SpanMarker with bert-base-uncased on my-data

This is a SpanMarker model that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-uncased as the underlying encoder.

Model Details

Model Description

Model Type: SpanMarker
Encoder: bert-base-uncased
Maximum Sequence Length: 256 tokens
Maximum Entity Length: 8 words
Language: en
License: cc-by-sa-4.0

Model Sources

Repository: SpanMarker on GitHub
Thesis: SpanMarker For Named Entity Recognition

Model Labels

Label	Examples
Data	"an overall mitochondrial", "defect", "Depth time - series"
Material	"cross - shore measurement locations", "the subject 's fibroblasts", "COXI , COXII and COXIII subunits"
Method	"EFSA", "an approximation", "in vitro"
Process	"translation", "intake", "a significant reduction of synthesis"

Evaluation

Metrics

Label	Precision	Recall	F1
all	0.6901	0.6228	0.6547
Data	0.6136	0.5714	0.5918
Material	0.7926	0.7413	0.7661
Method	0.4286	0.3	0.3529
Process	0.6780	0.5854	0.6283

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("In situ Peak Force Tapping AFM was employed for determining morphology and nano - mechanical properties of the surface layer .")

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	3	25.6049	106
Entities per sentence	0	5.2439	22

Training Hyperparameters

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
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: 10

Training Results

Epoch	Step	Validation Loss	Validation Precision	Validation Recall	Validation F1	Validation Accuracy
2.0134	300	0.0557	0.6921	0.5706	0.6255	0.7645
4.0268	600	0.0583	0.6994	0.6527	0.6752	0.7974
6.0403	900	0.0701	0.7085	0.6679	0.6876	0.8039
8.0537	1200	0.0797	0.6963	0.6870	0.6916	0.8129

Framework Versions

Python: 3.10.12
SpanMarker: 1.5.0
Transformers: 4.36.2
PyTorch: 2.0.1+cu118
Datasets: 2.16.1
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}
}

zhang19991111
/

bert-base-spanmarker-STEM-NER