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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - conll2002
  - conll2003
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: mBERT-base-cased-NER-CONLL
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: conll2002
          type: conll2002
          args: es
        metrics:
          - name: Precision
            type: precision
            value: 0.8621083924079579
          - name: Recall
            type: recall
            value: 0.8662683823529411
          - name: F1
            type: f1
            value: 0.8641833810888252
          - name: Accuracy
            type: accuracy
            value: 0.9790639230580277

mBERT-base-cased-NER-CONLL (EN-ES)

This model is a fine-tuned version of bert-base-multilingual-cased on the conll2003 and conll2002 datasets. Training was performed separately. It achieves the following results on the evaluation set:

Connll2003:

  • Loss: 0.0585
  • Precision: 0.9489
  • Recall: 0.9541
  • F1: 0.9515
  • Accuracy: 0.9880

Conll2002:

  • Loss: 0.1435
  • Precision: 0.8621
  • Recall: 0.8663
  • F1: 0.8642
  • Accuracy: 0.9791

Model description

IOB tagging Scheme. PER/LOC/MISC/ORG tags

Intended uses & limitations

More information needed

Training and evaluation data

Conll2002/2003 (ES-EN)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 3e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4

Training results

Conll2003:

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.1739 1.0 878 0.0741 0.9246 0.9181 0.9213 0.9823
0.045 2.0 1756 0.0586 0.9469 0.9476 0.9472 0.9870
0.0213 3.0 2634 0.0583 0.9503 0.9510 0.9506 0.9877
0.0113 4.0 3512 0.0585 0.9489 0.9541 0.9515 0.9880

Conll2002:

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
0.0739 1.0 4162 0.1322 0.8430 0.8267 0.8348 0.9741
0.0454 2.0 8324 0.1158 0.8664 0.8614 0.8639 0.9782
0.031 3.0 12486 0.1243 0.8521 0.8660 0.8590 0.9783
0.0136 4.0 16648 0.1435 0.8621 0.8663 0.8642 0.9791

Framework versions

  • Transformers 4.12.3
  • Pytorch 1.10.0+cu111
  • Datasets 1.15.1
  • Tokenizers 0.10.3