---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: summarise_v8
  results: []
---
![SGH logo.png](https://s3.amazonaws.com/moonup/production/uploads/1667143139655-631feef1124782a19eff4243.png)

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the SGH news articles and summaries dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8163
- Rouge2 Precision: 0.3628
- Rouge2 Recall: 0.3589
- Rouge2 Fmeasure: 0.3316

## Model description

This model was created to generate summaries of news articles.

## Intended uses & limitations

The model takes up to maximum article length of 768 tokens and generates a summary of maximum length of 512 tokens, and minimum length of 100 tokens.

## Training and evaluation data

This model was trained on 100+ articles and summaries from SGH.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 1.5952        | 0.23  | 10   | 1.0414          | 0.2823           | 0.3908        | 0.3013          |
| 1.8116        | 0.47  | 20   | 0.9171          | 0.3728           | 0.273         | 0.3056          |
| 1.6289        | 0.7   | 30   | 0.8553          | 0.3284           | 0.2892        | 0.291           |
| 1.5074        | 0.93  | 40   | 0.8163          | 0.3628           | 0.3589        | 0.3316          |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 1.2.1
- Tokenizers 0.12.1