Model save

README.md

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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: 0.4544
+- F1: 0.0
+- Roc Auc: 0.5
+- Accuracy: 0.2493
+
+## 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

model.safetensors

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