineTuningAraBERT_noAug_TestTask2_development") model = AutoModelForSequenceClassification.from_pretrained("MayBashendy/Arabic_CrossPrompt_FineTuningAraBERT_noAug_TestTask2_development", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4dec4bbdec4b6fd5edc40bc5294952ea501408e2d282559abc3077df4ca2d57
- Size of remote file:
- 541 MB
- SHA256:
- 027266de0dc937336d5334b58f2cf6b4854a5c6e23b670a4f607333b0a0ebc5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.