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alvarobartt
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span-marker-roberta-base-bne-conll-2002-es

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:
  - conll2002
metrics:
  - precision
  - recall
  - f1
widget:
  - text: George Washington estuvo en Washington.
pipeline_tag: token-classification
base_model: PlanTL-GOB-ES/roberta-base-bne
model-index:
  - name: SpanMarker with PlanTL-GOB-ES/roberta-base-bne on conll2002
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: conll2002
          type: conll2002
          split: eval
        metrics:
          - type: f1
            value: 0.871172868582195
            name: F1
          - type: precision
            value: 0.888328530259366
            name: Precision
          - type: recall
            value: 0.8546672828096118
            name: Recall

SpanMarker with PlanTL-GOB-ES/roberta-base-bne on conll2002

This is a SpanMarker model trained on the conll2002 dataset that can be used for Named Entity Recognition. This SpanMarker model uses PlanTL-GOB-ES/roberta-base-bne as the underlying encoder.

Model Details

Model Description

Model Sources

Model Labels

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

Uses

Direct Use for Inference 

from span_marker import SpanMarkerModel

# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("alvarobartt/span-marker-roberta-base-bne-conll-2002-es")
# Run inference
entities = model.predict("George Washington estuvo en Washington.")

Training Details

Training Set Metrics

Training set Min Median Max
Sentence length 1 31.8052 1238
Entities per sentence 0 2.2586 160

Training Hyperparameters

  • learning_rate: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • 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
0.1188 100 0.0704 0.0 0.0 0.0 0.8608
0.2375 200 0.0279 0.8765 0.4034 0.5525 0.9025
0.3563 300 0.0158 0.8381 0.7211 0.7752 0.9524
0.4751 400 0.0134 0.8525 0.7463 0.7959 0.9576
0.5938 500 0.0130 0.8844 0.7549 0.8145 0.9560
0.7126 600 0.0119 0.8480 0.8006 0.8236 0.9650
0.8314 700 0.0098 0.8794 0.8408 0.8597 0.9695
0.9501 800 0.0091 0.8842 0.8360 0.8594 0.9722
1.0689 900 0.0093 0.8976 0.8387 0.8672 0.9698
1.1876 1000 0.0094 0.8880 0.8517 0.8694 0.9739
1.3064 1100 0.0086 0.8920 0.8530 0.8721 0.9737
1.4252 1200 0.0092 0.8896 0.8452 0.8668 0.9728
1.5439 1300 0.0094 0.8765 0.8313 0.8533 0.9720
1.6627 1400 0.0089 0.8805 0.8445 0.8621 0.9720
1.7815 1500 0.0088 0.8834 0.8581 0.8706 0.9747
1.9002 1600 0.0088 0.8883 0.8547 0.8712 0.9747

Framework Versions

  • Python: 3.10.12
  • SpanMarker: 1.3.1.dev
  • Transformers: 4.33.2
  • 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}
}