---
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_keras_callback
model-index:
- name: vedantjumle/xlnet-1
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->

# vedantjumle/xlnet-1

This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co./xlnet-large-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5132
- Validation Loss: 0.5293
- Train Accuracy: 0.89
- Epoch: 11

## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 6000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 5.1007     | 4.9565          | 0.0133         | 0     |
| 5.0503     | 4.8870          | 0.0367         | 1     |
| 4.9095     | 4.6674          | 0.07           | 2     |
| 4.5990     | 4.1706          | 0.2033         | 3     |
| 4.0403     | 3.4616          | 0.4267         | 4     |
| 3.2648     | 2.6274          | 0.6033         | 5     |
| 2.5315     | 1.8851          | 0.71           | 6     |
| 1.8938     | 1.4084          | 0.8033         | 7     |
| 1.3599     | 1.0397          | 0.84           | 8     |
| 0.9752     | 0.7675          | 0.8667         | 9     |
| 0.6995     | 0.6496          | 0.8667         | 10    |
| 0.5132     | 0.5293          | 0.89           | 11    |


### Framework versions

- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1