transformers pytorch bart text2text-generation sagemaker summarization en dataset:tomasg25/scientific_lay_summarisation license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- tomasg25/scientific_lay_summarisation
language: en
license: apache-2.0
tags:
- sagemaker
- bart
- summarization
model-index:
- name: bart-large-tomasg25/scientific_lay_summarisation
  results:
  - task:
      type: abstractive-text-summarization
      name: Abstractive Text Summarization
    dataset:
      name: tomasg25/scientific_lay_summarisation
      type: plos
    metrics:
    - type: rogue-1
      value: 42.621
      name: Validation ROGUE-1
    - type: rogue-2
      value: 21.9825
      name: Validation ROGUE-2
    - type: rogue-l
      value: 33.034
      name: Validation ROGUE-L
    - type: rogue-1
      value: 41.3174
      name: Test ROGUE-1
    - type: rogue-2
      value: 20.8716
      name: Test ROGUE-2
    - type: rogue-l
      value: 32.1337
      name: Test ROGUE-L
---
## `bart-large-tomasg25/scientific_lay_summarisation`
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
- [🤗 Transformers Documentation: Amazon SageMaker](https://huggingface.co/transformers/sagemaker.html)
- [Example Notebooks](https://github.com/huggingface/notebooks/tree/master/sagemaker)
- [Amazon SageMaker documentation for Hugging Face](https://docs.aws.amazon.com/sagemaker/latest/dg/hugging-face.html)
- [Python SDK SageMaker documentation for Hugging Face](https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/index.html)
- [Deep Learning Container](https://github.com/aws/deep-learning-containers/blob/master/available_images.md#huggingface-training-containers)
## Hyperparameters
    {
    "cache_dir": "opt/ml/input",
    "dataset_config_name": "plos",
    "dataset_name": "tomasg25/scientific_lay_summarisation",
    "do_eval": true,
    "do_predict": true,
    "do_train": true,
    "fp16": true,
    "learning_rate": 5e-05,
    "model_name_or_path": "facebook/bart-large",
    "num_train_epochs": 1,
    "output_dir": "/opt/ml/model",
    "per_device_eval_batch_size": 4,
    "per_device_train_batch_size": 4,
    "predict_with_generate": true,
    "seed": 7
}
## Usage
    from transformers import pipeline
    summarizer = pipeline("summarization", model="sambydlo/bart-large-tomasg25/scientific_lay_summarisation")
    article = "Food production is a major driver of greenhouse gas (GHG) emissions, water and land use, and dietary risk factors are contributors to non-communicable diseases. Shifts in dietary patterns can therefore potentially provide benefits for both the environment and health. However, there is uncertainty about the magnitude of these impacts, and the dietary changes necessary to achieve them. We systematically review the evidence on changes in GHG emissions, land use, and water use, from shifting current dietary intakes to environ- mentally sustainable dietary patterns. We find 14 common sustainable dietary patterns across reviewed studies, with reductions as high as 70–80% of GHG emissions and land use, and 50% of water use (with medians of about 20–30% for these indicators across all studies) possible by adopting sustainable dietary patterns. Reductions in environmental footprints were generally proportional to the magnitude of animal-based food restriction. Dietary shifts also yielded modest benefits in all-cause mortality risk. Our review reveals that environmental and health benefits are possible by shifting current Western diets to a variety of more sustainable dietary patterns."
    summarizer(article)
## Results
| key | value |
| --- | ----- |
| eval_rouge1 | 41.3889 |
| eval_rouge2 | 13.3641 |
| eval_rougeL | 24.3154 |
| eval_rougeLsum | 36.612 |
| test_rouge1 | 41.4786 |
| test_rouge2 | 13.3787 |
| test_rougeL | 24.1558 |
| test_rougeLsum | 36.7723 |
