---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: bart-samsum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: train
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.4835
---

<!-- 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-samsum

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5071
- Rouge1: 0.4835
- Rouge2: 0.2546
- Rougel: 0.4128
- Rougelsum: 0.4131
- Gen Len: 17.9817

## 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: 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.8082        | 1.0   | 2947  | 1.5613          | 0.4763 | 0.2412 | 0.4043 | 0.4041    | 17.9332 |
| 1.5609        | 2.0   | 5894  | 1.5206          | 0.4827 | 0.2485 | 0.4082 | 0.4085    | 18.3169 |
| 1.4228        | 3.0   | 8841  | 1.5008          | 0.4851 | 0.2557 | 0.4138 | 0.4137    | 17.9851 |
| 1.3131        | 4.0   | 11788 | 1.5071          | 0.4835 | 0.2546 | 0.4128 | 0.4131    | 17.9817 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3