---
license: mit
base_model: facebook/bart-large-xsum
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: bert_large_xsum_samsum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: test
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.5083
---

<!-- 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. -->

# bert_large_xsum_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: 1.9030
- Rouge1: 0.5083
- Rouge2: 0.2528
- Rougel: 0.41
- Rougelsum: 0.4105
- Gen Len: 29.0183

## 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: 4
- total_train_batch_size: 16
- 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 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 41   | 1.6008          | 0.4779 | 0.2349 | 0.4058 | 0.4056    | 21.1037 |
| No log        | 2.0   | 82   | 1.5804          | 0.5104 | 0.2526 | 0.4242 | 0.4239    | 24.689  |
| No log        | 3.0   | 123  | 1.7310          | 0.5148 | 0.253  | 0.4162 | 0.4155    | 28.0793 |
| No log        | 4.0   | 164  | 1.7974          | 0.5019 | 0.2443 | 0.4127 | 0.4125    | 25.189  |
| No log        | 5.0   | 205  | 1.9030          | 0.5083 | 0.2528 | 0.41   | 0.4105    | 29.0183 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1