---
base_model: dccuchile/bert-base-spanish-wwm-uncased
tags:
- generated_from_keras_callback
model-index:
- name: lulygavri/sub2-trans
  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/sub2-trans

This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0341
- Validation Loss: 0.0805
- Train Accuracy: 0.9762
- Train Precision: [0.96558916 0.99786325]
- Train Precision W: 0.9769
- Train Recall: [0.99892125 0.934     ]
- Train Recall W: 0.9762
- Train F1: [0.98197243 0.96487603]
- Train F1 W: 0.9760
- Epoch: 5

## 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': 3436, '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: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Train Precision         | Train Precision W | Train Recall            | Train Recall W | Train F1                | Train F1 W | Epoch |
|:----------:|:---------------:|:--------------:|:-----------------------:|:-----------------:|:-----------------------:|:--------------:|:-----------------------:|:----------:|:-----:|
| 0.2010     | 0.7379          | 0.7400         | [0.71450617 0.99236641] | 0.8119            | [0.99892125 0.26      ] | 0.7400         | [0.83310841 0.41204437] | 0.6856     | 1     |
| 0.1127     | 0.4136          | 0.7982         | [0.76339654 0.9953271 ] | 0.8447            | [0.99892125 0.426     ] | 0.7982         | [0.86542056 0.59663866] | 0.7712     | 2     |
| 0.0818     | 0.1851          | 0.9411         | [0.91691395 1.        ] | 0.9460            | [1.    0.832]           | 0.9411         | [0.95665635 0.90829694] | 0.9397     | 3     |
| 0.0511     | 0.1053          | 0.9671         | [0.9526749 0.9978022]   | 0.9685            | [0.99892125 0.908     ] | 0.9671         | [0.97525013 0.95078534] | 0.9667     | 4     |
| 0.0341     | 0.0805          | 0.9762         | [0.96558916 0.99786325] | 0.9769            | [0.99892125 0.934     ] | 0.9762         | [0.98197243 0.96487603] | 0.9760     | 5     |


### Framework versions

- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2