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---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flan-t5-base-text_summarization_data_6_epochs
results: []
language:
- en
pipeline_tag: summarization
---
# flan-t5-base-text_summarization_data_6_epochs
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base).
It achieves the following results on the evaluation set:
- Loss: 1.6783
- Rouge1: 43.5994
- Rouge2: 20.4446
- Rougel: 40.132
- Rougelsum: 40.1692
- Gen Len: 14.5837
## 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-%206%20Epochs.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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | RougeL | RougeLsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.0079 | 1.0 | 1174 | 1.7150 | 43.4218 | 19.8984 | 40.0059 | 40.0582 | 14.5011 |
| 1.9122 | 2.0 | 2348 | 1.7020 | 44.0374 | 20.5756 | 40.5915 | 40.5683 | 14.617 |
| 1.8588 | 3.0 | 3522 | 1.6881 | 43.9498 | 20.5633 | 40.4656 | 40.5116 | 14.4528 |
| 1.8243 | 4.0 | 4696 | 1.6812 | 43.6024 | 20.4845 | 40.1784 | 40.2211 | 14.5075 |
| 1.7996 | 5.0 | 5870 | 1.6780 | 43.6652 | 20.553 | 40.2209 | 40.2651 | 14.5236 |
| 1.7876 | 6.0 | 7044 | 1.6783 | 43.5994 | 20.4446 | 40.132 | 40.1692 | 14.5837 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2 |