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ccdv/
lsg-bart-base-16384-mediasum

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

This model is a fine-tuned version of ccdv/lsg-bart-base-4096-mediasum on the ccdv/mediasum roberta_prepended mediasum 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	Connexions	R1	R2	RL	RLsum
16384	64	Full	256	768	35.31	18.35	31.81	32.47
16384	1	Full	256	768	35.21	18.20	31.73	32.37
16384	64	Global only	256	768	35.22	18.08	31.54	32.21
16384	1	None	256	768	35.17	18.13	31.54	32.20

Reference model:

Length	Global tokens	Fine-tuning	Block Size	Sparsity	Connexions	R1	R2	RL	RLsum
4096	1	-	256	0	768	35.16	18.13	31.54	32.20

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-mediasum, 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: 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