---
library_name: transformers
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: Bart-CNN-dataset
  results: []
---

<!-- 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-CNN-dataset

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2222
- Rouge1: 0.4398
- Rouge2: 0.1996
- Rougel: 0.2964
- Rougelsum: 0.4096
- Gen Len: 95.364

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 250  | 1.4136          | 0.4361 | 0.2058 | 0.2957 | 0.4075    | 99.678  |
| 1.3139        | 2.0   | 500  | 1.4521          | 0.444  | 0.2085 | 0.3035 | 0.4138    | 90.808  |
| 1.3139        | 3.0   | 750  | 1.5573          | 0.4409 | 0.2046 | 0.2945 | 0.4102    | 100.502 |
| 0.7471        | 4.0   | 1000 | 1.6873          | 0.4429 | 0.205  | 0.2985 | 0.4119    | 96.34   |
| 0.7471        | 5.0   | 1250 | 1.8544          | 0.4395 | 0.2016 | 0.2964 | 0.409     | 100.1   |
| 0.4392        | 6.0   | 1500 | 2.0239          | 0.4407 | 0.2012 | 0.2946 | 0.4085    | 97.476  |
| 0.4392        | 7.0   | 1750 | 2.1492          | 0.4409 | 0.199  | 0.2947 | 0.4101    | 94.41   |
| 0.2886        | 8.0   | 2000 | 2.2222          | 0.4398 | 0.1996 | 0.2964 | 0.4096    | 95.364  |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3