README.md

---
library_name: transformers
license: mit
base_model: xlnet/xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlnet-large-cased-deu-DAPT-finetuned-10-epochs
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlnet-large-cased-deu-DAPT-finetuned-10-epochs

This model is a fine-tuned version of [xlnet/xlnet-large-cased](https://huggingface.co/xlnet/xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4404
- F1: 0.0249
- Roc Auc: 0.5066
- Accuracy: 0.2678

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1     | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.458         | 1.0   | 95   | 0.4404          | 0.0249 | 0.5066  | 0.2678   |
| 0.4665        | 2.0   | 190  | 0.4676          | 0.0    | 0.5     | 0.2493   |
| 0.4473        | 3.0   | 285  | 0.4604          | 0.0    | 0.5     | 0.2493   |
| 0.4491        | 4.0   | 380  | 0.4544          | 0.0    | 0.5     | 0.2493   |
| 0.4379        | 5.0   | 475  | 0.4544          | 0.0    | 0.5     | 0.2493   |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0