---
license: mit
base_model: xlnet/xlnet-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: rubakha/xlnet-new
  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. -->

# rubakha/xlnet-new

This model is a fine-tuned version of [xlnet/xlnet-base-cased](https://huggingface.co./xlnet/xlnet-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1361
- Train Accuracy: 0.934
- Validation Loss: 0.1800
- Validation Accuracy: 0.9340
- Train Precision: 0.9357
- Train Recall: 0.934
- Train F1: 0.9335
- Epoch: 2

## 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': 5000, '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 | Train Accuracy | Validation Loss | Validation Accuracy | Train Precision | Train Recall | Train F1 | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:---------------:|:------------:|:--------:|:-----:|
| 0.6884     | 0.913          | 0.2723          | 0.9130              | 0.9152          | 0.913        | 0.9124   | 0     |
| 0.1980     | 0.9345         | 0.1866          | 0.9345              | 0.9360          | 0.9345       | 0.9341   | 1     |
| 0.1361     | 0.934          | 0.1800          | 0.9340              | 0.9357          | 0.934        | 0.9335   | 2     |


### Framework versions

- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.15.2