---
license: mit
base_model: facebook/bart-large-cnn
tags:
- summarization
- generated_from_trainer
datasets:
- scientific_papers
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-scientific-articles
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: scientific_papers
      type: scientific_papers
      config: pubmed
      split: train
      args: pubmed
    metrics:
    - name: Rouge1
      type: rouge
      value: 33.8477
---

<!-- 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. -->

# bart-large-cnn-finetuned-scientific-articles

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the scientific_papers dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6456
- Rouge1: 33.8477
- Rouge2: 11.8866
- Rougel: 20.1038
- Rougelsum: 30.5011

## 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: 5.6e-05
- train_batch_size: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 3.3695        | 1.0   | 56   | 2.8464          | 32.1056 | 10.3835 | 18.7541 | 29.2623   |
| 2.7639        | 2.0   | 112  | 2.6667          | 31.2657 | 10.758  | 18.9862 | 28.3279   |
| 2.5169        | 3.0   | 168  | 2.6219          | 33.226  | 11.4766 | 19.5923 | 30.0664   |
| 2.2985        | 4.0   | 224  | 2.6029          | 32.8562 | 11.5606 | 19.8616 | 29.7606   |
| 2.0851        | 5.0   | 280  | 2.6456          | 33.8477 | 11.8866 | 20.1038 | 30.5011   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1