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alvarobartt
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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
Model card Files Community
span-marker-xlm-roberta-large-conll-2002-es / README.md
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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
metrics:
  - precision
  - recall
  - f1
widget:
  - text: >-
      Por otro lado , el primer ministro portugu茅s , Antonio Guterres ,
      presidente de turno del Consejo Europeo , recibi贸 hoy al ministro del
      Interior de Colombia , Hugo de la Calle , enviado especial del presidente
      de su pa铆s , Andr茅s Pastrana .
  - text: >-
      Los consejeros de la Presidencia , Gaspar Zarr铆as , de Justicia , Carmen
      Hermos铆n , y de Asuntos Sociales , Isa铆as P茅rez Salda帽a , dar谩n comienzo
      ma帽ana a los turnos de comparecencias de los miembros del Gobierno andaluz
      en el Parlamento auton贸mico para informar de las l铆neas de actuaci贸n de
      sus departamentos .
  - text: >-
      ( SV2147 ) PP : PROBLEMAS INTERNOS PSOE INTERFIEREN EN POLITICA DE LA
      JUNTA C贸rdoba ( EFE ) .
  - text: >-
      Cuando vino a Soria , en febrero de 1998 , para sustituir al entonces
      destituido Antonio G贸mez , estaba dirigiendo al Badajoz B en tercera
      divisi贸n y consigui贸 con el Numancia la permanencia en la 煤ltima jornada
      frente al H茅rcules .
  - text: >-
      El ministro ecuatoriano de Defensa , Hugo Unda , asegur贸 hoy que las
      Fuerzas Armadas respetar谩n la decisi贸n del Parlamento sobre la amnist铆a
      para los involucrados en la asonada golpista del pasado 21 de enero ,
      cuando fue derrocado el presidente Jamil Mahuad .
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
  • 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 馃 Hub
model = SpanMarkerModel.from_pretrained("alvarobartt/span-marker-xlm-roberta-large-conll-2002-es")
# Run inference
entities = model.predict("( SV2147 ) PP : PROBLEMAS INTERNOS PSOE INTERFIEREN EN POLITICA DE LA JUNTA C贸rdoba ( EFE ) .")

Downstream Use

You can finetune this model on your own dataset.

Click to expand 
from span_marker import SpanMarkerModel, Trainer

# Download from the 馃 Hub
model = SpanMarkerModel.from_pretrained("alvarobartt/span-marker-xlm-roberta-large-conll-2002-es")

# 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("alvarobartt/span-marker-xlm-roberta-large-conll-2002-es-finetuned")

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}
}