---
license: mit
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: bart-large-cnn-small-xsum-3epochs
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: xsum
      type: xsum
      config: default
      split: train
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.2948
---

<!-- 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-large-cnn-small-xsum-3epochs

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9215
- Rouge1: 0.2948
- Rouge2: 0.102
- Rougel: 0.2136
- Rougelsum: 0.2237

## 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: 1.4899457508828423e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 16
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.6493        | 0.64  | 8    | 2.3018          | 0.2072 | 0.038  | 0.1369 | 0.1665    |
| 2.1539        | 1.32  | 16   | 2.0677          | 0.2403 | 0.0689 | 0.165  | 0.1833    |
| 1.9295        | 1.96  | 24   | 1.9494          | 0.2779 | 0.0907 | 0.1998 | 0.209     |
| 1.7358        | 2.64  | 32   | 1.9215          | 0.2948 | 0.102  | 0.2136 | 0.2237    |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2