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SpanMarker with bert-base-cased on conll2002

This is a SpanMarker model trained on the conll2002 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: conll2002
  • Language: es
  • License: cc-by-sa-4.0

Model Sources

Model Labels

Label Examples
LOC "Victoria", "Australia", "Melbourne"
MISC "Ley", "Ciudad", "CrimeNet"
ORG "Tribunal Supremo", "EFE", "Commonwealth"
PER "Abogado General del Estado", "Daryl Williams", "Abogado General"

Evaluation

Metrics

Label Precision Recall F1
all 0.8331 0.8074 0.8201
LOC 0.8471 0.7759 0.8099
MISC 0.7092 0.4264 0.5326
ORG 0.7854 0.8558 0.8191
PER 0.9471 0.9329 0.9400

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("(SV2147) PP: PROBLEMAS INTERNOS PSOE INTERFIEREN EN POLITICA DE LA JUNTA Córdoba (EFE).")

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 0 31.8014 1238
Entities per sentence 0 2.2583 160

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
0.1164 200 0.0260 0.6907 0.5358 0.6035 0.9264
0.2328 400 0.0199 0.7567 0.6384 0.6925 0.9414
0.3491 600 0.0176 0.7773 0.7273 0.7515 0.9563
0.4655 800 0.0157 0.8066 0.7598 0.7825 0.9601
0.5819 1000 0.0158 0.8031 0.7413 0.7710 0.9605
0.6983 1200 0.0156 0.7975 0.7598 0.7782 0.9609
0.8147 1400 0.0139 0.8210 0.7615 0.7901 0.9625
0.9310 1600 0.0129 0.8426 0.7848 0.8127 0.9651

Framework Versions

  • Python: 3.10.12
  • SpanMarker: 1.5.0
  • Transformers: 4.38.2
  • PyTorch: 2.2.1+cu121
  • Datasets: 2.18.0
  • Tokenizers: 0.15.2

Citation

BibTeX

@software{Aarsen_SpanMarker,
    author = {Aarsen, Tom},
    license = {Apache-2.0},
    title = {{SpanMarker for Named Entity Recognition}},
    url = {https://github.com/tomaarsen/SpanMarkerNER}
}
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Finetuned from

Dataset used to train sepulm01/span-marker-bert-base-conll2002-es

Evaluation results