🚀 bart-large-tomasg25/scientific_lay_summarisation
該模型可用於科學文本的摘要生成,藉助 Amazon SageMaker 和全新的 Hugging Face 深度學習容器進行訓練,能有效處理科學文本並生成高質量的摘要。
🚀 快速開始
此模型是使用 Amazon SageMaker 和新的 Hugging Face 深度學習容器進行訓練的。
更多信息請查看:
🔧 技術細節
超參數
{
"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
}
💻 使用示例
基礎用法
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)
📚 詳細文檔
結果
屬性 |
詳情 |
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 |
📄 許可證
本項目採用 Apache-2.0 許可證。