SpanMarker with xlm-roberta-base
Trained on various nordic lang. datasets: see https://huggingface.co/datasets/tollefj/nordic-ner
This is a SpanMarker model trained on the norne dataset that can be used for Named Entity Recognition. This SpanMarker model uses FacebookAI/xlm-roberta-base as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: FacebookAI/xlm-roberta-base
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 8 words
- Training Dataset: norne
- Language: en
- License: cc-by-sa-4.0
Model Sources
Model Labels
Label |
Examples |
LOC |
"Gran", "Leicestershire", "Den tyske antarktisekspedisjonen" |
MISC |
"socialdemokratiske", "nationalist", "Living Legend" |
ORG |
"Stabæk", "Samlaget", "Marillion" |
PER |
"Fish", "Dmitrij Medvedev", "Guru Ardjan Dev" |
Evaluation
Metrics
Label |
Precision |
Recall |
F1 |
all |
0.9218 |
0.9146 |
0.9182 |
LOC |
0.9284 |
0.9433 |
0.9358 |
MISC |
0.6515 |
0.6047 |
0.6272 |
ORG |
0.8951 |
0.8547 |
0.8745 |
PER |
0.9513 |
0.9526 |
0.9520 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
entities = model.predict("Roddarn blir proffs efter OS.")
Downstream Use
You can finetune this model on your own dataset.
Click to expand
from span_marker import SpanMarkerModel, Trainer
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
dataset = load_dataset("conll2003")
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("span_marker_model_id-finetuned")
Training Details
Training Set Metrics
Training set |
Min |
Median |
Max |
Sentence length |
1 |
12.8175 |
331 |
Entities per sentence |
0 |
1.0055 |
54 |
Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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
Training Results
Epoch |
Step |
Validation Loss |
Validation Precision |
Validation Recall |
Validation F1 |
Validation Accuracy |
0.5711 |
3000 |
0.0146 |
0.8650 |
0.8725 |
0.8687 |
0.9722 |
1.1422 |
6000 |
0.0123 |
0.8994 |
0.8920 |
0.8957 |
0.9778 |
1.7133 |
9000 |
0.0101 |
0.9184 |
0.8984 |
0.9083 |
0.9805 |
2.2844 |
12000 |
0.0101 |
0.9198 |
0.9110 |
0.9154 |
0.9818 |
2.8555 |
15000 |
0.0089 |
0.9245 |
0.9150 |
0.9197 |
0.9830 |
Framework Versions
- Python: 3.12.2
- SpanMarker: 1.5.0
- Transformers: 4.38.2
- PyTorch: 2.2.1+cu121
- Datasets: 2.18.0
- Tokenizers: 0.15.2
Citation
BibTeX
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}