---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
model-index:
- name: summarise_v9
  results: []
---
![SGH logo.png](https://s3.amazonaws.com/moonup/production/uploads/1667382308985-631feef1124782a19eff4243.png)
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3650
- Rouge1 Precision: 0.4673
- Rouge1 Recall: 0.4135
- Rouge1 Fmeasure: 0.4263
- Rouge2 Precision: 0.1579
- Rouge2 Recall: 0.1426
- Rouge2 Fmeasure: 0.1458
- Rougel Precision: 0.2245
- Rougel Recall: 0.2008
- Rougel Fmeasure: 0.2061
- Rougelsum Precision: 0.2245
- Rougelsum Recall: 0.2008
- Rougelsum Fmeasure: 0.2061

## Model description

This model was created to generate summaries of news articles.

## Intended uses & limitations

The model takes up to maximum article length of 3072 tokens and generates a summary of maximum length of 512 tokens, and minimum length of 100 tokens.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 Precision | Rouge1 Recall | Rouge1 Fmeasure | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | Rougel Precision | Rougel Recall | Rougel Fmeasure | Rougelsum Precision | Rougelsum Recall | Rougelsum Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:----------------:|:-------------:|:---------------:|:-------------------:|:----------------:|:------------------:|
| 2.8095        | 0.16  | 10   | 2.5393          | 0.287            | 0.5358        | 0.3674          | 0.1023           | 0.1917        | 0.1311          | 0.1374           | 0.2615        | 0.1771          | 0.1374              | 0.2615           | 0.1771             |
| 2.6056        | 0.32  | 20   | 2.4752          | 0.5005           | 0.3264        | 0.3811          | 0.1663           | 0.1054        | 0.1249          | 0.2582           | 0.1667        | 0.1957          | 0.2582              | 0.1667           | 0.1957             |
| 2.5943        | 0.48  | 30   | 2.4422          | 0.4615           | 0.3833        | 0.4047          | 0.1473           | 0.1273        | 0.1321          | 0.2242           | 0.1885        | 0.1981          | 0.2242              | 0.1885           | 0.1981             |
| 2.4842        | 0.64  | 40   | 2.4186          | 0.4675           | 0.3829        | 0.4081          | 0.1581           | 0.1294        | 0.1384          | 0.2286           | 0.187         | 0.1995          | 0.2286              | 0.187            | 0.1995             |
| 2.4454        | 0.8   | 50   | 2.3990          | 0.467            | 0.408         | 0.4222          | 0.1633           | 0.1429        | 0.1477          | 0.2294           | 0.2008        | 0.2076          | 0.2294              | 0.2008           | 0.2076             |
| 2.3622        | 0.96  | 60   | 2.3857          | 0.4567           | 0.3898        | 0.41            | 0.1433           | 0.1233        | 0.1295          | 0.2205           | 0.1876        | 0.1976          | 0.2205              | 0.1876           | 0.1976             |
| 2.4034        | 1.13  | 70   | 2.3835          | 0.4515           | 0.4304        | 0.4294          | 0.1526           | 0.1479        | 0.1459          | 0.2183           | 0.209         | 0.2078          | 0.2183              | 0.209            | 0.2078             |
| 2.2612        | 1.29  | 80   | 2.3804          | 0.455            | 0.4193        | 0.4236          | 0.1518           | 0.1429        | 0.1427          | 0.2177           | 0.2025        | 0.2037          | 0.2177              | 0.2025           | 0.2037             |
| 2.2563        | 1.45  | 90   | 2.3768          | 0.4821           | 0.391         | 0.4196          | 0.1652           | 0.1357        | 0.144           | 0.2385           | 0.1929        | 0.2069          | 0.2385              | 0.1929           | 0.2069             |
| 2.243         | 1.61  | 100  | 2.3768          | 0.4546           | 0.4093        | 0.4161          | 0.1552           | 0.1402        | 0.1422          | 0.2248           | 0.2016        | 0.2052          | 0.2248              | 0.2016           | 0.2052             |
| 2.2505        | 1.77  | 110  | 2.3670          | 0.4625           | 0.4189        | 0.4262          | 0.1606           | 0.1485        | 0.1493          | 0.2301           | 0.2098        | 0.2119          | 0.2301              | 0.2098           | 0.2119             |
| 2.2453        | 1.93  | 120  | 2.3650          | 0.4673           | 0.4135        | 0.4263          | 0.1579           | 0.1426        | 0.1458          | 0.2245           | 0.2008        | 0.2061          | 0.2245              | 0.2008           | 0.2061             |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.6.2.dev0
- Tokenizers 0.12.1