---
library_name: transformers
license: mit
base_model: xlnet-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Finetuning_XLNET_Paraphrase_Classification
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Finetuning_XLNET_Paraphrase_Classification

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: 0.7478
- Accuracy: 0.8775
- F1: 0.8777

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 459  | 0.4274          | 0.8260   | 0.8126 |
| 0.5099        | 2.0   | 918  | 0.3347          | 0.8578   | 0.8547 |
| 0.3271        | 3.0   | 1377 | 0.5607          | 0.875    | 0.8710 |
| 0.2297        | 4.0   | 1836 | 0.7238          | 0.8701   | 0.8702 |
| 0.1346        | 5.0   | 2295 | 0.7478          | 0.8775   | 0.8777 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3