---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-panx-de
  results: []
language:
- de
library_name: transformers
---

# xlm-roberta-base-finetuned-panx-de

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/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