SpanMarker with roberta-large on Jerado/enron_intangibles_ner
This is a SpanMarker model trained on the Jerado/enron_intangibles_ner dataset that can be used for Named Entity Recognition. This SpanMarker model uses roberta-large as the underlying encoder.
Model Details
Model Description
Model Sources
Model Labels
Label |
Examples |
Intangible |
"deal", "sample EES deal", "Enpower system" |
Evaluation
Metrics
Label |
Precision |
Recall |
F1 |
all |
0.4286 |
0.45 |
0.4390 |
Intangible |
0.4286 |
0.45 |
0.4390 |
Uses
Direct Use for Inference
from span_marker import SpanMarkerModel
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
entities = model.predict("It seems that there is a single significant policy concern for the ASIC policy committee.")
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 |
19.8706 |
216 |
Entities per sentence |
0 |
0.1865 |
6 |
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: 11
- mixed_precision_training: Native AMP
Training Results
Epoch |
Step |
Validation Loss |
Validation Precision |
Validation Recall |
Validation F1 |
Validation Accuracy |
3.3557 |
500 |
0.0075 |
0.4444 |
0.1667 |
0.2424 |
0.9753 |
6.7114 |
1000 |
0.0084 |
0.5714 |
0.3333 |
0.4211 |
0.9793 |
10.0671 |
1500 |
0.0098 |
0.6111 |
0.4583 |
0.5238 |
0.9815 |
Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- 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}
}