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
v2: 512-piece context, Jerusalem Talmud excluded, trained on LLM-judged labels for uncorrected errors
f34432f verified Download error_detection_config.json from Sefaria/BEREL_3.0-hebrew-error-detector: direct link, hf CLI and curl.
- Browser
- Download file 562 Bytes
-
https://huggingface.co/Sefaria/BEREL_3.0-hebrew-error-detector/resolve/main/error_detection_config.json
- Command line
-
hf download hf://Sefaria/BEREL_3.0-hebrew-error-detector/error_detection_config.json
-
curl -L -o error_detection_config.json https://huggingface.co/Sefaria/BEREL_3.0-hebrew-error-detector/resolve/main/error_detection_config.json
562 Bytes
| { | |
| "base_model": "dicta-il/BEREL_3.0", | |
| "dataset_repo": "Sefaria/hebrew-text-corrections (private), judged labels", | |
| "max_length": 512, | |
| "pos_weight": 10.0, | |
| "synthetic_weight": 0.5, | |
| "paired_negatives": true, | |
| "negative_weight": 1.0, | |
| "label_scope": "all", | |
| "n_folds": 0, | |
| "fold": 0, | |
| "validation_threshold": 0.536, | |
| "best_step": 5500, | |
| "training_validation_threshold": 0.7833262085914612, | |
| "threshold_note": "best F1 on the validation split with judged labels (evaluate-error-detector); training_validation_threshold was chosen during training" | |
| } |