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Add evaluation results on wikiann dataset (#1)
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metadata
license: mit
tags:
  - generated_from_trainer
metrics:
  - f1
datasets:
  - wikiann
model-index:
  - name: xlm-roberta-base-finetuned-panx-all
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: wikiann
          type: wikiann
          config: en
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.843189280620875
            verified: true
          - name: Precision
            type: precision
            value: 0.8410061269097046
            verified: true
          - name: Recall
            type: recall
            value: 0.8568527450211155
            verified: true
          - name: F1
            type: f1
            value: 0.8488554853827908
            verified: true
          - name: loss
            type: loss
            value: 0.6632214784622192
            verified: true

xlm-roberta-base-finetuned-panx-all

This model is a fine-tuned version of xlm-roberta-base on the PAN-X dataset. The model is trained in Chapter 4: Multilingual Named Entity Recognition in the NLP with Transformers book. You can find the full code in the accompanying Github repository.

It achieves the following results on the evaluation set:

  • Loss: 0.1739
  • F1: 0.8581

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: 5e-05
  • train_batch_size: 24
  • eval_batch_size: 24
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss F1
0.2912 1.0 835 0.1883 0.8238
0.1548 2.0 1670 0.1738 0.8480
0.101 3.0 2505 0.1739 0.8581

Framework versions

  • Transformers 4.12.0.dev0
  • Pytorch 1.9.1+cu102
  • Datasets 1.12.1
  • Tokenizers 0.10.3