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
- Repository: SpanMarker on GitHub
- Thesis: SpanMarker For Named Entity Recognition
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
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
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
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003
# Initialize a Trainer using the pretrained model & dataset
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}
}
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Model tree for tollefj/nordic-ner
Base model
FacebookAI/xlm-roberta-baseDataset used to train tollefj/nordic-ner
Evaluation results
- F1 on nornetest set self-reported0.918
- Precision on nornetest set self-reported0.922
- Recall on nornetest set self-reported0.915