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xlm-roberta-base-WNUT-ner

Token Classification
Transformers
PyTorch
xlm-roberta
Generated from Trainer
Eval Results
Inference Endpoints
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xlm-roberta-base-WNUT-ner / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - wnut_17
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: xlm-roberta-base
model-index:
  - name: xlm-roberta-base-WNUT-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: wnut_17
          type: wnut_17
          config: wnut_17
          split: test
          args: wnut_17
        metrics:
          - type: precision
            value: 0.6251511487303507
            name: Precision
          - type: recall
            value: 0.47914735866543096
            name: Recall
          - type: f1
            value: 0.5424973767051418
            name: F1
          - type: accuracy
            value: 0.952295460374455
            name: Accuracy

xlm-roberta-base-WNUT-ner

This model is a fine-tuned version of xlm-roberta-base on the wnut_17 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3376
  • Precision: 0.6252
  • Recall: 0.4791
  • F1: 0.5425
  • Accuracy: 0.9523

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Accuracy
No log 1.0 213 0.2787 0.5650 0.3383 0.4232 0.9418
No log 2.0 426 0.2535 0.6225 0.4004 0.4873 0.9485
0.177 3.0 639 0.2773 0.6594 0.3911 0.4910 0.9497
0.177 4.0 852 0.2651 0.6098 0.4708 0.5314 0.9526
0.0551 5.0 1065 0.3076 0.6026 0.4652 0.5251 0.9514
0.0551 6.0 1278 0.3031 0.6343 0.4662 0.5374 0.9531
0.0551 7.0 1491 0.3319 0.6336 0.4680 0.5384 0.9523
0.0276 8.0 1704 0.3430 0.6508 0.4560 0.5362 0.9526
0.0276 9.0 1917 0.3342 0.6138 0.4773 0.5370 0.9521
0.0157 10.0 2130 0.3376 0.6252 0.4791 0.5425 0.9523

Framework versions

  • Transformers 4.26.0
  • Pytorch 1.13.1+cu117
  • Datasets 2.9.0
  • Tokenizers 0.13.2