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: 0.8591260810195721
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: xtreme
          type: xtreme
          config: PAN-X.de
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8936608567922116
            verified: true
          - name: Precision
            type: precision
            value: 0.8994619740499086
            verified: true
          - name: Recall
            type: recall
            value: 0.9175378084372513
            verified: true
          - name: F1
            type: f1
            value: 0.9084099804488874
            verified: true
          - name: loss
            type: loss
            value: 0.4345104992389679
            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: 0.1352
F1: 0.8591

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
0.257	1.0	525	0.1512	0.8302
0.1305	2.0	1050	0.1401	0.8447
0.0817	3.0	1575	0.1352	0.8591

Framework versions

Transformers 4.18.0
Pytorch 1.10.0+cu111
Datasets 2.1.0
Tokenizers 0.12.1