Text Classification
Transformers
Safetensors
Arabic
bert
sentiment
arabic
classification
text-embeddings-inference
Instructions to use Nadasr/sentAnalysisModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nadasr/sentAnalysisModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nadasr/sentAnalysisModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nadasr/sentAnalysisModel") model = AutoModelForSequenceClassification.from_pretrained("Nadasr/sentAnalysisModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1be33567ff769cfa4a240349b6d43f1a216121b0e76306c47e8383f005147a6
- Size of remote file:
- 436 MB
- SHA256:
- 114d29c82707ce32b1ac682ab7cf80d62b01599d953cd2cc624169308ae14630
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