|
--- |
|
license: apache-2.0 |
|
tags: |
|
- summarization |
|
- generated_from_trainer |
|
model-index: |
|
- name: t5-small-bueno-tfg |
|
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. --> |
|
|
|
# t5-small-bueno-tfg |
|
|
|
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 3.3428 |
|
- Rouge2 Precision: 0.0931 |
|
- Rouge2 Recall: 0.0781 |
|
- Rouge2 Fmeasure: 0.0848 |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 1 |
|
- eval_batch_size: 1 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 4 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 2 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |
|
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| |
|
| 7.2339 | 0.27 | 10 | 4.7620 | 0.0453 | 0.0225 | 0.03 | |
|
| 4.3192 | 0.53 | 20 | 4.0073 | 0.0589 | 0.0336 | 0.0427 | |
|
| 3.9608 | 0.8 | 30 | 3.6537 | 0.0866 | 0.0623 | 0.0722 | |
|
| 3.7992 | 1.07 | 40 | 3.4747 | 0.091 | 0.0708 | 0.0795 | |
|
| 3.7694 | 1.33 | 50 | 3.3968 | 0.0898 | 0.0721 | 0.0799 | |
|
| 3.5839 | 1.6 | 60 | 3.3600 | 0.0992 | 0.0813 | 0.089 | |
|
| 3.5573 | 1.87 | 70 | 3.3428 | 0.0931 | 0.0781 | 0.0848 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.4 |
|
- Pytorch 1.13.1+cu116 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.2 |
|
|
