xlm-roberta-base-finetuned-panx-de

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

• Loss: 0.1363
• F1 Score: 0.8658

Model description

This model is a fine-tuned version of xlm-roberta-base on the German subset of the PAN-X dataset for Named Entity Recognition (NER). The model has been fine-tuned to perform token classification tasks and is evaluated on its performance in identifying named entities in German text.

Intended uses & limitations

Intended uses:

Named Entity Recognition (NER) tasks specifically for German. Token classification tasks involving German text.

Limitations:

The model's performance is optimized for German and may not generalize well to other languages without further fine-tuning. The model's predictions are based on the data it was trained on and may not handle out-of-domain data as effectively.

Training and evaluation data

The model was fine-tuned on the German subset of the PAN-X dataset, which includes labeled examples of named entities in German text. The evaluation data is a separate portion of the same dataset, used to assess the model's performance.

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 Score
0.2539	1.0	525	0.1505	0.8246
0.1268	2.0	1050	0.1380	0.8503
0.0794	3.0	1575	0.1363	0.8658

Evaluation results

The model's F1-score on the validation set for the German subset is 0.8658, indicating a strong performance in named entity recognition for German text.

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1