---
language: en
license: mit
library_name: transformers
tags:
- summarization
- bart
datasets: ccdv/arxiv-summarization
model-index:
- name: BARTxiv
  results:
  - task:
      type: summarization
    dataset:
      name: arxiv-summarization
      type: ccdv/arxiv-summarization
      split: validation
    metrics:
    - type: rouge1
      value: 41.70204016592095
    - type: rouge2
      value: 15.134827404979639
---

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

See the model implementation [here](https://interrsect.web.app).

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the [arxiv-summarization](https://huggingface.co/datasets/ccdv/arxiv-summarization) dataset.
It achieves the following results on the validation set:
- Loss: 0.86
- Rouge1: 41.70
- Rouge2: 15.13
- Rougel: 22.85
- Rougelsum: 37.77

## 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: 1e-6
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adafactor
- num_epochs: 9

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 1.24          | 1.0   | 1073 | 1.24            | 38.32   | 12.80   | 20.55   | 34.50     |
| 1.04          | 2.0   | 2146 | 1.04            | 39.65   | 13.74   | 21.28   | 35.83     |
| 0.979         | 3.0   | 3219 | 0.98            | 40.19   | 14.30   | 21.87   | 36.38     |
| 0.970         | 4.0   | 4292 | 0.97            | 40.87   | 14.44   | 22.14   | 36.89     |
| 0.918         | 5.0   | 5365 | 0.92            | 41.17   | 14.94   | 22.54   | 37.40     |
| 0.901         | 6.0   | 6438 | 0.90            | 41.02   | 14.65   | 22.46   | 37.05     |
| 0.889         | 7.0   | 7511 | 0.89            | 41.32   | 15.09   | 22.64   | 37.42     |
| 0.900         | 8.0   | 8584 | 0 .90           | 41.23   | 15.02   | 22.67   | 37.28     |
| 0.869         | 9.0   | 9657 | 0.87            | 41.70   | 15.13   | 22.85   | 37.77     |

### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.6.1
- Tokenizers 0.13.1