metadata
language: en
tags:
- generated_from_trainer
datasets:
- allenai/mslr2022
base_model: allenai/led-base-16384
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 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