transformers tensorboard safetensors bart text2text-generation generated_from_trainer summarization en dataset:samsum base_model:facebook/bart-large-cnn base_model:finetune:facebook/bart-large-cnn license:mit autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/bart-large-cnn
datasets:
- samsum
language:
- en
license: mit
metrics:
- rouge
pipeline_tag: summarization
tags:
- generated_from_trainer
model-index:
- name: bart-large-cnn-finetuned-sst2
  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-large-cnn-finetuned-sst2

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: 0.4287
- Rouge1: 0.4065
- Rouge2: 0.1979
- Rougel: 0.3084
- Rougelsum: 0.3750

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 0.2977        | 1.0   | 920  | 0.3094          | 0.4036 | 0.2071 | 0.3097 | 0.3746    |
| 0.2253        | 2.0   | 1841 | 0.3163          | 0.4067 | 0.2109 | 0.3130 | 0.3769    |
| 0.159         | 3.0   | 2762 | 0.3258          | 0.4108 | 0.2101 | 0.3163 | 0.3796    |
| 0.1091        | 4.0   | 3683 | 0.3680          | 0.4060 | 0.2006 | 0.3069 | 0.3750    |
| 0.0723        | 5.0   | 4600 | 0.4287          | 0.4065 | 0.1979 | 0.3084 | 0.3750    |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2