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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnn_dailymail |
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- xsum |
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- samsum |
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- billsum |
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- lytang/MeetingBank-transcript |
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metrics: |
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- rouge |
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model-index: |
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- name: t5_xsum_samsum_billsum_cnn_dailymail |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: cnn_dailymail |
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type: cnn_dailymail |
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config: 3.0.0 |
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split: train |
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args: 3.0.0 |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.2373 |
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license: mit |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: summarization |
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--- |
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# t5_xsum_samsum_billsum_cnn_dailymail |
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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. |
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## Intended Uses & Limitations |
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### Intended Uses |
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- Document summarization: The model is well-suited for summarizing lengthy documents or articles, making it valuable for content curation and information extraction tasks. |
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- Content generation: It can be used to generate concise summaries from input text, which is useful for creating short and informative snippets. |
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### Limitations |
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- Model size: The model's size may require significant computational resources for deployment, limiting its use in resource-constrained environments. |
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- Domain-specific content: While it performs well on general text summarization tasks, its performance may vary when applied to domain-specific content. |
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## Training and Evaluation Data |
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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. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Training results |
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#### samsum |
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| Rouge1 | Rouge2 | RougeL | RougeLsum | |
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|:-------:|:-------:|:-------:|:---------:| |
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| 0.0138 | 0.0002 | 0.0138 | 0.0138 | |
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#### CNN_Dailymail |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
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| 1.8486 | 1.0 | 32300 | 1.6478 | 0.2373 | 0.1086 | 0.1972 | 0.1971 | 18.9674 | |
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### Framework versions |
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- Transformers 4.33.0 |
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- Pytorch 2.0.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.3 |
