summarization

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Transformers >= 4.23.1
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: attn

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:

Generate hyperparameters

The following hyperparameters were used during generation:

Framework versions