---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-text_summarization_data
  results: []
language:
- en
pipeline_tag: summarization
---

# led-base-16384-text_summarization_data

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9531
- Rouge1: 43.3689
- Rouge2: 19.9885
- RougeL: 39.9887
- RougeLsum: 40.0679
- Gen Len: 14.0392

## 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/LED%20-%20Text%20Summarization%20-%204%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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | RougeL  | RougeLsum | General Length |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.329         | 1.0   | 1197 | 0.9704          | 42.4111 | 19.8995 | 39.4717 | 39.5449   | 14.254  |
| 0.8367        | 2.0   | 2394 | 0.9425          | 43.1141 | 19.6089 | 39.7533 | 39.8298   | 14.1058 |
| 0.735         | 3.0   | 3591 | 0.9421          | 42.8101 | 19.8281 | 39.617  | 39.6751   | 13.7101 |
| 0.6737        | 4.0   | 4788 | 0.9531          | 43.3689 | 19.9885 | 39.9887 | 40.0679   | 14.0392 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1