cnn_news_summary_model_trained_on_reduced_data

<!-- 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 on 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

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Tokenizers 0.19.1