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 4.36.1
Pytorch 1.12.1
Datasets 2.3.2
Tokenizers 0.11.6