supreethrao/instructNER_conll03_xl

SpanMarker

This is a SpanMarker model trained on the conll2003 dataset that can be used for Named Entity Recognition.

Model Details

Model Description

• Model Type: SpanMarker
• Maximum Sequence Length: 256 tokens
• Maximum Entity Length: 8 words
• Training Dataset: conll2003

Model Sources

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

Model Labels

Label	Examples
LOC	"BRUSSELS", "Britain", "Germany"
MISC	"British", "EU-wide", "German"
ORG	"European Union", "EU", "European Commission"
PER	"Nikolaus van der Pas", "Peter Blackburn", "Werner Zwingmann"

Evaluation

Metrics

Label	Precision	Recall	F1
all	0.9156	0.9263	0.9210
LOC	0.9327	0.9394	0.9361
MISC	0.7973	0.8462	0.8210
ORG	0.8987	0.9133	0.9059
PER	0.9706	0.9610	0.9658

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_conll03_xl")
# Run inference
entities = model.predict("Dong Jiong (China) beat Thomas Stuer-Lauridsen (Denmark) 15-10 15-6")

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("supreethrao/instructNER_conll03_xl")

# 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("supreethrao/instructNER_conll03_xl-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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• total_train_batch_size: 32
• total_eval_batch_size: 32
• 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
• mixed_precision_training: Native AMP

Framework Versions

• Python: 3.10.13
• SpanMarker: 1.5.0
• Transformers: 4.35.2
• PyTorch: 2.1.1
• Datasets: 2.15.0
• 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}
}