| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: flan-t5-base-text_summarization_data |
| results: [] |
| language: |
| - en |
| pipeline_tag: summarization |
| --- |
| |
| # flan-t5-base-text_summarization_data |
|
|
| This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.7386 |
| - Rouge1: 43.6615 |
| - Rouge2: 20.349 |
| - Rougel: 40.1032 |
| - Rougelsum: 40.1589 |
| - Gen Len: 14.6434 |
|
|
| ## Model description |
|
|
| This is a text summarization model. |
|
|
| For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Text%20Summarization/Text-Summarized%20Data%20-%20Comparison/Flan-T5%20-%20Text%20Summarization%20-%201%20Epoch.ipynb |
| |
| ## Intended uses & limitations |
| |
| This model is intended to demonstrate my ability to solve a complex problem using technology. |
| |
| ## Training and evaluation data |
| |
| Dataset Source: https://www.kaggle.com/datasets/cuitengfeui/textsummarization-data |
| |
| ## 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: 1 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| |
| | 2.0287 | 1.0 | 1197 | 1.7386 | 43.6615 | 20.349 | 40.1032 | 40.1589 | 14.6434 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.26.1 |
| - Pytorch 1.12.1 |
| - Datasets 2.9.0 |
| - Tokenizers 0.12.1 |
| |
| |
| ## License Notice |
| This model is a fine-tuned derivative of a pretrained model. |
| Users must comply with the original model license. |
| |
| |
| ## Dataset Notice |
| This model was fine-tuned on third-party datasets which may have separate licenses or usage restrictions. |
