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This model relies on a custom modeling file, you need to add trust_remote_code=True
See #13467

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline

tokenizer = AutoTokenizer.from_pretrained("ccdv/lsg-bart-base-4096-multinews", trust_remote_code=True)
model = AutoModelForSeq2SeqLM.from_pretrained("ccdv/lsg-bart-base-4096-multinews", 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-multinews

This model is a fine-tuned version of ccdv/lsg-bart-base-4096 on the multi_news default 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 47.10 18.94 25.22 43.13
4096 Local 128 0 384 46.73 18.79 25.13 42.76
4096 Pooling 128 4 644 46.83 18.87 25.23 42.86
4096 Stride 128 4 644 46.83 18.68 24.98 42.88
4096 Block Stride 128 4 644 46.83 18.72 25.06 42.88
4096 Norm 128 4 644 46.74 18.60 24.93 42.79
4096 LSH 128 4 644 46.74 18.82 25.19 42.77

With smaller block size (lower ressources):

Length Sparse Type Block Size Sparsity Connexions R1 R2 RL RLsum
4096 Local 64 0 192 45.61 17.91 24.54 41.65
4096 Local 32 0 96 43.50 16.36 23.45 39.61
4096 Pooling 32 4 160 44.77 17.31 24.16 40.86
4096 Stride 32 4 160 45.29 17.81 24.45 41.40
4096 Block Stride 32 4 160 45.39 17.86 24.51 41.43
4096 Norm 32 4 160 44.65 17.25 24.09 40.76
4096 LSH 32 4 160 44.44 17.20 24.00 40.57

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:

  • 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: 12.0

Generate hyperparameters

The following hyperparameters were used during generation:

  • dataset_name: multi_news
  • dataset_config_name: default
  • eval_batch_size: 8
  • eval_samples: 5622
  • early_stopping: True
  • ignore_pad_token_for_loss: True
  • length_penalty: 2.0
  • max_length: 320
  • min_length: 32
  • 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
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Dataset used to train ccdv/lsg-bart-base-4096-multinews