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_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 | |
| } | |
| ] | |
| } |