---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: SocialScienceBART
  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. -->

# SocialScienceBART

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: 4.6504
- Rouge1: 51.3376
- Rouge2: 18.2656
- Rougel: 36.0279
- Rougelsum: 47.688
- Bertscore Precision: 81.2268
- Bertscore Recall: 83.5394
- Bertscore F1: 82.3632
- Bleu: 0.1466
- Gen Len: 195.1837

## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu   | Gen Len  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.3499        | 0.1314 | 100  | 5.9601          | 44.2626 | 14.89   | 31.4939 | 41.4297   | 78.5226             | 81.7375          | 80.0922      | 0.1200 | 195.1837 |
| 5.7616        | 0.2628 | 200  | 5.5234          | 45.0397 | 15.5203 | 31.9711 | 41.5206   | 77.988              | 82.1682          | 80.0147      | 0.1261 | 195.1837 |
| 5.3197        | 0.3943 | 300  | 5.2264          | 46.0652 | 15.9668 | 32.867  | 42.5272   | 78.4756             | 82.4394          | 80.4011      | 0.1308 | 195.1837 |
| 5.1661        | 0.5257 | 400  | 5.0219          | 45.5622 | 15.8452 | 33.3135 | 42.7801   | 79.6663             | 82.5824          | 81.0931      | 0.1287 | 195.1837 |
| 5.0513        | 0.6571 | 500  | 4.8896          | 45.2597 | 15.7552 | 33.7344 | 42.337    | 79.3705             | 82.7284          | 81.0087      | 0.1287 | 195.1837 |
| 4.8073        | 0.7885 | 600  | 4.7506          | 48.6142 | 17.418  | 35.1837 | 45.2098   | 80.4041             | 83.2297          | 81.7876      | 0.1409 | 195.1837 |
| 4.7293        | 0.9199 | 700  | 4.6504          | 51.3376 | 18.2656 | 36.0279 | 47.688    | 81.2268             | 83.5394          | 82.3632      | 0.1466 | 195.1837 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1