---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- scientific_lay_summarisation
metrics:
- rouge
model-index:
- name: t5-small-scientific_lay_summarisation
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: scientific_lay_summarisation
      type: scientific_lay_summarisation
      config: elife
      split: validation
      args: elife
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.0546
---

<!-- 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. -->

# t5-small-scientific_lay_summarisation

This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on the scientific_lay_summarisation dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0503
- Rouge1: 0.0546
- Rouge2: 0.0154
- Rougel: 0.0461
- Rougelsum: 0.0462
- Gen Len: 19.0

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 272  | 3.1627          | 0.048  | 0.0123 | 0.0402 | 0.0402    | 19.0    |
| 3.6506        | 2.0   | 544  | 3.0881          | 0.0524 | 0.0143 | 0.0441 | 0.0442    | 19.0    |
| 3.6506        | 3.0   | 816  | 3.0586          | 0.0543 | 0.0155 | 0.0461 | 0.0462    | 19.0    |
| 3.2737        | 4.0   | 1088 | 3.0503          | 0.0546 | 0.0154 | 0.0461 | 0.0462    | 19.0    |


### Framework versions

- Transformers 4.27.2
- Pytorch 2.1.0+cu121
- Datasets 2.11.0
- Tokenizers 0.13.3