---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-xsum-finetuned-natural-questions
  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-xsum-finetuned-natural-questions

This model is a fine-tuned version of [facebook/bart-large-xsum](https://huggingface.co/facebook/bart-large-xsum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2729
- Rouge1: 19.7211
- Rouge2: 17.4272
- Rougel: 19.0681
- Rougelsum: 19.3677

## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log        | 0.99  | 34   | 0.2562          | 17.9806 | 15.2059 | 16.807  | 17.5533   |
| No log        | 1.99  | 68   | 0.1845          | 14.6261 | 10.494  | 13.0132 | 13.8392   |
| No log        | 2.98  | 102  | 0.2171          | 17.3737 | 14.7893 | 16.5485 | 16.8383   |
| No log        | 4.0   | 137  | 0.3474          | 17.6187 | 14.727  | 16.5614 | 17.1476   |
| No log        | 4.99  | 171  | 0.3462          | 17.7103 | 15.1403 | 16.9424 | 17.3123   |
| 0.1255        | 5.99  | 205  | 0.3355          | 19.2782 | 16.5525 | 18.4283 | 18.8422   |
| 0.1255        | 6.98  | 239  | 0.2281          | 19.8816 | 17.4387 | 19.238  | 19.552    |
| 0.1255        | 7.94  | 272  | 0.2729          | 19.7211 | 17.4272 | 19.0681 | 19.3677   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3