---
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: []
---

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# 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 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](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html): 

> 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