---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
model-index:
- name: summarise_v5
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# summarise_v5

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: 2.3252
- Rouge2 Precision: 0.1458
- Rouge2 Recall: 0.1306
- Rouge2 Fmeasure: 0.1343

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### 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: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 2.6266        | 0.13  | 10   | 2.4604          | 0.1021           | 0.179         | 0.124           |
| 2.4818        | 0.27  | 20   | 2.4122          | 0.1402           | 0.1422        | 0.1345          |
| 2.3451        | 0.4   | 30   | 2.3846          | 0.1631           | 0.1177        | 0.1307          |
| 2.4462        | 0.53  | 40   | 2.3584          | 0.1671           | 0.1175        | 0.133           |
| 2.443         | 0.67  | 50   | 2.3395          | 0.1444           | 0.1359        | 0.1344          |
| 2.3822        | 0.8   | 60   | 2.3377          | 0.1517           | 0.1411        | 0.1395          |
| 2.4304        | 0.93  | 70   | 2.3252          | 0.1458           | 0.1306        | 0.1343          |


### Framework versions

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