|
---
|
|
base_model: dccuchile/bert-base-spanish-wwm-cased
|
|
tags:
|
|
- generated_from_trainer
|
|
metrics:
|
|
- accuracy
|
|
- f1
|
|
model-index:
|
|
- name: ABL_trad_j
|
|
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. -->
|
|
|
|
# ABL_trad_j
|
|
|
|
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset.
|
|
It achieves the following results on the evaluation set:
|
|
- Loss: 2.6432
|
|
- Accuracy: 0.6883
|
|
- F1: 0.6865
|
|
|
|
## 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: 1e-05
|
|
- train_batch_size: 8
|
|
- eval_batch_size: 8
|
|
- seed: 42
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
|
- lr_scheduler_type: linear
|
|
- num_epochs: 32
|
|
|
|
### Training results
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|
|
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
|
|
| 0.9532 | 1.0 | 1500 | 0.9116 | 0.5825 | 0.5793 |
|
|
| 0.8601 | 2.0 | 3000 | 0.8433 | 0.6033 | 0.6016 |
|
|
| 0.7962 | 3.0 | 4500 | 0.8150 | 0.6275 | 0.6252 |
|
|
| 0.7633 | 4.0 | 6000 | 0.7969 | 0.635 | 0.6334 |
|
|
| 0.7153 | 5.0 | 7500 | 0.7825 | 0.6492 | 0.6483 |
|
|
| 0.678 | 6.0 | 9000 | 0.7910 | 0.6408 | 0.6392 |
|
|
| 0.6336 | 7.0 | 10500 | 0.7772 | 0.6608 | 0.6606 |
|
|
| 0.5981 | 8.0 | 12000 | 0.7863 | 0.6617 | 0.6605 |
|
|
| 0.5455 | 9.0 | 13500 | 0.7954 | 0.6658 | 0.6657 |
|
|
| 0.4972 | 10.0 | 15000 | 0.8206 | 0.6633 | 0.6623 |
|
|
| 0.4823 | 11.0 | 16500 | 0.8442 | 0.6683 | 0.6673 |
|
|
| 0.4258 | 12.0 | 18000 | 0.8966 | 0.6742 | 0.6734 |
|
|
| 0.4182 | 13.0 | 19500 | 0.9327 | 0.6767 | 0.6761 |
|
|
| 0.3588 | 14.0 | 21000 | 0.9780 | 0.6717 | 0.6689 |
|
|
| 0.3576 | 15.0 | 22500 | 1.0288 | 0.6833 | 0.6828 |
|
|
| 0.3252 | 16.0 | 24000 | 1.0873 | 0.6842 | 0.6836 |
|
|
| 0.3104 | 17.0 | 25500 | 1.1417 | 0.685 | 0.6847 |
|
|
| 0.2691 | 18.0 | 27000 | 1.2447 | 0.6842 | 0.6827 |
|
|
| 0.2559 | 19.0 | 28500 | 1.3480 | 0.6825 | 0.6816 |
|
|
| 0.2522 | 20.0 | 30000 | 1.4782 | 0.6867 | 0.6859 |
|
|
| 0.2234 | 21.0 | 31500 | 1.5748 | 0.6833 | 0.6815 |
|
|
| 0.1954 | 22.0 | 33000 | 1.7041 | 0.69 | 0.6897 |
|
|
| 0.1979 | 23.0 | 34500 | 1.8398 | 0.6808 | 0.6789 |
|
|
| 0.176 | 24.0 | 36000 | 1.9141 | 0.6867 | 0.6860 |
|
|
| 0.1862 | 25.0 | 37500 | 2.0105 | 0.6883 | 0.6881 |
|
|
| 0.1409 | 26.0 | 39000 | 2.1345 | 0.685 | 0.6840 |
|
|
| 0.1527 | 27.0 | 40500 | 2.2039 | 0.6858 | 0.6853 |
|
|
| 0.1474 | 28.0 | 42000 | 2.2990 | 0.6933 | 0.6920 |
|
|
| 0.1428 | 29.0 | 43500 | 2.3780 | 0.6883 | 0.6878 |
|
|
| 0.1348 | 30.0 | 45000 | 2.4859 | 0.6858 | 0.6839 |
|
|
| 0.1046 | 31.0 | 46500 | 2.5546 | 0.6825 | 0.6801 |
|
|
| 0.1147 | 32.0 | 48000 | 2.6432 | 0.6883 | 0.6865 |
|
|
|
|
|
|
### Framework versions
|
|
|
|
- Transformers 4.37.2
|
|
- Pytorch 2.1.0+cu121
|
|
- Datasets 2.16.1
|
|
- Tokenizers 0.15.1
|
|
|
