---
license: apache-2.0
base_model: PlanTL-GOB-ES/roberta-base-bne
tags:
- generated_from_keras_callback
model-index:
- name: lulygavri/rob-conv
  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. -->

# lulygavri/rob-conv

This model is a fine-tuned version of [PlanTL-GOB-ES/roberta-base-bne](https://huggingface.co/PlanTL-GOB-ES/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0787
- Validation Loss: 0.0220
- Train Accuracy: 0.9948
- Train Precision: [0.95822589 0.99925584 0.99829758]
- Train Precision W: 0.9949
- Train Recall: [0.99678112 0.99385686 0.99761824]
- Train Recall W: 0.9948
- Train F1: [0.97712332 0.99654904 0.99795779]
- Train F1 W: 0.9948
- Epoch: 1

## 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': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3964, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 500, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: mixed_float16

### Training results

| Train Loss | Validation Loss | Train Accuracy | Train Precision                    | Train Precision W | Train Recall                       | Train Recall W | Train F1                           | Train F1 W | Epoch |
|:----------:|:---------------:|:--------------:|:----------------------------------:|:-----------------:|:----------------------------------:|:--------------:|:----------------------------------:|:----------:|:-----:|
| 0.0787     | 0.0220          | 0.9948         | [0.95822589 0.99925584 0.99829758] | 0.9949            | [0.99678112 0.99385686 0.99761824] | 0.9948         | [0.97712332 0.99654904 0.99795779] | 0.9948     | 1     |


### Framework versions

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