---
license: mit
base_model: xlnet-large-cased
tags:
- generated_from_keras_callback
model-index:
- name: vedantjumle/indo-ml-final-test-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/indo-ml-final-test-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: 0.7254
- Validation Loss: 0.6440
- Train Accuracy: 0.88
- Epoch: 9

## 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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 5.1411     | 5.0487          | 0.01           | 0     |
| 5.0561     | 4.8924          | 0.0267         | 1     |
| 4.8362     | 4.4063          | 0.2033         | 2     |
| 4.2361     | 3.4961          | 0.45           | 3     |
| 3.3575     | 2.5506          | 0.6167         | 4     |
| 2.5070     | 1.8625          | 0.74           | 5     |
| 1.8507     | 1.3431          | 0.8067         | 6     |
| 1.3367     | 0.9980          | 0.84           | 7     |
| 0.9787     | 0.7747          | 0.86           | 8     |
| 0.7254     | 0.6440          | 0.88           | 9     |


### Framework versions

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