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
| { | |
| "n_items": 904, | |
| "strict_span_role": { | |
| "precision": 0.7887755102040817, | |
| "recall": 0.817989417989418, | |
| "f1": 0.8031168831168832, | |
| "tp": 773, | |
| "fp": 207, | |
| "fn": 172 | |
| }, | |
| "normalised_span_role": { | |
| "precision": 0.7887755102040817, | |
| "recall": 0.817989417989418, | |
| "f1": 0.8031168831168832, | |
| "tp": 773, | |
| "fp": 207, | |
| "fn": 172 | |
| }, | |
| "normalised_name_only": { | |
| "precision": 0.8510204081632653, | |
| "recall": 0.8825396825396825, | |
| "f1": 0.8664935064935065, | |
| "tp": 834, | |
| "fp": 146, | |
| "fn": 111 | |
| }, | |
| "role_given_name": { | |
| "matched_names": 834, | |
| "correct_roles": 773, | |
| "accuracy": 0.9268585131894485 | |
| }, | |
| "per_role_normalised_span_role": { | |
| "AUTHOR": { | |
| "precision": 0.8827586206896552, | |
| "recall": 0.8533333333333334, | |
| "f1": 0.8677966101694914, | |
| "tp": 128, | |
| "fp": 17, | |
| "fn": 22 | |
| }, | |
| "CENSOR": { | |
| "precision": 0.8736842105263158, | |
| "recall": 0.8924731182795699, | |
| "f1": 0.8829787234042553, | |
| "tp": 83, | |
| "fp": 12, | |
| "fn": 10 | |
| }, | |
| "COMMENTATOR": { | |
| "precision": 0.5833333333333334, | |
| "recall": 0.4666666666666667, | |
| "f1": 0.5185185185185186, | |
| "tp": 7, | |
| "fp": 5, | |
| "fn": 8 | |
| }, | |
| "OWNER": { | |
| "precision": 0.6993670886075949, | |
| "recall": 0.7700348432055749, | |
| "f1": 0.7330016583747927, | |
| "tp": 221, | |
| "fp": 95, | |
| "fn": 66 | |
| }, | |
| "TRANSCRIBER": { | |
| "precision": 0.8033240997229917, | |
| "recall": 0.8192090395480226, | |
| "f1": 0.8111888111888111, | |
| "tp": 290, | |
| "fp": 71, | |
| "fn": 64 | |
| }, | |
| "TRANSLATOR": { | |
| "precision": 0.8627450980392157, | |
| "recall": 0.9565217391304348, | |
| "f1": 0.9072164948453608, | |
| "tp": 44, | |
| "fp": 7, | |
| "fn": 2 | |
| } | |
| } | |
| } |