Text Classification
Transformers
PyTorch
Safetensors
Arabic
Egyptian Arabic
Moroccan Arabic
bert
text-embeddings-inference
Instructions to use IbrahimAmin/marbertv2-arabic-written-dialect-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IbrahimAmin/marbertv2-arabic-written-dialect-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IbrahimAmin/marbertv2-arabic-written-dialect-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IbrahimAmin/marbertv2-arabic-written-dialect-classifier") model = AutoModelForSequenceClassification.from_pretrained("IbrahimAmin/marbertv2-arabic-written-dialect-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5435c8672aff84c93760563f71058a7beb41eb86dee4d5705ba18c7bccc235bb
- Size of remote file:
- 651 MB
- SHA256:
- 9d49ede53f8ae0fcd10c8a81012238d8f05e3785ddcacdc1476d0ed583d569ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.