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
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