transformers pytorch longt5 text2text-generation generated_from_trainer summarization dataset:pszemraj/scientific_lay_summarisation-elife-norm license:apache-2.0 model-index autotrain_compatible region:us
---
datasets:
- pszemraj/scientific_lay_summarisation-elife-norm
license: apache-2.0
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
inference: false
model-index:
- name: long-t5-tglobal-xl-scientific_lay_summarisation-elife-norm-16384-summ-v1
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: pszemraj/scientific_lay_summarisation-elife-norm
      type: pszemraj/scientific_lay_summarisation-elife-norm
      split: validation
    metrics:
    - type: rouge
      value: 47.1446
      name: Rouge1
---

# long-t5-tglobal-xl-sci-simplify-elife

This model is a fine-tuned version of [google/long-t5-tglobal-xl](https://huggingface.co/google/long-t5-tglobal-xl) on the pszemraj/scientific_lay_summarisation-elife-norm dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6666
- Rouge1: 47.1446
- Rouge2: 14.2158
- Rougel: 23.3524
- Rougelsum: 44.6063
- Gen Len: 431.22

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

the `pszemraj/scientific_lay_summarisation-elife-norm` dataset, input 16384 tokens then truncate, output 1024 tokens then truncate.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 6963
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.7959        | 1.0   | 543  | 1.6770          | 44.4187 | 12.6752 | 22.4669 | 41.944    | 456.33  |
| 1.7578        | 2.0   | 1086 | 1.6666          | 47.1446 | 14.2158 | 23.3524 | 44.6063   | 431.22  |