metadata
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: cnn_news_summary_model_trained_on_reduced_data
results: []
cnn_news_summary_model_trained_on_reduced_data
This model is a fine-tuned version of t5-small on an cnn_daily_mail dataset. It achieves the following results on the evaluation set:
- Loss: 1.6040
- Rouge1: 0.2183
- Rouge2: 0.0946
- Rougel: 0.1843
- Rougelsum: 0.1842
- Generated Length: 19.0
Model Description
The developers of the Text-To-Text Transfer Transformer (T5) write:
With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.
T5-Small is the checkpoint with 60 million parameters.
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 | 0.1827 | 0.1827 | 19.0 |
| 1.9203 | 2.0 | 862 | 1.6075 | 0.2166 | 0.0937 | 0.1828 | 0.1827 | 19.0 |
| 1.822 | 3.0 | 1293 | 1.6040 | 0.2183 | 0.0946 | 0.1843 | 0.1842 | 19.0 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Tokenizers 0.19.1