---
license: mit
base_model: facebook/bart-large-xsum
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: validation
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.547
---

<!-- 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-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3852
- Rouge1: 0.547
- Rouge2: 0.2837
- Rougel: 0.4462
- Rougelsum: 0.4454
- Gen Len: 29.72

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.5201        | 0.27  | 500  | 1.4589          | 0.5276 | 0.2694 | 0.4246 | 0.424     | 33.5067 |
| 1.3757        | 0.54  | 1000 | 1.5105          | 0.506  | 0.2566 | 0.415  | 0.4146    | 29.76   |
| 1.3496        | 0.81  | 1500 | 1.4039          | 0.5365 | 0.2759 | 0.4233 | 0.4221    | 29.8    |
| 1.094         | 1.09  | 2000 | 1.4119          | 0.5407 | 0.2827 | 0.4293 | 0.4288    | 29.84   |
| 1.1488        | 1.36  | 2500 | 1.3680          | 0.5275 | 0.2637 | 0.423  | 0.4224    | 26.92   |
| 1.1222        | 1.63  | 3000 | 1.2875          | 0.5369 | 0.2844 | 0.4473 | 0.4463    | 29.2267 |
| 1.1092        | 1.9   | 3500 | 1.3968          | 0.533  | 0.2818 | 0.4354 | 0.4363    | 30.0667 |
| 0.8509        | 2.17  | 4000 | 1.3682          | 0.5306 | 0.2874 | 0.4327 | 0.4331    | 29.1467 |
| 0.9565        | 2.44  | 4500 | 1.3450          | 0.5466 | 0.2782 | 0.4419 | 0.4409    | 29.2133 |
| 0.8496        | 2.72  | 5000 | 1.3768          | 0.5366 | 0.2807 | 0.4359 | 0.4351    | 30.7733 |
| 0.8397        | 2.99  | 5500 | 1.3852          | 0.547  | 0.2837 | 0.4462 | 0.4454    | 29.72   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3