SpanMarker with roberta-base on conll2003
This is a SpanMarker model trained on the conll2003 dataset that can be used for Named Entity Recognition. This SpanMarker model uses roberta-base as the underlying encoder.
Model Details
Model Description
- Model Type: SpanMarker
- Encoder: roberta-base
- Maximum Sequence Length: 256 tokens
- Maximum Entity Length: 6 words
- Training Dataset: conll2003
- Language: en
- License: apache-2.0
Model Sources
Model Labels
Label |
Examples |
LOC |
"BRUSSELS", "Britain", "Germany" |
MISC |
"British", "EU-wide", "German" |
ORG |
"EU", "European Commission", "European Union" |
PER |
"Werner Zwingmann", "Nikolaus van der Pas", "Peter Blackburn" |
Evaluation
Metrics
Label |
Precision |
Recall |
F1 |
all |
0.8944 |
0.9102 |
0.9022 |
LOC |
0.9220 |
0.9215 |
0.9217 |
MISC |
0.7332 |
0.7949 |
0.7628 |
ORG |
0.8764 |
0.8964 |
0.8863 |
PER |
0.9605 |
0.9629 |
0.9617 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
entities = model.predict("3. Tristan Hoffman (Netherlands) TVM same time")
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 |
14.5019 |
113 |
Entities per sentence |
0 |
1.6736 |
20 |
Training Hyperparameters
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
Training Results
Epoch |
Step |
Validation Loss |
Validation Precision |
Validation Recall |
Validation F1 |
Validation Accuracy |
0.2775 |
500 |
0.0282 |
0.9105 |
0.8355 |
0.8714 |
0.9670 |
0.5549 |
1000 |
0.0166 |
0.9215 |
0.9205 |
0.9210 |
0.9824 |
0.8324 |
1500 |
0.0151 |
0.9247 |
0.9346 |
0.9296 |
0.9853 |
Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.41.2
- PyTorch: 2.3.0+cu121
- Datasets: 2.20.0
- Tokenizers: 0.19.1
Citation
BibTeX
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}