---
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlnet-case-25
  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-case-25

This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5796
- Precision: 0.9156
- Recall: 0.9067
- F1: 0.9103
- Accuracy: 0.9067

## 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: 224
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 44   | 0.3254          | 0.8710    | 0.8533 | 0.8494 | 0.8533   |
| No log        | 2.0   | 88   | 0.2517          | 0.8653    | 0.8933 | 0.8768 | 0.8933   |
| No log        | 3.0   | 132  | 0.2558          | 0.8497    | 0.8933 | 0.8690 | 0.8933   |
| No log        | 4.0   | 176  | 0.3633          | 0.8782    | 0.8933 | 0.8803 | 0.8933   |
| No log        | 5.0   | 220  | 0.3514          | 0.8978    | 0.9067 | 0.9008 | 0.9067   |
| No log        | 6.0   | 264  | 0.4314          | 0.8922    | 0.8933 | 0.8927 | 0.8933   |
| No log        | 7.0   | 308  | 0.5872          | 0.9079    | 0.8733 | 0.8869 | 0.8733   |
| No log        | 8.0   | 352  | 0.4349          | 0.9081    | 0.9067 | 0.9073 | 0.9067   |
| No log        | 9.0   | 396  | 0.4632          | 0.9188    | 0.9133 | 0.9156 | 0.9133   |
| No log        | 10.0  | 440  | 0.4815          | 0.9156    | 0.9133 | 0.9143 | 0.9133   |
| No log        | 11.0  | 484  | 0.4972          | 0.9156    | 0.9133 | 0.9143 | 0.9133   |
| 0.0831        | 12.0  | 528  | 0.5025          | 0.9317    | 0.92   | 0.9240 | 0.92     |
| 0.0831        | 13.0  | 572  | 0.5105          | 0.9156    | 0.9133 | 0.9143 | 0.9133   |
| 0.0831        | 14.0  | 616  | 0.5225          | 0.9156    | 0.9133 | 0.9143 | 0.9133   |
| 0.0831        | 15.0  | 660  | 0.6110          | 0.9036    | 0.88   | 0.8892 | 0.88     |
| 0.0831        | 16.0  | 704  | 0.5772          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0831        | 17.0  | 748  | 0.5688          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0831        | 18.0  | 792  | 0.5698          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0831        | 19.0  | 836  | 0.5712          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0831        | 20.0  | 880  | 0.5770          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0831        | 21.0  | 924  | 0.5826          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0831        | 22.0  | 968  | 0.5803          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0001        | 23.0  | 1012 | 0.5788          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0001        | 24.0  | 1056 | 0.5795          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |
| 0.0001        | 25.0  | 1100 | 0.5796          | 0.9156    | 0.9067 | 0.9103 | 0.9067   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0