Upload best k-fold model (Fold 4, F1: 0.9102)

README.md

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+---
+language:
+- he
+license: mit
+tags:
+- named-entity-recognition
+- token-classification
+- hebrew
+- historical-manuscripts
+- joint-learning
+- multi-task-learning
+- k-fold-validation
+datasets:
+- custom
+metrics:
+- f1
+- accuracy
+library_name: transformers
+pipeline_tag: token-classification
+---
+
+# Hebrew Manuscript Joint NER Model v2
+
+## Model Description
+
+This model performs **joint entity recognition and role classification** for Hebrew historical manuscripts. It simultaneously:
+1. **Named Entity Recognition (NER)**: Identifies person names in Hebrew text
+2. **Role Classification**: Classifies each person as Author, Copyist, or Other
+
+**Key Features:**
+- Multi-task learning architecture
+- Trained on Hebrew manuscript catalog data (MARC records)
+- Uses distant supervision from structured metadata
+- Optimized for historical Hebrew text
+- Robust k-fold cross-validation
+
+
+## K-Fold Cross-Validation Results
+
+This model was trained using **5-fold cross-validation** for robust evaluation.
+
+### Aggregate Performance
+
+| Metric | Mean | Std Dev | Min | Max |
+|--------|------|---------|-----|-----|
+| **NER F1** | **0.9080** | ±0.0035 | 0.9011 | 0.9102 |
+| **Classification Accuracy** | **1.0000** | ±0.0000 | - | - |
+
+### Per-Fold Results
+
+- **Fold 1**: NER F1 = 0.9011, Class Acc = 1.0000
+- **Fold 2**: NER F1 = 0.9089, Class Acc = 1.0000
+- **Fold 3**: NER F1 = 0.9096, Class Acc = 1.0000
+- **Fold 4**: NER F1 = 0.9102, Class Acc = 1.0000
+- **Fold 5**: NER F1 = 0.9100, Class Acc = 1.0000
+
+**Best Model**: Fold 4 (NER F1: 0.9102)
+
+
+## Model Architecture
+
+- **Base Model**: [dicta-il/dictabert](https://huggingface.co/dicta-il/dictabert)
+- **Architecture**: Joint multi-task learning
+  - NER head: Token classification (B-PERSON, I-PERSON, O)
+  - Role classification head: Sequence classification (AUTHOR, COPYIST, OTHER)
+- **Training**: 5-fold cross-validation with early stopping
+- **Regularization**: Dropout (0.3), Weight decay (0.01)
+
+## Intended Use
+
+### Primary Use Cases
+- Extracting person names from Hebrew manuscript descriptions
+- Identifying roles of people mentioned in manuscripts
+- Building knowledge graphs of Hebrew manuscript creators
+- Digital humanities research on Hebrew manuscripts
+
+### Example Usage
+
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("alexgoldberg/hebrew-manuscript-joint-ner-v2")
+model = AutoModel.from_pretrained("alexgoldberg/hebrew-manuscript-joint-ner-v2")
+
+# Example text (Hebrew)
+text = "הספר נכתב על ידי רבי משה בן מימון"
+tokens = tokenizer(text, return_tensors="pt")
+
+# Get predictions
+with torch.no_grad():
+    outputs = model(**tokens)
+    # outputs contains both NER and role classification logits
+```
+
+## Training Data
+
+- **Source**: Hebrew manuscript catalog records (MARC format)
+- **Size**: ~10,000 samples
+- **Annotation**: Distant supervision from structured metadata fields
+- **Languages**: Hebrew (historical and modern)
+- **Domain**: Manuscript descriptions, colophons, catalog records
+
+## Training Procedure
+
+### Hyperparameters
+- **Epochs**: 10 (with early stopping, patience=3)
+- **Batch Size**: 4
+- **Learning Rate**: 2e-5
+- **Optimizer**: AdamW
+- **Dropout**: 0.3
+- **Weight Decay**: 0.01
+- **Lambda Weight**: 0.5 (for multi-task loss balancing)
+
+### Data Split
+- **K-Fold**: 5-fold stratified cross-validation
+- **Stratification**: By number of persons per sample
+- **Train/Val per fold**: 90/10 split
+
+## Evaluation
+
+### Metrics
+- **NER**: Precision, Recall, F1 (seqeval)
+- **Classification**: Accuracy
+- **Combined**: Geometric mean of NER F1 and Classification Accuracy
+
+### Validation Strategy
+5-fold cross-validation ensures robust performance estimates and reduces overfitting to a single train/test split.
+
+## Limitations
+
+- Optimized for Hebrew manuscript descriptions (may not generalize to other Hebrew text types)
+- Person names must follow historical Hebrew naming conventions
+- Limited to three role categories (Author, Copyist, Other)
+- Trained on catalog data (may not work well on manuscript images/OCR)
+
+## Ethical Considerations
+
+- Model trained on historical cultural heritage data
+- Should be used to assist, not replace, expert manuscript catalogers
+- Potential biases from historical naming conventions and catalog practices
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{hebrew-manuscript-joint-ner-v2,
+  author = {Goldberg, Alexander},
+  title = {Hebrew Manuscript Joint NER Model v2},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/alexgoldberg/hebrew-manuscript-joint-ner-v2}}
+}
+```
+
+## Model Card Authors
+
+Alexander Goldberg
+
+## Model Card Contact
+
+For questions or issues, please open an issue on the model repository.

kfold_results.json

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+{
+  "n_folds": 5,
+  "ner_f1_mean": 0.9079608298355216,
+  "ner_f1_std": 0.0034783587137207097,
+  "ner_f1_min": 0.9010667224430035,
+  "ner_f1_max": 0.9102049440101416,
+  "class_acc_mean": 1.0,
+  "class_acc_std": 0.0,
+  "per_fold": [
+    {
+      "fold": 1,
+      "ner_f1": 0.9010667224430035,
+      "class_acc": 1.0
+    },
+    {
+      "fold": 2,
+      "ner_f1": 0.9088618227635447,
+      "class_acc": 1.0
+    },
+    {
+      "fold": 3,
+      "ner_f1": 0.9096222083072428,
+      "class_acc": 1.0
+    },
+    {
+      "fold": 4,
+      "ner_f1": 0.9102049440101416,
+      "class_acc": 1.0
+    },
+    {
+      "fold": 5,
+      "ner_f1": 0.9100484516536759,
+      "class_acc": 1.0
+    }
+  ]
+}

pytorch_model.bin

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