---
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