README.md

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