---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- scientific_papers
model-index:
- name: summarise_v3
  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_v3

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   | 2.8968          | 0.0887           | 0.143         | 0.0945          |
| 2.6151        | 0.16  | 20   | 2.6183          | 0.1205           | 0.0854        | 0.0907          |
| 2.5809        | 0.24  | 30   | 2.4685          | 0.1371           | 0.0748        | 0.0911          |
| 2.1297        | 0.32  | 40   | 2.5209          | 0.1481           | 0.092         | 0.1029          |
| 2.8083        | 0.4   | 50   | 2.3871          | 0.1785           | 0.1047        | 0.1217          |
| 3.0703        | 0.48  | 60   | 2.3674          | 0.1576           | 0.0985        | 0.1103          |
| 2.4715        | 0.56  | 70   | 2.3555          | 0.1703           | 0.1036        | 0.1194          |
| 2.4538        | 0.64  | 80   | 2.3411          | 0.1619           | 0.0935        | 0.1108          |
| 2.3046        | 0.72  | 90   | 2.3105          | 0.152            | 0.0975        | 0.1107          |
| 1.7466        | 0.8   | 100  | 2.3416          | 0.1534           | 0.0872        | 0.1038          |
| 2.7695        | 0.88  | 110  | 2.3227          | 0.154            | 0.095         | 0.1081          |
| 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