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-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 Sparsity Connexions R1 R2 RL RLsum
16384 64 Full 256 0 768 35.31 18.35 31.81 32.47
16384 1 Full 256 0 768 35.21 18.20 31.73 32.37
16384 64 Global only 256 0 768 35.22 18.08 31.54 32.21
16384 1 None 256 0 768 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: attn

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:

Generate hyperparameters

The following hyperparameters were used during generation:

Framework versions