|
--- |
|
base_model: dccuchile/distilbert-base-spanish-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
model-index: |
|
- name: Prototipo_3_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_3_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: 1.2540 |
|
- Accuracy: 0.5423 |
|
|
|
## 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: 3e-05 |
|
- train_batch_size: 24 |
|
- eval_batch_size: 24 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 250 |
|
- num_epochs: 5 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
|
|:-------------:|:------:|:----:|:---------------:|:--------:| |
|
| 1.2186 | 0.1778 | 200 | 1.1556 | 0.4803 | |
|
| 1.1345 | 0.3556 | 400 | 1.0663 | 0.525 | |
|
| 1.102 | 0.5333 | 600 | 1.0479 | 0.5293 | |
|
| 1.1325 | 0.7111 | 800 | 1.0483 | 0.5353 | |
|
| 1.1211 | 0.8889 | 1000 | 1.0337 | 0.521 | |
|
| 0.9736 | 1.0667 | 1200 | 1.0006 | 0.5503 | |
|
| 0.9428 | 1.2444 | 1400 | 1.0214 | 0.5523 | |
|
| 0.9095 | 1.4222 | 1600 | 1.0174 | 0.555 | |
|
| 0.9806 | 1.6 | 1800 | 1.0155 | 0.5527 | |
|
| 0.969 | 1.7778 | 2000 | 1.0043 | 0.5547 | |
|
| 0.9112 | 1.9556 | 2200 | 1.0050 | 0.5537 | |
|
| 0.7557 | 2.1333 | 2400 | 1.0496 | 0.5607 | |
|
| 0.8212 | 2.3111 | 2600 | 1.0494 | 0.5597 | |
|
| 0.7695 | 2.4889 | 2800 | 1.0510 | 0.5687 | |
|
| 0.7648 | 2.6667 | 3000 | 1.0513 | 0.5603 | |
|
| 0.8232 | 2.8444 | 3200 | 1.0316 | 0.563 | |
|
| 0.6288 | 3.0222 | 3400 | 1.0883 | 0.5503 | |
|
| 0.6736 | 3.2 | 3600 | 1.1232 | 0.548 | |
|
| 0.682 | 3.3778 | 3800 | 1.1695 | 0.543 | |
|
| 0.6682 | 3.5556 | 4000 | 1.1608 | 0.5427 | |
|
| 0.6516 | 3.7333 | 4200 | 1.1636 | 0.545 | |
|
| 0.6731 | 3.9111 | 4400 | 1.1694 | 0.5403 | |
|
| 0.5388 | 4.0889 | 4600 | 1.2120 | 0.544 | |
|
| 0.5663 | 4.2667 | 4800 | 1.2278 | 0.544 | |
|
| 0.5579 | 4.4444 | 5000 | 1.2439 | 0.538 | |
|
| 0.5216 | 4.6222 | 5200 | 1.2507 | 0.5427 | |
|
| 0.4634 | 4.8 | 5400 | 1.2531 | 0.5393 | |
|
| 0.5359 | 4.9778 | 5600 | 1.2540 | 0.5423 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.1 |
|
- Pytorch 2.2.1+cu121 |
|
- Datasets 2.19.0 |
|
- Tokenizers 0.19.1 |
|
|
