metadata

tags:
  - generated_from_trainer
datasets:
  - allenai/mslr2022
model-index:
  - name: baseline
    results: []

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 seperator tokens "</s>". The model slightly outperforms the reported results in the original paper: MS2: Multi-Document Summarization of Medical Studies.

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