transformers tf roberta token-classification generated_from_keras_callback base_model:roberta-base license:mit autotrain_compatible endpoints_compatible region:us
---
license: mit
base_model: roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: hugomssantos/roberta-base-kaggle
  results: []
---

<!-- This model card has been generated automatically according to the information Keras had access to. You should
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# hugomssantos/roberta-base-kaggle

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0158
- Validation Loss: 0.0106
- Train Precision: 0.9132
- Train Recall: 0.9612
- Train F1: 0.9366
- Train Accuracy: 0.9972
- 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 414, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.2683     | 0.0314          | 0.8             | 0.7978       | 0.7989   | 0.9896         | 0     |
| 0.0287     | 0.0132          | 0.9105          | 0.9584       | 0.9339   | 0.9959         | 1     |
| 0.0158     | 0.0106          | 0.9132          | 0.9612       | 0.9366   | 0.9972         | 2     |


### Framework versions

- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
