t5_xsum_samsum_billsum_cnn_dailymail

The t5_xsum_samsum_billsum_cnn_dailymail model is a text summarization model fine-tuned on the t5-base architecture, which is a versatile text-to-text transfer transformer. This powerful model excels at generating abstractive summaries from input text. It has been fine-tuned on multiple datasets, including CNN/Daily Mail (cnn_dailymail), XSum (xsum), SamSum (samsum), BillSum (billsum), and the MeetingBank-transcript dataset by lytang.

Intended Uses & Limitations

Intended Uses

• Document summarization: The model is well-suited for summarizing lengthy documents or articles, making it valuable for content curation and information extraction tasks.
• Content generation: It can be used to generate concise summaries from input text, which is useful for creating short and informative snippets.

Limitations

• Model size: The model's size may require significant computational resources for deployment, limiting its use in resource-constrained environments.
• Domain-specific content: While it performs well on general text summarization tasks, its performance may vary when applied to domain-specific content.

Training and Evaluation Data

The model has been trained on a diverse set of datasets, including CNN/Daily Mail, XSum, SamSum, BillSum, and the MeetingBank-transcript dataset. These datasets provide a wide range of text summarization examples, enabling the model to generalize across various domains and styles of text.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

samsum

CNN_Dailymail

Framework versions

• Transformers 4.33.0
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3