transformers pytorch longt5 text2text-generation generated_from_trainer dataset:pszemraj/scientific_lay_summarisation-plos-norm license:bsd-3-clause license:apache-2.0 model-index autotrain_compatible region:us
---
datasets:
- pszemraj/scientific_lay_summarisation-plos-norm
license:
- bsd-3-clause
- apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
inference: false
model-index:
- name: long-t5-tglobal-xl-16384-book-summary-scientific_lay_summarisation-plos-norm-16384-summ-v1
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: pszemraj/scientific_lay_summarisation-plos-norm
      type: pszemraj/scientific_lay_summarisation-plos-norm
      split: validation
    metrics:
    - type: rouge
      value: 44.3203
      name: Rouge1
---


# long-t5-tglobal-xl-16384-booksci-summary-plos-10k

This model is a fine-tuned version of [pszemraj/long-t5-tglobal-xl-16384-book-summary](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary) on the pszemraj/scientific_lay_summarisation-plos-norm dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5041
- Rouge1: 44.3203
- Rouge2: 11.0576
- Rougel: 22.7584
- Rougelsum: 40.1462
- Gen Len: 256.66

## Model description

Another test of further fine-tuning booksum-based models, this one fine-tuned on the PLOS subset of lay-summaries for about 10k examples input, to make it roughly equivalent to [this checkpoint](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-booksci-summary-v1) fine-tuned on the ELIFE subset for two epochs (also around 10k examples).

## 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: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 165
- 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: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.7715        | 0.28  | 350  | 1.5310          | 43.4729 | 10.4616 | 22.1928 | 39.505    | 260.87  |
| 1.9307        | 0.56  | 700  | 1.5102          | 44.1634 | 10.9336 | 22.3896 | 40.2939   | 253.58  |
| 1.2981        | 0.84  | 1050 | 1.5046          | 44.2728 | 10.8455 | 22.4122 | 40.3019   | 261.29  |

