SpanMarker
This is a SpanMarker model trained on the conll2003 dataset that can be used for Named Entity Recognition.
Model Details
Model Description
- Model Type: SpanMarker
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 8 words
- Training Dataset: conll2003
Model Sources
Model Labels
Label |
Examples |
LOC |
"BRUSSELS", "Britain", "Germany" |
MISC |
"British", "EU-wide", "German" |
ORG |
"European Union", "EU", "European Commission" |
PER |
"Nikolaus van der Pas", "Peter Blackburn", "Werner Zwingmann" |
Evaluation
Metrics
Label |
Precision |
Recall |
F1 |
all |
0.9156 |
0.9263 |
0.9210 |
LOC |
0.9327 |
0.9394 |
0.9361 |
MISC |
0.7973 |
0.8462 |
0.8210 |
ORG |
0.8987 |
0.9133 |
0.9059 |
PER |
0.9706 |
0.9610 |
0.9658 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_conll03_xl")
entities = model.predict("Dong Jiong (China) beat Thomas Stuer-Lauridsen (Denmark) 15-10 15-6")
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_conll03_xl")
dataset = load_dataset("conll2003")
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("supreethrao/instructNER_conll03_xl-finetuned")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Sentence length |
1 |
14.5019 |
113 |
Entities per sentence |
0 |
1.6736 |
20 |
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}
}