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

This model is a fine-tuned version of ccdv/lsg-bart-base-4096 on the kmfoda/booksum kmfoda--booksum dataset. It achieves the following results on the evaluation set:

• eval_loss: 3.2654
• eval_rouge1: 33.9468
• eval_rouge2: 6.7034
• eval_rougeL: 16.7879
• eval_rougeLsum: 31.7677
• eval_gen_len: 427.6918
• eval_runtime: 2910.3841
• eval_samples_per_second: 0.492
• eval_steps_per_second: 0.062
• eval_samples: 1431

Model description

More information needed

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: 30.0

Generate hyperparameters

The following hyperparameters were used during generation:

• dataset_name: kmfoda/booksum
• dataset_config_name: kmfoda--booksum
• eval_batch_size: 8
• eval_samples: 1431
• 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 4.23.1
• Pytorch 1.12.1
• Datasets 2.3.2
• Tokenizers 0.11.6