|
--- |
|
tags: |
|
- generated_from_trainer |
|
base_model: dccuchile/distilbert-base-spanish-uncased |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: Prototipo_4_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_4_EMI |
|
|
|
This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.0406 |
|
- Accuracy: 0.5567 |
|
|
|
## 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: 50 |
|
- eval_batch_size: 50 |
|
- 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 | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:| |
|
| 1.3118 | 0.3704 | 200 | 1.2660 | 0.4457 | |
|
| 1.0828 | 0.7407 | 400 | 1.0757 | 0.5203 | |
|
| 1.0145 | 1.1111 | 600 | 1.0380 | 0.534 | |
|
| 0.9955 | 1.4815 | 800 | 1.0184 | 0.5377 | |
|
| 0.9449 | 1.8519 | 1000 | 0.9944 | 0.5507 | |
|
| 0.9143 | 2.2222 | 1200 | 1.0077 | 0.5533 | |
|
| 0.8609 | 2.5926 | 1400 | 1.0104 | 0.5473 | |
|
| 0.882 | 2.9630 | 1600 | 1.0037 | 0.5507 | |
|
| 0.8049 | 3.3333 | 1800 | 1.0202 | 0.5593 | |
|
| 0.8045 | 3.7037 | 2000 | 1.0234 | 0.5503 | |
|
| 0.78 | 4.0741 | 2200 | 1.0280 | 0.5593 | |
|
| 0.7651 | 4.4444 | 2400 | 1.0411 | 0.5583 | |
|
| 0.7301 | 4.8148 | 2600 | 1.0406 | 0.5567 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.1 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.0 |
|
- Tokenizers 0.19.1 |
|
|
