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alvarobartt
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bert-base-multilingual-cased-ner-spanish

Token Classification
SpanMarker
PyTorch
Spanish
ner
named-entity-recognition
generated_from_span_marker_trainer
Eval Results
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metadata
language:
  - es
license: cc-by-4.0
library_name: span-marker
tags:
  - span-marker
  - token-classification
  - ner
  - named-entity-recognition
  - generated_from_span_marker_trainer
datasets:
  - xtreme
metrics:
  - precision
  - recall
  - f1
widget:
  - text: Me llamo Álvaro y vivo en Barcelona (España).
  - text: Marie Curie fue profesora en la Universidad de Paris.
  - text: >-
      La Universidad de Salamanca es la universidad en activo más antigua de
      España.
pipeline_tag: token-classification
base_model: bert-base-multilingual-cased
model-index:
  - name: SpanMarker with bert-base-multilingual-cased on xtreme/PAN-X.es
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: xtreme/PAN-X.es
          type: xtreme
          split: eval
        metrics:
          - type: f1
            value: 0.9186626746506986
            name: F1
          - type: precision
            value: 0.9231154938993816
            name: Precision
          - type: recall
            value: 0.9142526071842411
            name: Recall

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}
}