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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: t5-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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model-index: |
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- name: cnn_news_summary_model_trained_on_reduced_data |
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results: [] |
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datasets: |
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- abisee/cnn_dailymail |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# cnn_news_summary_model_trained_on_reduced_data |
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an **[cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail)** dataset. |
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It achieves the following results on the evaluation set: |
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- ***Loss***: 1.6597 |
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- **Rouge_1**: 0.2162 |
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- **Rouge_2**: 0.0943 |
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- **Rouge_l**: 0.1834 |
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- **Rouge_lsum**: 0.1834 |
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- **Generated_Length**: 19.0 |
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## Model description |
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**Base Model:** *t5-small*, which is a smaller version of the *T5 (Text-to-Text Transfer Transformer) model* developed by ***Google***. |
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This model can be particularly useful if you need to quickly summarize large volumes of text, making it easier to digest and understand key information. |
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## Intended uses & limitations |
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* ### Intended Use |
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* The model is designed for **text summarization**, which involves condensing long pieces of text into shorter, more digestible summaries. Here are some specific use cases: |
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* **News Summarization:** Quickly summarizing news articles to provide readers with the main points. |
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* **Document Summarization**: Condensing lengthy reports or research papers into brief overviews. |
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* **Content Curation**: Helping content creators and curators to generate summaries for newsletters, blogs, or social media posts. |
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* **Educational Tools**: Assisting students and educators by summarizing academic texts and articles. |
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* ### Limitations |
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* While the model is powerful, it does have some limitations: |
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* **Accuracy**: The summaries generated might not always capture all the key points accurately, especially for complex or nuanced texts. |
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* **Bias**: The model can inherit biases present in the training data, which might affect the quality and neutrality of the summaries. |
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* **Context Understanding**: It might struggle with understanding the full context of very long documents, leading to incomplete or misleading summaries. |
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* **Language and Style**: The model’s output might not always match the desired tone or style, requiring further editing. |
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* **Data Dependency**: Performance can vary depending on the quality and nature of the input data. It performs best on data similar to its training set (news articles) |
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## Training and evaluation data |
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The model was trained using the Adam optimizer with a learning rate of **2e-05** over **2 epochs**. |
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## Training procedure |
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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: 16 |
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- eval_batch_size: 16 |
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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: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:| |
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| No log | 1.0 | 288 | 1.6727 | 0.217 | 0.0949 | 0.1841 | 0.1839 | 19.0 | |
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| 1.9118 | 2.0 | 576 | 1.6597 | 0.2162 | 0.0943 | 0.1834 | 0.1834 | 19.0 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.0.0 |
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- Tokenizers 0.19.1 |
