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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
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