---
library_name: transformers
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: bart-large-cnn-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: 0.4139
---

<!-- 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-samsum

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3028
- Rouge1: 0.4139
- Rouge2: 0.2105
- Rougel: 0.3191
- Rougelsum: 0.3193
- Gen Len: 60.0134

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.9128        | 0.4344 | 100  | 0.3621          | 0.3984 | 0.1999 | 0.3038 | 0.3038    | 60.8888 |
| 0.3205        | 0.8689 | 200  | 0.3097          | 0.4102 | 0.2138 | 0.3186 | 0.3188    | 60.6345 |
| 0.2702        | 1.3033 | 300  | 0.3041          | 0.4159 | 0.211  | 0.3179 | 0.3179    | 60.077  |
| 0.251         | 1.7377 | 400  | 0.2964          | 0.4191 | 0.2154 | 0.3229 | 0.3233    | 59.9022 |
| 0.2262        | 2.1721 | 500  | 0.3055          | 0.4135 | 0.208  | 0.3178 | 0.3179    | 60.4132 |
| 0.1906        | 2.6066 | 600  | 0.3028          | 0.4139 | 0.2105 | 0.3191 | 0.3193    | 60.0134 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1