---
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: 0.0053
- Validation Loss: 0.4856
- Train Accuracy: 0.9033
- Epoch: 93

## 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    |
| 0.3848     | 0.4618          | 0.9            | 12    |
| 0.2920     | 0.4516          | 0.8733         | 13    |
| 0.2286     | 0.4097          | 0.8967         | 14    |
| 0.1789     | 0.3951          | 0.9            | 15    |
| 0.1512     | 0.3845          | 0.8933         | 16    |
| 0.1320     | 0.3741          | 0.9067         | 17    |
| 0.1116     | 0.3553          | 0.9067         | 18    |
| 0.0935     | 0.3710          | 0.9            | 19    |
| 0.0886     | 0.3831          | 0.9067         | 20    |
| 0.0723     | 0.3490          | 0.91           | 21    |
| 0.0641     | 0.3448          | 0.91           | 22    |
| 0.0601     | 0.3682          | 0.9            | 23    |
| 0.0590     | 0.3716          | 0.9033         | 24    |
| 0.0491     | 0.3619          | 0.91           | 25    |
| 0.0404     | 0.3728          | 0.9033         | 26    |
| 0.0394     | 0.3624          | 0.91           | 27    |
| 0.0394     | 0.3249          | 0.9167         | 28    |
| 0.0387     | 0.3465          | 0.91           | 29    |
| 0.0456     | 0.3580          | 0.91           | 30    |
| 0.0323     | 0.3645          | 0.9133         | 31    |
| 0.0308     | 0.3633          | 0.9133         | 32    |
| 0.0312     | 0.3658          | 0.9033         | 33    |
| 0.0244     | 0.3621          | 0.9067         | 34    |
| 0.0255     | 0.3705          | 0.9067         | 35    |
| 0.0238     | 0.3618          | 0.9067         | 36    |
| 0.0222     | 0.3603          | 0.9067         | 37    |
| 0.0230     | 0.3678          | 0.9067         | 38    |
| 0.0272     | 0.4125          | 0.9033         | 39    |
| 0.0318     | 0.3973          | 0.91           | 40    |
| 0.0262     | 0.3871          | 0.9067         | 41    |
| 0.0299     | 0.3935          | 0.9033         | 42    |
| 0.0285     | 0.4192          | 0.9067         | 43    |
| 0.0206     | 0.4100          | 0.9133         | 44    |
| 0.0188     | 0.4106          | 0.9067         | 45    |
| 0.0179     | 0.4355          | 0.91           | 46    |
| 0.0151     | 0.4091          | 0.9133         | 47    |
| 0.0138     | 0.4046          | 0.9167         | 48    |
| 0.0128     | 0.4063          | 0.91           | 49    |
| 0.0174     | 0.4197          | 0.91           | 50    |
| 0.0247     | 0.4015          | 0.9133         | 51    |
| 0.0159     | 0.4290          | 0.91           | 52    |
| 0.0161     | 0.4353          | 0.9033         | 53    |
| 0.0163     | 0.4568          | 0.9033         | 54    |
| 0.0153     | 0.4428          | 0.8933         | 55    |
| 0.0145     | 0.4273          | 0.9033         | 56    |
| 0.0129     | 0.4315          | 0.8967         | 57    |
| 0.0107     | 0.4265          | 0.8933         | 58    |
| 0.0173     | 0.4303          | 0.8967         | 59    |
| 0.0150     | 0.4386          | 0.8933         | 60    |
| 0.0166     | 0.4308          | 0.91           | 61    |
| 0.0135     | 0.4533          | 0.8933         | 62    |
| 0.0096     | 0.4507          | 0.9            | 63    |
| 0.0091     | 0.4371          | 0.9033         | 64    |
| 0.0089     | 0.4383          | 0.9033         | 65    |
| 0.0083     | 0.4450          | 0.9033         | 66    |
| 0.0080     | 0.4487          | 0.9033         | 67    |
| 0.0082     | 0.4500          | 0.9            | 68    |
| 0.0077     | 0.4528          | 0.9033         | 69    |
| 0.0075     | 0.4516          | 0.9            | 70    |
| 0.0073     | 0.4474          | 0.9            | 71    |
| 0.0222     | 0.4517          | 0.9            | 72    |
| 0.0082     | 0.4778          | 0.9033         | 73    |
| 0.0072     | 0.4674          | 0.9            | 74    |
| 0.0072     | 0.4641          | 0.8967         | 75    |
| 0.0068     | 0.4537          | 0.9            | 76    |
| 0.0066     | 0.4565          | 0.8967         | 77    |
| 0.0063     | 0.4551          | 0.9033         | 78    |
| 0.0078     | 0.4614          | 0.8967         | 79    |
| 0.0107     | 0.4598          | 0.8967         | 80    |
| 0.0069     | 0.4536          | 0.9            | 81    |
| 0.0107     | 0.4594          | 0.9033         | 82    |
| 0.0072     | 0.4353          | 0.9033         | 83    |
| 0.0112     | 0.4995          | 0.9            | 84    |
| 0.0063     | 0.4875          | 0.8967         | 85    |
| 0.0060     | 0.4859          | 0.9033         | 86    |
| 0.0061     | 0.4804          | 0.9            | 87    |
| 0.0058     | 0.4811          | 0.9033         | 88    |
| 0.0058     | 0.4805          | 0.9033         | 89    |
| 0.0057     | 0.4811          | 0.9033         | 90    |
| 0.0057     | 0.4865          | 0.9033         | 91    |
| 0.0055     | 0.4864          | 0.9033         | 92    |
| 0.0053     | 0.4856          | 0.9033         | 93    |


### Framework versions

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