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---
tags:
- generated_from_trainer
base_model: dccuchile/bert-base-spanish-wwm-uncased
metrics:
- accuracy
model-index:
- name: Prototipo_5_EMI
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. -->
# Prototipo_5_EMI
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:
- Loss: 1.4215
- Accuracy: 0.538
## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.2459 | 0.1481 | 200 | 1.2168 | 0.4493 |
| 1.1445 | 0.2963 | 400 | 1.0823 | 0.512 |
| 1.1117 | 0.4444 | 600 | 1.0979 | 0.5053 |
| 1.0618 | 0.5926 | 800 | 1.0457 | 0.5273 |
| 1.0343 | 0.7407 | 1000 | 1.0219 | 0.537 |
| 1.1239 | 0.8889 | 1200 | 1.0353 | 0.5257 |
| 0.9012 | 1.0370 | 1400 | 1.0637 | 0.5383 |
| 0.86 | 1.1852 | 1600 | 1.0682 | 0.5333 |
| 0.898 | 1.3333 | 1800 | 1.0341 | 0.5483 |
| 0.929 | 1.4815 | 2000 | 1.0437 | 0.5363 |
| 0.9921 | 1.6296 | 2200 | 0.9968 | 0.5473 |
| 0.9776 | 1.7778 | 2400 | 1.0418 | 0.5553 |
| 0.9166 | 1.9259 | 2600 | 0.9874 | 0.5573 |
| 0.703 | 2.0741 | 2800 | 1.0564 | 0.556 |
| 0.8123 | 2.2222 | 3000 | 1.0582 | 0.561 |
| 0.6727 | 2.3704 | 3200 | 1.0942 | 0.5483 |
| 0.6843 | 2.5185 | 3400 | 1.1128 | 0.558 |
| 0.7528 | 2.6667 | 3600 | 1.0823 | 0.5547 |
| 0.7747 | 2.8148 | 3800 | 1.0744 | 0.5497 |
| 0.7471 | 2.9630 | 4000 | 1.0749 | 0.5527 |
| 0.5774 | 3.1111 | 4200 | 1.1422 | 0.552 |
| 0.6105 | 3.2593 | 4400 | 1.2226 | 0.543 |
| 0.573 | 3.4074 | 4600 | 1.2427 | 0.5417 |
| 0.6047 | 3.5556 | 4800 | 1.2403 | 0.537 |
| 0.5334 | 3.7037 | 5000 | 1.2470 | 0.5413 |
| 0.5688 | 3.8519 | 5200 | 1.2585 | 0.5507 |
| 0.4928 | 4.0 | 5400 | 1.2653 | 0.5437 |
| 0.4314 | 4.1481 | 5600 | 1.3419 | 0.541 |
| 0.4556 | 4.2963 | 5800 | 1.3677 | 0.5413 |
| 0.4815 | 4.4444 | 6000 | 1.3912 | 0.5407 |
| 0.4431 | 4.5926 | 6200 | 1.4004 | 0.5347 |
| 0.4312 | 4.7407 | 6400 | 1.4161 | 0.5397 |
| 0.459 | 4.8889 | 6600 | 1.4215 | 0.538 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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