---
library_name: transformers
base_model: mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: fge-robos-qa-model
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# fge-robos-qa-model

This model is a fine-tuned version of [mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es](https://huggingface.co/mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0088
- Model Preparation Time: 0.0077
- Exact: 55.7377
- F1: 82.8805
- Total: 915
- Hasans Exact: 55.7377
- Hasans F1: 82.8805
- Hasans Total: 915
- Best Exact: 55.7377
- Best Exact Thresh: 0.0
- Best F1: 82.8805
- Best F1 Thresh: 0.0

## 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:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Exact   | F1      | Total | Hasans Exact | Hasans F1 | Hasans Total | Best Exact | Best Exact Thresh | Best F1 | Best F1 Thresh |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:-------:|:-------:|:-----:|:------------:|:---------:|:------------:|:----------:|:-----------------:|:-------:|:--------------:|
| No log        | 1.0   | 58   | 0.8963          | 0.0077                 | 54.2077 | 81.4281 | 915   | 54.2077      | 81.4281   | 915          | 54.2077    | 0.0               | 81.4281 | 0.0            |
| No log        | 2.0   | 116  | 0.9578          | 0.0077                 | 54.9727 | 82.3694 | 915   | 54.9727      | 82.3694   | 915          | 54.9727    | 0.0               | 82.3694 | 0.0            |
| No log        | 3.0   | 174  | 1.0088          | 0.0077                 | 55.7377 | 82.8805 | 915   | 55.7377      | 82.8805   | 915          | 55.7377    | 0.0               | 82.8805 | 0.0            |
| No log        | 4.0   | 232  | 1.0865          | 0.0077                 | 54.4262 | 81.7459 | 915   | 54.4262      | 81.7459   | 915          | 54.4262    | 0.0               | 81.7459 | 0.0            |
| No log        | 5.0   | 290  | 1.2034          | 0.0077                 | 53.7705 | 81.5328 | 915   | 53.7705      | 81.5328   | 915          | 53.7705    | 0.0               | 81.5328 | 0.0            |
| No log        | 6.0   | 348  | 1.2822          | 0.0077                 | 54.2077 | 81.9985 | 915   | 54.2077      | 81.9985   | 915          | 54.2077    | 0.0               | 81.9985 | 0.0            |
| No log        | 7.0   | 406  | 1.3357          | 0.0077                 | 54.2077 | 81.7294 | 915   | 54.2077      | 81.7294   | 915          | 54.2077    | 0.0               | 81.7294 | 0.0            |
| No log        | 8.0   | 464  | 1.3738          | 0.0077                 | 54.6448 | 81.9526 | 915   | 54.6448      | 81.9526   | 915          | 54.6448    | 0.0               | 81.9526 | 0.0            |
| 0.4292        | 9.0   | 522  | 1.4215          | 0.0077                 | 54.7541 | 81.7385 | 915   | 54.7541      | 81.7385   | 915          | 54.7541    | 0.0               | 81.7385 | 0.0            |
| 0.4292        | 10.0  | 580  | 1.4342          | 0.0077                 | 53.4426 | 81.3729 | 915   | 53.4426      | 81.3729   | 915          | 53.4426    | 0.0               | 81.3729 | 0.0            |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0