---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: bart_samsum
  results: []
datasets:
- samsum
pipeline_tag: summarization
---

<!-- 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4947
- Rouge1: 53.3294
- Rouge2: 28.6009
- Rougel: 44.2008
- Rougelsum: 49.2031
- Bleu: 0.0
- Meteor: 0.4887
- Gen Len: 30.1209

## 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
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bleu | Meteor | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:----:|:------:|:-------:|
| 1.3838        | 0.9997 | 1841 | 1.5631          | 52.3252 | 27.2646 | 42.5893 | 48.2397   | 0.0  | 0.4825 | 32.0415 |
| 1.0835        | 2.0    | 3683 | 1.4947          | 53.3294 | 28.6009 | 44.2008 | 49.2031   | 0.0  | 0.4887 | 30.1209 |
| 0.8345        | 2.9997 | 5524 | 1.5956          | 52.1812 | 27.1239 | 42.9864 | 47.6384   | 0.0  | 0.4774 | 30.5446 |
| 0.672         | 4.0    | 7366 | 1.6695          | 52.8148 | 27.4815 | 43.3732 | 48.4633   | 0.0  | 0.4836 | 31.0342 |
| 0.538         | 4.9986 | 9205 | 1.8055          | 52.0988 | 26.762  | 42.5505 | 47.3721   | 0.0  | 0.4738 | 29.8901 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1