Token Classification
Transformers
Safetensors
Hebrew
English
bert
named-entity-recognition
hebrew-manuscripts
marc
role-classification
Instructions to use alexgoldberg/hebrew-manuscript-joint-ner-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alexgoldberg/hebrew-manuscript-joint-ner-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="alexgoldberg/hebrew-manuscript-joint-ner-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("alexgoldberg/hebrew-manuscript-joint-ner-v2") model = AutoModelForTokenClassification.from_pretrained("alexgoldberg/hebrew-manuscript-joint-ner-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "experiment": "person_role_v3", | |
| "base_model": "dicta-il/dictabert", | |
| "max_length": 256, | |
| "batch_size": 8, | |
| "gradient_accumulation_steps": 1, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "dropout": 0.2, | |
| "epochs": 5, | |
| "early_stopping_patience": 2, | |
| "seed": 42, | |
| "device": "auto", | |
| "data_dir": "ner/experiments/person_role_v3/data", | |
| "runs_dir": "ner/experiments/person_role_v3/runs", | |
| "run_dir": "ner/experiments/person_role_v3/runs/full_20260527T122002Z", | |
| "epochs_requested": 5, | |
| "train_rows": 6823, | |
| "val_rows": 911, | |
| "best_epoch": 5, | |
| "best_val_token_f1": 0.7895277207392197, | |
| "device_resolved": "mps" | |
| } |