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---
library_name: transformers
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: PRE-xlnet-large-cased-finetuned-augmentation
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. -->
# PRE-xlnet-large-cased-finetuned-augmentation
This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co./xlnet-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3312
- F1: 0.7328
- Roc Auc: 0.8488
- Accuracy: 0.7773
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.34 | 1.0 | 389 | 0.3108 | 0.1424 | 0.5799 | 0.5386 |
| 0.2957 | 2.0 | 778 | 0.2733 | 0.3070 | 0.6420 | 0.5637 |
| 0.2697 | 3.0 | 1167 | 0.2241 | 0.4578 | 0.6995 | 0.6525 |
| 0.2248 | 4.0 | 1556 | 0.1944 | 0.5903 | 0.7595 | 0.7239 |
| 0.1689 | 5.0 | 1945 | 0.1793 | 0.6729 | 0.8127 | 0.7561 |
| 0.1052 | 6.0 | 2334 | 0.1961 | 0.6682 | 0.7962 | 0.7600 |
| 0.0964 | 7.0 | 2723 | 0.2035 | 0.6728 | 0.7989 | 0.7613 |
| 0.0885 | 8.0 | 3112 | 0.2315 | 0.7185 | 0.8404 | 0.7593 |
| 0.0497 | 9.0 | 3501 | 0.2608 | 0.7264 | 0.8476 | 0.7593 |
| 0.0411 | 10.0 | 3890 | 0.2688 | 0.7212 | 0.8363 | 0.7831 |
| 0.0182 | 11.0 | 4279 | 0.3081 | 0.7300 | 0.8558 | 0.7709 |
| 0.0186 | 12.0 | 4668 | 0.3179 | 0.7216 | 0.8452 | 0.7754 |
| 0.0131 | 13.0 | 5057 | 0.3312 | 0.7328 | 0.8488 | 0.7773 |
| 0.0069 | 14.0 | 5446 | 0.3464 | 0.7272 | 0.8472 | 0.7716 |
| 0.0069 | 15.0 | 5835 | 0.3522 | 0.7316 | 0.8481 | 0.7793 |
| 0.0027 | 16.0 | 6224 | 0.3555 | 0.7303 | 0.8500 | 0.7773 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
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