Token Classification
Transformers
Safetensors
Hebrew
bert
hebrew
rabbinic-hebrew
ocr-post-correction
error-detection
Instructions to use Sefaria/BEREL_3.0-hebrew-error-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sefaria/BEREL_3.0-hebrew-error-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Sefaria/BEREL_3.0-hebrew-error-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Sefaria/BEREL_3.0-hebrew-error-detector") model = AutoModelForTokenClassification.from_pretrained("Sefaria/BEREL_3.0-hebrew-error-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Yesterday
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v2: 512-piece context, Jerusalem Talmud excluded, trained on LLM-judged labels for uncorrected errors
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2 days ago
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BEREL 3.0 Hebrew error detector (sw0.5-pw10, 3 epochs)
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