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SpanMarker with bert-base-multilingual-cased on xtreme/PAN-X.es

This is a SpanMarker model trained on the xtreme/PAN-X.es dataset that can be used for Named Entity Recognition. This SpanMarker model uses bert-base-multilingual-cased as the underlying encoder.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
LOC "Salamanca", "Paris", "Barcelona (España)"
ORG "ONU", "Fútbol Club Barcelona", "Museo Nacional del Prado"
PER "Fray Luis de León", "Leo Messi", "Álvaro Bartolomé"

Uses

Direct Use for Inference

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("alvarobartt/bert-base-multilingual-cased-ner-spanish")
# Run inference
entities = model.predict("Marie Curie fue profesora en la Universidad de Paris.")

Training Details

Training Set Metrics

Training set Min Median Max
Sentence length 3 6.4642 64
Entities per sentence 1 1.2375 24

Training Hyperparameters

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 2

Training Results

Epoch Step Validation Loss Validation Precision Validation Recall Validation F1 Validation Accuracy
0.3998 1000 0.0388 0.8761 0.8641 0.8701 0.9223
0.7997 2000 0.0326 0.8995 0.8740 0.8866 0.9341
1.1995 3000 0.0277 0.9076 0.9019 0.9047 0.9424
1.5994 4000 0.0261 0.9143 0.9113 0.9128 0.9473
1.9992 5000 0.0234 0.9231 0.9143 0.9187 0.9502

Framework Versions

  • Python: 3.10.12
  • SpanMarker: 1.3.1.dev
  • Transformers: 4.33.3
  • PyTorch: 2.0.1+cu118
  • Datasets: 2.14.5
  • Tokenizers: 0.13.3

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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Dataset used to train alvarobartt/bert-base-multilingual-cased-ner-spanish

Collection including alvarobartt/bert-base-multilingual-cased-ner-spanish

Evaluation results