allenai/led-base-16384-cochrane

Overview

This model is a fine-tuned version of allenai/led-base-16384 on the Cochrane dataset. The model received as input 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 Cochrane leaderboard for results on the blind test set.

It achieves the following results on the validation set:

• Loss: 4.0216
• Rouge1 Fmeasure Mean: 26.3026
• Rouge2 Fmeasure Mean: 6.0324
• Rougel Fmeasure Mean: 18.1513
• Rougelsum Fmeasure Mean: 22.5031
• Bertscore Hashcode: microsoft/deberta-xlarge-mnli_L40_no-idf_version=0.3.11(hug_trans=4.22.0.dev0)-rescaled_fast-tokenizer
• Bertscore F1 Mean: 20.5937
• 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: 20
• mixed_precision_training: Native AMP
• label_smoothing_factor: 0.1

Framework versions

• Transformers 4.22.0.dev0
• Pytorch 1.12.0
• Datasets 2.4.0
• Tokenizers 0.12.1