Downloads · 30 days
13
12% of all-time downloads
alexgoldberg/hebrew-manuscript-joint-ner-v2
hebrew-manuscript-joint-ner-v2 is a token classification model from alexgoldberg. Use it when you need labels on individual words, such as names. It is set up for transformers.
This repository contains the MHM Pipeline person NER model. The current checkpoint is the role-aware v3 replacement for the earlier custom two-head checkpoint, while keeping the same repository and bundle name for com…
Downloads · 30 days
13
12% of all-time downloads
All-time downloads
106
Public
Parameters
184M
3 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors735 MB · 100%
From the Hugging Face model README
This repository contains the MHM Pipeline person NER model. The current checkpoint is the role-aware v3 replacement for the earlier custom two-head checkpoint, while keeping the same repository and bundle name for compatibility.
The model is a DictaBERT token-classification checkpoint that predicts BIO labels with the person role encoded directly in the tag:
AUTHORTRANSCRIBEROWNERCENSORTRANSLATORCOMMENTATORHeld-out v3 test split, 904 items:
| Metric | Score |
|---|---|
| strict span + role F1 | 0.8031 |
| strict precision | 0.7888 |
| strict recall | 0.8180 |
| name-only F1 | 0.8665 |
| role accuracy when name matched | 0.9269 |
Per-role strict span+role F1:
| Role | F1 |
|---|---|
| AUTHOR | 0.8678 |
| CENSOR | 0.8830 |
| COMMENTATOR | 0.5185 |
| OWNER | 0.7330 |
| TRANSCRIBER | 0.8112 |
| TRANSLATOR | 0.9072 |
from transformers import AutoModelForTokenClassification, AutoTokenizer
repo_id = "alexgoldberg/hebrew-manuscript-joint-ner-v2"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForTokenClassification.from_pretrained(repo_id)
In MHM Pipeline, use ner.inference_pipeline.JointNERPipeline. It preserves the
legacy output schema:
from ner.inference_pipeline import JointNERPipeline
pipeline = JointNERPipeline("alexgoldberg/hebrew-manuscript-joint-ner-v2")
entities = pipeline.process_text("הספר נכתב על ידי משה בן יעקב.")
Example output:
[
{
"person": "משה בן יעקב",
"role": "TRANSCRIBER",
"confidence": 0.9918,
"model_confidence": 0.9918,
"start": 17,
"end": 28
}
]
The previous custom checkpoint can be recovered from the Hub commit history. This version intentionally replaces keyword-based role classification with neural role-aware BIO labels.