led-base-16384-finetuned-govreport

led-base-16384-finetuned-govreport

This model is a fine-tuned version of allenai/led-base-16384 on the pszemraj/govreport-summarization-8192 dataset. It achieves the following results on the evaluation set:

• Loss: 1.2887

The rouge metrics calculations were processed later down the line (final notebook can be found HERE).

It achieved the following results on the validation set:

• Rouge1: 50.3574
• Rouge2: 20.0448
• Rougel: 22.2156
• Rougelsum: 22.2156

It achieved the following results on the test set:

• Rouge1: 52.6378
• Rouge2: 22.2130
• Rougel: 23.5898
• Rougelsum: 23.5898

Model description

As described in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan, Allenai's Longformer Encoder-Decoder (LED) was initialized from bart-base since both models share the exact same architecture. To be able to process 16K tokens, bart-base's position embedding matrix was simply copied 16 times.

This model is especially interesting for long-range summarization and question answering.

Intended uses & limitations

pszemraj/govreport-summarization-8192 is a pre-processed version of the dataset ccdv/govreport-summarization, which is a dataset for summarization of long documents adapted from this repository and this paper.

The Allenai's LED model was fine-tuned to this dataset, allowing the summarization of documents up to 16384 tokens.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.0
• Datasets 2.1.0
• Tokenizers 0.13.3