---
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: 34.2136
---

<!-- 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: 2.6416
- Rouge1: 34.2136
- Rouge2: 11.6215
- Rougel: 20.2516
- Rougelsum: 30.6019

## 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          | 32.133  | 10.3816 | 18.7538 | 29.311    |
| 2.7639        | 2.0   | 112  | 2.6667          | 31.5794 | 10.8708 | 19.2408 | 28.6171   |
| 2.517         | 3.0   | 168  | 2.6220          | 33.1806 | 11.2477 | 19.7199 | 30.1012   |
| 2.2989        | 4.0   | 224  | 2.6031          | 32.7604 | 10.9356 | 19.4766 | 29.6503   |
| 2.0883        | 5.0   | 280  | 2.6416          | 34.2136 | 11.6215 | 20.2516 | 30.6019   |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1