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Add evaluation results on xtreme dataset (#1)
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metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - xtreme
metrics:
  - f1
model-index:
  - name: xlm-roberta-base-finetuned-panx-de
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: xtreme
          type: xtreme
          args: PAN-X.de
        metrics:
          - name: F1
            type: f1
            value: 0.8620945214069894
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: xtreme
          type: xtreme
          config: PAN-X.de
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.899981559318585
            verified: true
          - name: Precision
            type: precision
            value: 0.9009035072102546
            verified: true
          - name: Recall
            type: recall
            value: 0.9206610317249744
            verified: true
          - name: F1
            type: f1
            value: 0.9106751198809252
            verified: true
          - name: loss
            type: loss
            value: 0.3754884600639343
            verified: true

xlm-roberta-base-finetuned-panx-de

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

  • Loss: 0.1372
  • F1: 0.8621

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
0.2575 1.0 525 0.1621 0.8292
0.1287 2.0 1050 0.1378 0.8526
0.0831 3.0 1575 0.1372 0.8621

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1