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-16384-pubmed", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-16384-pubmed", 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-16384-pubmed

This model is a fine-tuned version of ccdv/lsg-bart-base-4096-pubmed on the scientific_papers pubmed dataset.
The model is converted to handle 16384 long sequences and fine-tuned accordingly during 1 epoch.
It achieves the following results on the test set:

Length	Global tokens	Fine-tuning	Block Size	Sparsity	Connexions	R1	R2	RL	RLsum
16384	64	Full	256	0	768	48.32	22.52	29.36	44.57
16384	1	Full	256	0	768	48.26	22.53	29.40	44.51
16384	64	Global only	256	0	768	48.12	20.46	29.34	44.40
16384	1	None	256	0	768	48.03	22.42	29.28	44.32

Reference model:

Length	Global tokens	Fine-tuning	Block Size	Sparsity	Connexions	R1	R2	RL	RLsum
4096	1	-	256	0	768	47.37	21.74	28.59	43.67

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 ccdv/lsg-bart-base-4096-pubmed, 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: 1.0

Generate hyperparameters

The following hyperparameters were used during generation:

• dataset_name: scientific_papers
• dataset_config_name: pubmed
• eval_batch_size: 4
• eval_samples: 6658
• early_stopping: True
• ignore_pad_token_for_loss: True
• length_penalty: 2.0
• max_length: 512
• min_length: 128
• 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