|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: t5-small |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model: cnn_dailymail |
|
model-index: |
|
- name: cnn_news_summary_model_trained_on_reduced_data |
|
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. --> |
|
|
|
# cnn_news_summary_model_trained_on_reduced_data |
|
|
|
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: 1.6040 |
|
- Rouge1: 0.2183 |
|
- Rouge2: 0.0945 |
|
- Rougel: 0.1842 |
|
- Rougelsum: 0.1843 |
|
- Generated Length: 19.0 |
|
|
|
## 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: 3 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:| |
|
| No log | 1.0 | 431 | 1.6239 | 0.2171 | 0.0934 | 0.1825 | 0.1827 | 19.0 | |
|
| 1.9203 | 2.0 | 862 | 1.6075 | 0.2167 | 0.0936 | 0.1826 | 0.1828 | 19.0 | |
|
| 1.822 | 3.0 | 1293 | 1.6040 | 0.2183 | 0.0945 | 0.1842 | 0.1843 | 19.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.4.1+cu121 |
|
- Datasets 3.0.0 |
|
- Tokenizers 0.19.1 |
|
|
