---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
datasets:
- fedora-copr/pep-sum
metrics:
- rouge
model-index:
- name: pep_summarization
  results:
  - task:
      name: Summarization
      type: summarization
    dataset:
      name: fedora-copr/pep-sum
      type: fedora-copr/pep-sum
    metrics:
    - name: Rouge1
      type: rouge
      value: 75.3806
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# pep_summarization

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co./facebook/bart-large-cnn) on the fedora-copr/pep-sum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1242
- Rouge1: 75.3806
- Rouge2: 74.6735
- Rougel: 75.5866
- Rougelsum: 75.5446
- Gen Len: 85.3188

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log        | 1.0   | 69   | 0.0957          | 72.6601 | 71.6824 | 72.6858 | 72.4668   | 95.4493 |
| No log        | 2.0   | 138  | 0.1345          | 75.0063 | 74.0782 | 75.0597 | 74.8943   | 92.0145 |
| No log        | 3.0   | 207  | 0.1412          | 75.3012 | 74.5492 | 75.4246 | 75.324    | 85.4638 |
| No log        | 4.0   | 276  | 0.1089          | 74.8426 | 74.0317 | 74.8939 | 74.8128   | 85.0435 |
| No log        | 5.0   | 345  | 0.1242          | 75.3806 | 74.6735 | 75.5866 | 75.5446   | 85.3188 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0