README.md

metadata

language:
  - en
tags:
  - summarization
datasets:
  - scientific_papers
metrics:
  - rouge
model-index:
  - name: ccdv/lsg-bart-base-4096-pubmed
    results: []

This model relies on a custom modeling file, you need to add trust_remote_code=True
See #13467

ccdv/lsg-bart-base-4096-pubmed

This model is a fine-tuned version of ccdv/lsg-bart-base-4096 on the scientific_papers pubmed dataset.
It achieves the following results on the test set:

Length	Sparse Type	Block Size	Sparsity	Connexions	R1	R2	RL	RLsum
4096	Local	256	0	768	47.33	21.67	28.53	43.67
4096	Local	128	0	384	46.84	21.24	28.22	43.15
4096	Pooling	128	4	644	47.07	21.41	28.40	43.36
4096	Stride	128	4	644	47.02	21.46	28.33	43.35
4096	Norm	128	4	644	47.01	21.32	28.26	43.33
4096	LSH	128	4	644	46.92	21.27	28.26	43.26

Model description

The model relies on Local-Sparse-Global attention to handle long sequences:

The model has about ~145 millions parameters (6 encoder layers - 6 decoder layers).
The model is warm started from BART-base, converted to handle long sequences (encoder only) and fine tuned. \

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: 8e-05
• train_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 7.0

Generate hyperparameters

The following hyperparameters were used during generation:

• dataset_name: scientific_papers
• dataset_config_name: pubmed
• eval_batch_size: 8
• early_stopping: True
• ignore_pad_token_for_loss: True
• length_penalty: 2.0
• max_length: 512
• min_length: 128
• num_beams: 5
• num_samples: None
• no_repeat_ngram_size: None
• seed: 123

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.1+cu102
• Datasets 2.1.0
• Tokenizers 0.11.6