---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-CNN-ML
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: cnn_dailymail
      type: cnn_dailymail
      config: 3.0.0
      split: test
      args: 3.0.0
    metrics:
    - name: Rouge1
      type: rouge
      value: 44.4382
---

<!-- 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-finetuned-CNN-ML

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1137
- Rouge1: 44.4382
- Rouge2: 20.686
- Rougel: 29.9355
- Rougelsum: 41.4113
- Gen Len: 93.846

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.0341        | 1.0   | 1000 | 1.5412          | 43.0331 | 20.1656 | 29.6298 | 39.9858   | 83.22   |
| 0.6416        | 2.0   | 2000 | 1.8461          | 44.2294 | 20.5043 | 29.6298 | 41.1457   | 93.366  |
| 0.3766        | 3.0   | 3000 | 2.1137          | 44.4382 | 20.686  | 29.9355 | 41.4113   | 93.846  |


### Framework versions

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