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
File size: 635 Bytes
1e1da14 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"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"
} |