Text Classification
Transformers
Safetensors
Arabic
xlm-roberta
semantic-highlighting
arabic
sentence-relevance
rag
reranker
Eval Results (legacy)
text-embeddings-inference
Instructions to use HeshamHaroon/arabic-semantic-highlighter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HeshamHaroon/arabic-semantic-highlighter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HeshamHaroon/arabic-semantic-highlighter")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HeshamHaroon/arabic-semantic-highlighter") model = AutoModelForSequenceClassification.from_pretrained("HeshamHaroon/arabic-semantic-highlighter") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf49ce6af52b15db4d575c972f011c1a90de213c47df18035b8f2fd4ed1b1e7c
- Size of remote file:
- 17.1 MB
- SHA256:
- 14917dd757b81bc44d4af6b028367351702656670c1954e055dabdfcf21593cf
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