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Overview

This model is a fine-tuned version of allenai/led-base-16384 on the MS^2 dataset. The model received as input the background section and the titles and abstracts of up to 25 included studies for each example, concatenated by the "</s>" token. Global attention is applied to the special start token "<s>" and each of the document separator tokens "</s>". The model slightly outperforms the reported results in the original paper: MS2: Multi-Document Summarization of Medical Studies. See the MS2 leaderboard for results on the blind test set.

It achieves the following results on the evaluation set:

• Loss: 3.7602
• Rouge1 Fmeasure Mean: 28.5338
• Rouge2 Fmeasure Mean: 9.5060
• RougeL Fmeasure Mean: 20.9321
• RougeLsum Fmeasure Mean: 24.0998
• Bertscore Hashcode: microsoft/deberta-xlarge-mnli_L40_no-idf_version=0.3.11(hug_trans=4.21.0.dev0)-rescaled_fast-tokenizer
• Bertscore F1 Mean: 22.7619
• Seed: 42
• Model Name Or Path: allenai/led-base-16384
• Doc Sep Token: "</s>"

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 3e-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10
• mixed_precision_training: Native AMP
• label_smoothing_factor: 0.1

Framework versions

• Transformers 4.21.0.dev0
• Pytorch 1.10.0
• Datasets 2.4.0
• Tokenizers 0.12.1