SpanMarker
This is a SpanMarker model trained on the SpeedOfMagic/ontonotes_english dataset that can be used for Named Entity Recognition.
Model Details
Model Description
Model Sources
Model Labels
Label |
Examples |
CARDINAL |
"tens of thousands", "One point three million", "two" |
DATE |
"Sunday", "a year", "two thousand one" |
EVENT |
"World War Two", "Katrina", "Hurricane Katrina" |
FAC |
"Route 80", "the White House", "Dylan 's Candy Bars" |
GPE |
"America", "Atlanta", "Miami" |
LANGUAGE |
"English", "Russian", "Arabic" |
LAW |
"Roe", "the Patriot Act", "FISA" |
LOC |
"Asia", "the Gulf Coast", "the West Bank" |
MONEY |
"twenty - seven million dollars", "one hundred billion dollars", "less than fourteen thousand dollars" |
NORP |
"American", "Muslim", "Americans" |
ORDINAL |
"third", "First", "first" |
ORG |
"Wal - Mart", "Wal - Mart 's", "a Wal - Mart" |
PERCENT |
"seventeen percent", "sixty - seven percent", "a hundred percent" |
PERSON |
"Kira Phillips", "Rick Sanchez", "Bob Shapiro" |
PRODUCT |
"Columbia", "Discovery Shuttle", "Discovery" |
QUANTITY |
"forty - five miles", "six thousand feet", "a hundred and seventy pounds" |
TIME |
"tonight", "evening", "Tonight" |
WORK_OF_ART |
"A Tale of Two Cities", "Newsnight", "Headline News" |
Evaluation
Metrics
Label |
Precision |
Recall |
F1 |
all |
0.9046 |
0.9109 |
0.9077 |
CARDINAL |
0.8579 |
0.8524 |
0.8552 |
DATE |
0.8634 |
0.8893 |
0.8762 |
EVENT |
0.6719 |
0.6935 |
0.6825 |
FAC |
0.7211 |
0.7852 |
0.7518 |
GPE |
0.9725 |
0.9647 |
0.9686 |
LANGUAGE |
0.9286 |
0.5909 |
0.7222 |
LAW |
0.7941 |
0.7297 |
0.7606 |
LOC |
0.7632 |
0.8101 |
0.7859 |
MONEY |
0.8914 |
0.8885 |
0.8900 |
NORP |
0.9311 |
0.9643 |
0.9474 |
ORDINAL |
0.8227 |
0.9282 |
0.8723 |
ORG |
0.9217 |
0.9073 |
0.9145 |
PERCENT |
0.9145 |
0.9198 |
0.9171 |
PERSON |
0.9638 |
0.9643 |
0.9640 |
PRODUCT |
0.6778 |
0.8026 |
0.7349 |
QUANTITY |
0.7850 |
0.8 |
0.7925 |
TIME |
0.6794 |
0.6730 |
0.6762 |
WORK_OF_ART |
0.6562 |
0.6442 |
0.6502 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_ontonotes5_xl")
entities = model.predict("Robert White, Canadian Auto Workers union president, used the impending Scarborough shutdown to criticize the U.S. - Canada free trade agreement and its champion, Prime Minister Brian Mulroney.")
Downstream Use
You can finetune this model on your own dataset.
Click to expand
from span_marker import SpanMarkerModel, Trainer
model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_ontonotes5_xl")
dataset = load_dataset("conll2003")
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("supreethrao/instructNER_ontonotes5_xl-finetuned")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Sentence length |
1 |
18.1647 |
210 |
Entities per sentence |
0 |
1.3655 |
32 |
Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Framework Versions
- Python: 3.10.13
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.1
- Datasets: 2.15.0
- Tokenizers: 0.15.0
Citation
BibTeX
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}