bhadauriaupendra062/span-marker-roberta-base-conll03

SpanMarker with roberta-base on conll2003

This is a SpanMarker model trained on the conll2003 dataset that can be used for Named Entity Recognition. This SpanMarker model uses roberta-base as the underlying encoder.

Model Details

Model Description

• Model Type: SpanMarker
• Encoder: roberta-base
• Maximum Sequence Length: 256 tokens
• Maximum Entity Length: 6 words
• Training Dataset: conll2003
• Language: en
• License: apache-2.0

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	"EU", "European Commission", "European Union"
PER	"Werner Zwingmann", "Nikolaus van der Pas", "Peter Blackburn"

Evaluation

Metrics

Label	Precision	Recall	F1
all	0.8944	0.9102	0.9022
LOC	0.9220	0.9215	0.9217
MISC	0.7332	0.7949	0.7628
ORG	0.8764	0.8964	0.8863
PER	0.9605	0.9629	0.9617

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("3. Tristan Hoffman (Netherlands) TVM same time")

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: 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
0.2775	500	0.0282	0.9105	0.8355	0.8714	0.9670
0.5549	1000	0.0166	0.9215	0.9205	0.9210	0.9824
0.8324	1500	0.0151	0.9247	0.9346	0.9296	0.9853

Framework Versions

• Python: 3.10.12
• SpanMarker: 1.5.0
• Transformers: 4.41.2
• PyTorch: 2.3.0+cu121
• Datasets: 2.20.0
• 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}
}