lambdavi/span-marker-luke-base-conll2003

SpanMarker

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

Model Details

Important Note: I used the Tokenizer from "roberta-base".

from span_marker import SpanMarkerModel
from span_marker.tokenizer import SpanMarkerTokenizer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-base-conll2003")
+tokenizer = SpanMarkerTokenizer.from_pretrained("roberta-base", config=model.tokenizer.config)
+model.set_tokenizer(tokenizer)

# Run inference
entities = model.predict("Portsmouth:Middlesex 199 and 426 (J. Pooley 111,M. Ramprakash 108,M. Gatting 83), Hampshire 232 and 109-5.")

Model Description

• Model Type: SpanMarker
• Maximum Sequence Length: 512 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	"Germany", "BRUSSELS", "Britain"
MISC	"German", "British", "EU-wide"
ORG	"European Commission", "EU", "European Union"
PER	"Werner Zwingmann", "Nikolaus van der Pas", "Peter Blackburn"

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel
from span_marker.tokenizer import SpanMarkerTokenizer

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("lambdavi/span-marker-luke-base-conll2003")
tokenizer = SpanMarkerTokenizer.from_pretrained("roberta-base", config=model.tokenizer.config)
model.set_tokenizer(tokenizer)

# Run inference
entities = model.predict("Portsmouth:Middlesex 199 and 426 (J. Pooley 111,M. Ramprakash 108,M. Gatting 83), Hampshire 232 and 109-5.")

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	1	14.5019	113
Entities per sentence	0	1.6736	20

Training Hyperparameters

• learning_rate: 1e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5

Training Results

Epoch	Step	Validation Loss	Validation Precision	Validation Recall	Validation F1	Validation Accuracy
1.0	883	0.0123	0.9293	0.9274	0.9284	0.9848
2.0	1766	0.0089	0.9412	0.9456	0.9434	0.9882
3.0	2649	0.0077	0.9499	0.9505	0.9502	0.9893
4.0	3532	0.0070	0.9527	0.9537	0.9532	0.9900
5.0	4415	0.0068	0.9543	0.9557	0.9550	0.9902

Framework Versions

• Python: 3.10.12
• SpanMarker: 1.5.0
• Transformers: 4.36.0
• PyTorch: 2.0.0
• 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}
}