---
library_name: transformers
base_model: FacebookAI/xlm-roberta-large-finetuned-conll03-german
tags:
- generated_from_trainer
metrics:
- accuracy
- rouge
model-index:
- name: 23aa0d2641e24d14d82a734181021dc2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 23aa0d2641e24d14d82a734181021dc2

This model is a fine-tuned version of [FacebookAI/xlm-roberta-large-finetuned-conll03-german](https://huggingface.co/FacebookAI/xlm-roberta-large-finetuned-conll03-german) on the nyu-mll/glue [mnli] dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1001
- Data Size: 0.5
- Epoch Runtime: 945.5774
- Accuracy: 0.3545
- F1 Macro: 0.1745
- Rouge1: 0.3544
- Rouge2: 0.0
- Rougel: 0.3545
- Rougelsum: 0.3543

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Data Size | Epoch Runtime | Accuracy | F1 Macro | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:-------------:|:--------:|:--------:|:------:|:------:|:------:|:---------:|
| No log        | 0     | 0     | 1.1268          | 0         | 13.7862       | 0.3205   | 0.1676   | 0.3208 | 0.0    | 0.3204 | 0.3207    |
| 1.1142        | 1     | 12271 | 1.1005          | 0.0078    | 29.6405       | 0.3545   | 0.1745   | 0.3544 | 0.0    | 0.3545 | 0.3543    |
| 1.1158        | 2     | 24542 | 1.0978          | 0.0156    | 44.0543       | 0.3545   | 0.1745   | 0.3544 | 0.0    | 0.3545 | 0.3543    |
| 1.1067        | 3     | 36813 | 1.0977          | 0.0312    | 73.6568       | 0.3273   | 0.1644   | 0.3273 | 0.0    | 0.3275 | 0.3277    |
| 1.1137        | 4     | 49084 | 1.0979          | 0.0625    | 131.7620      | 0.3545   | 0.1745   | 0.3544 | 0.0    | 0.3545 | 0.3543    |
| 1.1129        | 5     | 61355 | 1.1017          | 0.125     | 248.6076      | 0.3545   | 0.1745   | 0.3544 | 0.0    | 0.3545 | 0.3543    |
| 1.1043        | 6     | 73626 | 1.1031          | 0.25      | 481.5122      | 0.3273   | 0.1644   | 0.3273 | 0.0    | 0.3275 | 0.3277    |
| 1.1049        | 7     | 85897 | 1.1001          | 0.5       | 945.5774      | 0.3545   | 0.1745   | 0.3544 | 0.0    | 0.3545 | 0.3543    |


### Framework versions

- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.3.0
- Tokenizers 0.22.1