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---
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: []
datasets:
- abisee/cnn_dailymail
---
<!-- 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](https://huggingface.co/t5-small) on an **[cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail)** dataset.
It achieves the following results on the evaluation set:
- ***Loss***: 1.6597
- **Rouge_1**: 0.2162
- **Rouge_2**: 0.0943
- **Rouge_l**: 0.1834
- **Rouge_lsum**: 0.1834
- **Generated_Length**: 19.0
## Model description
**Base Model:** *t5-small*, which is a smaller version of the *T5 (Text-to-Text Transfer Transformer) model* developed by ***Google***.
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.
## Intended uses & limitations
* ### Intended Use
* 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:
* **News Summarization:** Quickly summarizing news articles to provide readers with the main points.
* **Document Summarization**: Condensing lengthy reports or research papers into brief overviews.
* **Content Curation**: Helping content creators and curators to generate summaries for newsletters, blogs, or social media posts.
* **Educational Tools**: Assisting students and educators by summarizing academic texts and articles.
* ### Limitations
* While the model is powerful, it does have some limitations:
* **Accuracy**: The summaries generated might not always capture all the key points accurately, especially for complex or nuanced texts.
* **Bias**: The model can inherit biases present in the training data, which might affect the quality and neutrality of the summaries.
* **Context Understanding**: It might struggle with understanding the full context of very long documents, leading to incomplete or misleading summaries.
* **Language and Style**: The model’s output might not always match the desired tone or style, requiring further editing.
* **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)
## Training and evaluation data
The model was trained using the Adam optimizer with a learning rate of **2e-05** over **2 epochs**.
## Training procedure
### 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:|
| No log | 1.0 | 288 | 1.6727 | 0.217 | 0.0949 | 0.1841 | 0.1839 | 19.0 |
| 1.9118 | 2.0 | 576 | 1.6597 | 0.2162 | 0.0943 | 0.1834 | 0.1834 | 19.0 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1 |