Text Classification
Transformers
Safetensors
Hebrew
bert
intent-classification
hebrew
alephbert
shopping
text-embeddings-inference
Instructions to use spivi87/alephbert-intent-he with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spivi87/alephbert-intent-he with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spivi87/alephbert-intent-he")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("spivi87/alephbert-intent-he") model = AutoModelForSequenceClassification.from_pretrained("spivi87/alephbert-intent-he", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Link the AlephBERT repo in the credits
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Retrain (3 seeds) with single-item coverage; fix add-vs-remove confusion
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Retrain on augmented data; fix buy-verb (תקנה) requests
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Simplify the evaluation report
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Rewrite the model card as a beginner-friendly tutorial
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docs(ALE-484): rich eval report with baselines + multi-run variance
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docs(ALE-484): full model card with baselines comparison + 3-run variance + bias/dialect section
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feat(ALE-483): retrained on seed-level split + full model card
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feat: initial weight + tokenizer + eval upload
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docs: placeholder model card with YAML metadata
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initial commit
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