---
license: mit
tags:
- generated_from_trainer
datasets:
- pubmed-summarization
metrics:
- rouge
model-index:
- name: bart-finetuned-summarization-pubmed
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: pubmed-summarization
      type: pubmed-summarization
      config: section
      split: validation
      args: section
    metrics:
    - name: Rouge1
      type: rouge
      value: 43.1219
---

<!-- 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-finetuned-summarization-pubmed

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the pubmed-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7193
- Rouge1: 43.1219
- Rouge2: 18.7311
- Rougel: 28.1006
- Rougelsum: 38.0914
- Gen Len: 128.6263

## 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: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 50
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 1.8564        | 1.0   | 2398 | 1.7437          | 43.2294 | 18.867  | 28.2156 | 38.1868   | 128.4766 |
| 1.75          | 2.0   | 4796 | 1.7193          | 43.1219 | 18.7311 | 28.1006 | 38.0914   | 128.6263 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3