Text Classification
Transformers
PyTorch
Arabic
deberta-v2
subjectivity
newspapers
CLEF2023
text-embeddings-inference
Instructions to use GroNLP/mdebertav3-subjectivity-arabic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GroNLP/mdebertav3-subjectivity-arabic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="GroNLP/mdebertav3-subjectivity-arabic")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("GroNLP/mdebertav3-subjectivity-arabic") model = AutoModelForSequenceClassification.from_pretrained("GroNLP/mdebertav3-subjectivity-arabic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41491c50d9882547b47c6aa808706d6d4633002b852a86cd3ccce75fbd5e9a9c
- Size of remote file:
- 1.11 GB
- SHA256:
- 6a196ffbcfe32b9dc01cc34638d03fa86fbf4f7c0c66fc0a915258f76c013cef
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