long-t5-tglobal-base-16384-booksci-summary: v1

An experiment investigating transfer learning capabilities by fine-tuning models on different datasets starting from the booksum checkpoint.

Model Details

This model is a fine-tuned version of pszemraj/long-t5-tglobal-base-16384-book-summary on the pszemraj/scientific_lay_summarisation-elife-norm dataset for two epochs.

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-16384-booksci-summary-v1"
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

• This is an initial experiment
• Domain generalization abilities at time of writing are unknown

Training procedure

Note: this model was trained at a lower LR & not till "absolute convergence" with the intention of retaining some of the properties learned from the initial fine-tuning on booksum

Results

It achieves the following results on the evaluation set:

• Loss: 2.3994
• Rouge1: 34.2428
• Rouge2: 4.3644
• Rougel: 12.5332
• Rougelsum: 30.6965
• Gen Len: 294.0249

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• 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: 0.05
• num_epochs: 2.0

Training results