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ccdv/
lsg-bart-base-4096-pubmed

Name: lsg-bart-base-4096-pubmed
Availability: InStock

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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.

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-4096-pubmed", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-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-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	47.37	21.74	28.59	43.67
4096	Local	128	0	384	47.02	21.33	28.34	43.31
4096	Pooling	128	4	644	47.11	21.42	28.43	43.40
4096	Stride	128	4	644	47.16	21.49	28.38	43.44
4096	Block Stride	128	4	644	47.13	21.46	28.39	43.42
4096	Norm	128	4	644	47.09	21.44	28.40	43.36
4096	LSH	128	4	644	47.11	21.41	28.41	43.42

With smaller block size (lower ressources):

Length	Sparse Type	Block Size	Sparsity	Connexions	R1	R2	RL	RLsum
4096	Local	64	0	192	45.74	20.26	27.51	41.99
4096	Local	32	0	96	42.69	17.83	25.62	38.89
4096	Pooling	32	4	160	44.60	19.35	26.83	40.85
4096	Stride	32	4	160	45.52	20.07	27.39	41.75
4096	Block Stride	32	4	160	45.30	19.89	27.22	41.54
4096	Norm	32	4	160	44.30	19.05	26.57	40.47
4096	LSH	32	4	160	44.53	19.27	26.84	40.74

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: 8.0

Generate hyperparameters

The following hyperparameters were used during generation:

dataset_name: scientific_papers
dataset_config_name: pubmed
eval_batch_size: 8
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