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Sponsored by
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pszemraj / long-t5-tglobal-base-sci-simplify-elife
Model's Last Updated: December 29 2025
summarization
Introduction of long-t5-tglobal-base-sci-simplify-elife
Model Details of long-t5-tglobal-base-sci-simplify-elife
long-t5-tglobal-base-sci-simplify: elife subset
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-elife-norm
dataset.
- The variant trained on the PLOS subset can be found here
Usage
It's recommended to use this model with
beam search decoding
. If interested, you can also use the
textsum
util repo to have most of this abstracted out for you:
pip install -U textsum
from textsum.summarize import Summarizer
model_name = "pszemraj/long-t5-tglobal-base-sci-simplify-elife"
summarizer = Summarizer(model_name) # GPU auto-detected
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 and evaluation data
The
elife
subset of the lay summaries dataset. Refer to
pszemraj/scientific_lay_summarisation-elife-norm
Training procedure
Eval results
It achieves the following results on the evaluation set:
- Loss: 1.9990
- Rouge1: 38.5587
- Rouge2: 9.7336
- Rougel: 21.1974
- Rougelsum: 35.9333
- Gen Len: 392.7095
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: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.2995 | 1.47 | 100 | 2.0175 | 35.2501 | 8.2121 | 20.4587 | 32.4494 | 439.7552 |
| 2.2171 | 2.94 | 200 | 1.9990 | 38.5587 | 9.7336 | 21.1974 | 35.9333 | 392.7095 |
Runs of pszemraj long-t5-tglobal-base-sci-simplify-elife on huggingface.co
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More Information About long-t5-tglobal-base-sci-simplify-elife huggingface.co Model
Url of long-t5-tglobal-base-sci-simplify-elife
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Updated:December 29 2025
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