---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- scientific_papers
model-index:
- name: longformer_summarise
  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. -->

# longformer_summarise

This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3003
- Rouge2 Precision: 0.1654
- Rouge2 Recall: 0.0966
- Rouge2 Fmeasure: 0.1118

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 2.909         | 0.08  | 10   | 2.8969          | 0.09             | 0.1439        | 0.0953          |
| 2.615         | 0.16  | 20   | 2.6182          | 0.1232           | 0.0865        | 0.0924          |
| 2.581         | 0.24  | 30   | 2.4687          | 0.1357           | 0.0733        | 0.09            |
| 2.1294        | 0.32  | 40   | 2.5215          | 0.1495           | 0.0932        | 0.1044          |
| 2.8083        | 0.4   | 50   | 2.3870          | 0.1794           | 0.1054        | 0.1224          |
| 3.0704        | 0.48  | 60   | 2.3676          | 0.1572           | 0.0989        | 0.1108          |
| 2.4716        | 0.56  | 70   | 2.3554          | 0.1707           | 0.1039        | 0.1198          |
| 2.454         | 0.64  | 80   | 2.3411          | 0.1619           | 0.0943        | 0.1115          |
| 2.3046        | 0.72  | 90   | 2.3105          | 0.1547           | 0.0965        | 0.1116          |
| 1.7467        | 0.8   | 100  | 2.3417          | 0.1551           | 0.0877        | 0.1046          |
| 2.7696        | 0.88  | 110  | 2.3226          | 0.1543           | 0.0954        | 0.1085          |
| 2.4999        | 0.96  | 120  | 2.3003          | 0.1654           | 0.0966        | 0.1118          |


### Framework versions

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