|
--- |
|
base_model: MMG/mlm-spanish-roberta-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- accuracy |
|
- precision |
|
- recall |
|
- f1 |
|
model-index: |
|
- name: roberta-finetuned-intention-prediction-es |
|
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. --> |
|
|
|
# roberta-finetuned-intention-prediction-es |
|
|
|
This model is a fine-tuned version of [MMG/mlm-spanish-roberta-base](https://huggingface.co/MMG/mlm-spanish-roberta-base) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.9097 |
|
- Accuracy: 0.6918 |
|
- Precision: 0.6953 |
|
- Recall: 0.6918 |
|
- F1: 0.6848 |
|
|
|
## 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: 16 |
|
- eval_batch_size: 16 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 20 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
|
| 2.2985 | 1.0 | 102 | 1.7435 | 0.4970 | 0.4378 | 0.4970 | 0.4215 | |
|
| 1.3399 | 2.0 | 204 | 1.4205 | 0.5828 | 0.5872 | 0.5828 | 0.5624 | |
|
| 0.8893 | 3.0 | 306 | 1.2699 | 0.6393 | 0.6276 | 0.6393 | 0.6192 | |
|
| 0.5691 | 4.0 | 408 | 1.3327 | 0.6515 | 0.6604 | 0.6515 | 0.6417 | |
|
| 0.3837 | 5.0 | 510 | 1.3836 | 0.6592 | 0.6710 | 0.6592 | 0.6528 | |
|
| 0.2543 | 6.0 | 612 | 1.4253 | 0.6641 | 0.6703 | 0.6641 | 0.6528 | |
|
| 0.1669 | 7.0 | 714 | 1.5317 | 0.6650 | 0.6795 | 0.6650 | 0.6546 | |
|
| 0.1139 | 8.0 | 816 | 1.5939 | 0.6725 | 0.6754 | 0.6725 | 0.6615 | |
|
| 0.0805 | 9.0 | 918 | 1.6987 | 0.6594 | 0.6696 | 0.6594 | 0.6518 | |
|
| 0.0578 | 10.0 | 1020 | 1.6960 | 0.6793 | 0.6782 | 0.6793 | 0.6690 | |
|
| 0.0374 | 11.0 | 1122 | 1.7590 | 0.6824 | 0.6877 | 0.6824 | 0.6729 | |
|
| 0.03 | 12.0 | 1224 | 1.7425 | 0.6842 | 0.6859 | 0.6842 | 0.6785 | |
|
| 0.0183 | 13.0 | 1326 | 1.8165 | 0.6830 | 0.6846 | 0.6830 | 0.6774 | |
|
| 0.0152 | 14.0 | 1428 | 1.8348 | 0.6866 | 0.6927 | 0.6866 | 0.6799 | |
|
| 0.0109 | 15.0 | 1530 | 1.8562 | 0.6940 | 0.6967 | 0.6940 | 0.6855 | |
|
| 0.0097 | 16.0 | 1632 | 1.8766 | 0.6889 | 0.6947 | 0.6889 | 0.6833 | |
|
| 0.0073 | 17.0 | 1734 | 1.8745 | 0.6920 | 0.6948 | 0.6920 | 0.6851 | |
|
| 0.0062 | 18.0 | 1836 | 1.8944 | 0.6895 | 0.6919 | 0.6895 | 0.6825 | |
|
| 0.0057 | 19.0 | 1938 | 1.9103 | 0.6936 | 0.6984 | 0.6936 | 0.6867 | |
|
| 0.0052 | 20.0 | 2040 | 1.9097 | 0.6918 | 0.6953 | 0.6918 | 0.6848 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
|
|
