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span-marker-xlm-roberta-large-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 fue a Washington.
pipeline_tag: token-classification
base_model: xlm-roberta-large
model-index:
  - name: SpanMarker with xlm-roberta-large on conll2002
    results:
      - task:
          type: token-classification
          name: Named Entity Recognition
        dataset:
          name: conll2002
          type: unknown
          split: eval
        metrics:
          - type: f1
            value: 0.8911398300151355
            name: F1
          - type: precision
            value: 0.8981459751232105
            name: Precision
          - type: recall
            value: 0.8842421441774492
            name: Recall

SpanMarker with xlm-roberta-large on conll2002

This is a SpanMarker model that can be used for Named Entity Recognition. This SpanMarker model uses xlm-roberta-large as the underlying encoder.

Model Details

Model Description

  • Model Type: SpanMarker
  • Encoder: xlm-roberta-large
  • Maximum Sequence Length: 256 tokens
  • Maximum Entity Length: 8 words
  • Training Dataset: conll2002
  • Language: es
  • License: cc-by-4.0

Model Sources

Model Labels

Label Examples
LOC "Melbourne", "Australia", "Victoria"
MISC "CrimeNet", "Ciudad", "Ley"
ORG "Commonwealth", "Tribunal Supremo", "EFE"
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-xlm-roberta-large-conll-2002-es")
# Run inference
entities = model.predict("George Washington fue a 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: 1e-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
0.0587 50 0.4612 0.0280 0.0007 0.0014 0.8576
0.1174 100 0.0512 0.5 0.0002 0.0005 0.8609
0.1761 150 0.0254 0.7622 0.5494 0.6386 0.9278
0.2347 200 0.0177 0.7840 0.7135 0.7471 0.9483
0.2934 250 0.0153 0.8072 0.7944 0.8007 0.9662
0.3521 300 0.0175 0.8439 0.7544 0.7966 0.9611
0.4108 350 0.0103 0.8828 0.8108 0.8452 0.9687
0.4695 400 0.0105 0.8674 0.8433 0.8552 0.9724
0.5282 450 0.0098 0.8651 0.8477 0.8563 0.9745
0.5869 500 0.0092 0.8634 0.8306 0.8467 0.9736
0.6455 550 0.0106 0.8556 0.8581 0.8568 0.9758
0.7042 600 0.0096 0.8712 0.8521 0.8616 0.9733
0.7629 650 0.0090 0.8791 0.8420 0.8601 0.9740
0.8216 700 0.0082 0.8883 0.8799 0.8840 0.9769
0.8803 750 0.0081 0.8877 0.8604 0.8739 0.9763
0.9390 800 0.0087 0.8785 0.8738 0.8762 0.9763
0.9977 850 0.0084 0.8777 0.8653 0.8714 0.9767
1.0563 900 0.0081 0.8894 0.8713 0.8803 0.9767
1.1150 950 0.0078 0.8944 0.8708 0.8825 0.9768
1.1737 1000 0.0079 0.8973 0.8722 0.8846 0.9776
1.2324 1050 0.0080 0.8792 0.8780 0.8786 0.9783
1.2911 1100 0.0082 0.8821 0.8574 0.8696 0.9767
1.3498 1150 0.0075 0.8928 0.8697 0.8811 0.9774
1.4085 1200 0.0076 0.8919 0.8803 0.8860 0.9792
1.4671 1250 0.0078 0.8846 0.8695 0.8770 0.9781
1.5258 1300 0.0074 0.8944 0.8845 0.8894 0.9792
1.5845 1350 0.0076 0.8922 0.8856 0.8889 0.9796
1.6432 1400 0.0072 0.9004 0.8799 0.8900 0.9790
1.7019 1450 0.0076 0.8944 0.8889 0.8916 0.9800
1.7606 1500 0.0074 0.8962 0.8861 0.8911 0.9800
1.8192 1550 0.0072 0.8988 0.8886 0.8937 0.9809
1.8779 1600 0.0074 0.8962 0.8833 0.8897 0.9797
1.9366 1650 0.0071 0.8976 0.8849 0.8912 0.9799
1.9953 1700 0.0071 0.8981 0.8842 0.8911 0.9799

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}
}