Transformers >= 4.36.1
This model relies on a custom modeling file, you need to add trust_remote_code=True
See #13467

LSG ArXiv paper.
Github/conversion script is available at this link.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-4096-mediasum", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-mediasum", trust_remote_code=True)

text = "Replace by what you want."
pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer, device=0)
generated_text = pipe(
  text, 
  truncation=True, 
  max_length=64, 
  no_repeat_ngram_size=7,
  num_beams=2,
  early_stopping=True
  )

ccdv/lsg-bart-base-4096-mediasum

This model is a fine-tuned version of ccdv/lsg-bart-base-4096 on the ccdv/mediasum roberta_prepended 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	35.16	18.13	31.54	32.20
4096	Local	128	0	384	34.16	17.61	30.75	31.41
4096	Pooling	128	4	644	34.52	17.71	31.01	31.67
4096	Stride	128	4	644	35.05	18.11	31.47	32.13
4096	Block Stride	128	4	644	34.72	17.81	31.13	31.82
4096	Norm	128	4	644	34.75	17.86	31.10	31.77
4096	LSH	128	4	644	34.54	17.81	31.05	31.71

With smaller block size (lower ressources):

Length	Sparse Type	Block Size	Sparsity	Connexions	R1	R2	RL	RLsum
4096	Local	64	0	192	32.55	16.66	29.36	30.00
4096	Local	32	0	96	30.98	15.41	27.84	28.46
4096	Pooling	32	4	160	31.84	16.02	28.68	29.30
4096	Stride	32	4	160	32.67	16.68	29.47	30.10
4096	Block Stride	32	4	160	32.51	16.64	29.33	29.94
4096	Norm	32	4	160	32.44	16.48	29.20	29.79
4096	LSH	32	4	160	31.79	16.04	28.67	29.31

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
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 6.0

Generate hyperparameters

The following hyperparameters were used during generation:

• dataset_name: ccdv/mediasum
• dataset_config_name: roberta_prepended
• eval_batch_size: 8
• eval_samples: 10000
• early_stopping: True
• ignore_pad_token_for_loss: True
• length_penalty: 2.0
• max_length: 128
• min_length: 3
• num_beams: 5
• 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