---
library_name: transformers
license: mit
base_model: facebook/bart-large-cnn
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart_large_cnn_samsum_model_10epoch
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bart_large_cnn_samsum_model_10epoch

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5260
- Model Preparation Time: 0.0066
- Rouge1: 0.4165
- Rouge2: 0.1911
- Rougel: 0.3142
- Rougelsum: 0.3143
- Gen Len: 60.615

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 200  | 1.4282          | 0.0066                 | 0.4109 | 0.2008 | 0.3084 | 0.3085    | 59.755  |
| No log        | 2.0   | 400  | 1.5080          | 0.0066                 | 0.4214 | 0.2027 | 0.3175 | 0.3175    | 59.3862 |
| 1.2171        | 3.0   | 600  | 1.5348          | 0.0066                 | 0.4093 | 0.1949 | 0.3071 | 0.307     | 60.2062 |
| 1.2171        | 4.0   | 800  | 1.7114          | 0.0066                 | 0.4092 | 0.1928 | 0.3067 | 0.3066    | 60.38   |
| 0.6518        | 5.0   | 1000 | 1.8757          | 0.0066                 | 0.4149 | 0.1935 | 0.3118 | 0.3117    | 59.5    |
| 0.6518        | 6.0   | 1200 | 2.0521          | 0.0066                 | 0.4126 | 0.1902 | 0.3107 | 0.3108    | 60.335  |
| 0.6518        | 7.0   | 1400 | 2.1551          | 0.0066                 | 0.4138 | 0.1917 | 0.3117 | 0.3115    | 60.1888 |
| 0.3371        | 8.0   | 1600 | 2.4051          | 0.0066                 | 0.4132 | 0.1913 | 0.3116 | 0.3116    | 60.28   |
| 0.3371        | 9.0   | 1800 | 2.4850          | 0.0066                 | 0.4146 | 0.1897 | 0.3129 | 0.3131    | 60.7375 |
| 0.2072        | 10.0  | 2000 | 2.5260          | 0.0066                 | 0.4165 | 0.1911 | 0.3142 | 0.3143    | 60.615  |


### Framework versions

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