|
--- |
|
license: apache-2.0 |
|
base_model: t5-small |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: results_t5base |
|
results: [] |
|
pipeline_tag: summarization |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# results_t5base |
|
|
|
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3660 |
|
- Rouge1: 0.904 |
|
- Rouge2: 0.8349 |
|
- Rougel: 0.8863 |
|
- Gen Len: 237.7528 |
|
|
|
## 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: 0.0001 |
|
- 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 |
|
- lr_scheduler_warmup_steps: 500 |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Gen Len | |
|
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:--------:| |
|
| 0.6675 | 0.8969 | 200 | 0.5012 | 0.8797 | 0.7929 | 0.8578 | 236.6854 | |
|
| 0.5426 | 1.7937 | 400 | 0.4133 | 0.8937 | 0.8192 | 0.8751 | 237.7101 | |
|
| 0.2768 | 2.6906 | 600 | 0.3971 | 0.8984 | 0.8262 | 0.8797 | 237.7551 | |
|
| 0.4136 | 3.5874 | 800 | 0.3864 | 0.9001 | 0.8295 | 0.8824 | 237.7483 | |
|
| 0.3067 | 4.4843 | 1000 | 0.3815 | 0.9011 | 0.8307 | 0.8833 | 237.7506 | |
|
| 0.4425 | 5.3812 | 1200 | 0.3735 | 0.9015 | 0.8319 | 0.884 | 237.7528 | |
|
| 0.4285 | 6.2780 | 1400 | 0.3720 | 0.9026 | 0.8334 | 0.885 | 237.7528 | |
|
| 0.3025 | 7.1749 | 1600 | 0.3687 | 0.9039 | 0.8345 | 0.8859 | 237.7528 | |
|
| 0.2699 | 8.0717 | 1800 | 0.3681 | 0.9034 | 0.8341 | 0.8857 | 237.7528 | |
|
| 0.4072 | 8.9686 | 2000 | 0.3657 | 0.9039 | 0.8349 | 0.8862 | 237.7528 | |
|
| 0.4555 | 9.8655 | 2200 | 0.3660 | 0.904 | 0.8349 | 0.8863 | 237.7528 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.2 |
|
- Pytorch 2.1.2 |
|
- Datasets 2.19.1 |
|
- Tokenizers 0.19.1 |
