---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: 02_ToS-BART
  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. -->

# 02_ToS-BART

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: 0.5697
- Rouge1: 0.6086
- Rouge2: 0.4577
- Rougel: 0.5072
- Rougelsum: 0.5071
- Gen Len: 110.7293

## 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: 3e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| No log        | 1.0   | 360  | 0.5018          | 0.5957 | 0.44   | 0.4873 | 0.4876    | 110.8398 |
| 0.049         | 2.0   | 720  | 0.5468          | 0.5923 | 0.4364 | 0.4812 | 0.4813    | 111.6133 |
| 0.0789        | 3.0   | 1080 | 0.5157          | 0.6035 | 0.4439 | 0.4933 | 0.4934    | 110.1768 |
| 0.0789        | 4.0   | 1440 | 0.5905          | 0.5873 | 0.4279 | 0.4781 | 0.4781    | 110.8343 |
| 0.044         | 5.0   | 1800 | 0.5581          | 0.6046 | 0.4544 | 0.5023 | 0.502     | 110.8674 |
| 0.0231        | 6.0   | 2160 | 0.5697          | 0.6086 | 0.4577 | 0.5072 | 0.5071    | 110.7293 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0