---
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. -->

**Transformers >= 4.36.1**\
**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)**

LSG ArXiv [paper](https://arxiv.org/abs/2210.15497). \
Github/conversion script is available at this [link](https://github.com/ccdv-ai/convert_checkpoint_to_lsg).

```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 4.36.1
- Pytorch 1.12.1
- Datasets 2.3.2
- Tokenizers 0.11.6