long-t5-tglobal-base-sci-simplify

Exploring how well long-document models trained on "lay summaries" of scientific papers generalize.

A lay summary is a summary of a research paper or scientific study that is written in plain language, without the use of technical jargon, and is designed to be easily understood by non-experts.

Model description

This model is a fine-tuned version of google/long-t5-tglobal-base on the pszemraj/scientific_lay_summarisation-plos-norm dataset for two epochs.

• The variant trained on the ELIFE subset can be found here

Usage

It's recommended to use this model with beam search decoding . If you are interested, you can also use the textsum util repo to have most of this abstracted for you:

Install with pip :

pip install -U textsum

Use in python:

from textsum.summarize import Summarizer

summarizer = Summarizer('pszemraj/long-t5-tglobal-base-sci-simplify')
text = "put the text you don't want to read here"
summary = summarizer.summarize_string(text)
print(summary)

Intended uses & limitations

• Ability to generalize outside of the dataset domain (pubmed/bioscience type papers) has to be evaluated.

Training procedure

Eval results

It achieves the following results on the evaluation set:

• Loss: 1.6778
• Rouge1: 49.1475
• Rouge2: 18.9281
• Rougel: 26.9893
• Rougelsum: 45.0973
• Gen Len: 399.4125

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0004
• train_batch_size: 4
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.01
• num_epochs: 2.0

Training results