readme: ---
  tags:
  - summarization
  - summary
  - booksum
  - long-document
  - long-form
  - lsg
  datasets:
  - kmfoda/booksum
  metrics:
  - rouge
  model-index:
  - name: ccdv/lsg-bart-base-4096-booksum
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  **This model relies on a custom modeling file, you need to add trust_remote_code=True**\
  **See [\#13467](https://github.com/huggingface/transformers/pull/13467)**
  
  ```python
  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](https://huggingface.co/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
  