Text Classification
Transformers
Safetensors
Arabic
xlm-roberta
arabic
iraqi-dialect
msa
message-classification
fine-tuned
Eval Results (legacy)
text-embeddings-inference
Instructions to use ahmedmajid92/Arabic_MI_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmedmajid92/Arabic_MI_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ahmedmajid92/Arabic_MI_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ahmedmajid92/Arabic_MI_Classifier") model = AutoModelForSequenceClassification.from_pretrained("ahmedmajid92/Arabic_MI_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1ec70b9eae6e35493692c2536700eed2095695150aabb2b8b184194cf5bff2f
- Size of remote file:
- 17.1 MB
- SHA256:
- 3c088c06cf975b7097e469bd69630cdb0d675c6db1ce3af1042b6e19c6d01f22
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.