This is a SpanMarker model trained on the DFKI-SLT/few-nerd dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-cased as the underlying encoder.

Model Details

Model Description

Model Type: SpanMarker
Encoder: bert-base-cased
Maximum Sequence Length: 256 tokens
Maximum Entity Length: 8 words
Training Dataset: DFKI-SLT/few-nerd
Language: en
License: cc-by-sa-4.0

Model Sources

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

Model Labels

Label	Examples
art	"Time", "The Seven Year Itch", "Imelda de ' Lambertazzi"
building	"Henry Ford Museum", "Boston Garden", "Sheremetyevo International Airport"
event	"French Revolution", "Iranian Constitutional Revolution", "Russian Revolution"
location	"Croatian", "the Republic of Croatia", "Mediterranean Basin"
organization	"Church 's Chicken", "IAEA", "Texas Chicken"
other	"Amphiphysin", "BAR", "N-terminal lipid"
person	"Hicks", "Ellaline Terriss", "Edmund Payne"
product	"Phantom", "Corvettes - GT1 C6R", "100EX"

Evaluation

Metrics

Label	Precision	Recall	F1
all	0.7685	0.7674	0.7679
art	0.7749	0.6884	0.7291
building	0.6045	0.6612	0.6316
event	0.6437	0.5161	0.5729
location	0.8066	0.8425	0.8241
organization	0.7127	0.6836	0.6978
other	0.6802	0.6775	0.6789
person	0.8900	0.9135	0.9016
product	0.6596	0.6305	0.6447

Uses

Direct Use for Inference

python

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("Caretaker manager George Goss led them on a run in the FA Cup, defeating Liverpool in round 4, to reach the semi-final at Stamford Bridge, where they were defeated 2–0 by Sheffield United on 28 March 1925.")

Downstream Use

You can finetune this model on your own dataset.

Click to expand

python

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	24.4956	163
Entities per sentence	0	2.5439	35

Training Hyperparameters

learning_rate: 5e-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
0.1629	200	0.0323	0.7242	0.5919	0.6514	0.8980
0.3259	400	0.0232	0.7537	0.7149	0.7337	0.9252
0.4888	600	0.0212	0.7767	0.7301	0.7527	0.9301
0.6517	800	0.0209	0.7605	0.7615	0.7610	0.9353
0.8147	1000	0.0194	0.7815	0.7604	0.7708	0.9383
0.9776	1200	0.0195	0.7681	0.7833	0.7756	0.9403

Framework Versions

Python: 3.10.12
SpanMarker: 1.5.0
Transformers: 4.35.2
PyTorch: 2.1.0+cu121
Datasets: 2.16.0
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}
}

MinhMinh09/span-marker-bert-base-fewnerd-coarse-super

SpanMarker with bert-base-cased on DFKI-SLT/few-nerd