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---
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: 0.0138
- Validation Loss: 0.4046
- Train Accuracy: 0.9167
- Epoch: 48
## 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 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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