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

# SocialScienceBARTPrincipal

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: 4.8587
- Rouge1: 48.4993
- Rouge2: 14.8435
- Rougel: 33.0264
- Rougelsum: 44.9256
- Bertscore Precision: 80.3517
- Bertscore Recall: 82.7128
- Bertscore F1: 81.5112
- Bleu: 0.1092
- Gen Len: 195.1640

## 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  |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 6.5089        | 0.1314 | 100  | 6.2390          | 39.4898 | 11.0769 | 27.6002 | 36.497    | 75.7798             | 80.6901          | 78.1466      | 0.0800 | 195.1640 |
| 5.9338        | 0.2628 | 200  | 5.7540          | 41.6352 | 11.9524 | 29.0458 | 38.5778   | 77.0272             | 81.1993          | 79.0507      | 0.0882 | 195.1640 |
| 5.6077        | 0.3943 | 300  | 5.4443          | 41.5238 | 12.2762 | 29.4389 | 38.8683   | 77.5496             | 81.3713          | 79.4075      | 0.0894 | 195.1640 |
| 5.3997        | 0.5257 | 400  | 5.2541          | 44.1846 | 13.1247 | 30.5659 | 41.1211   | 78.8697             | 81.8978          | 80.3498      | 0.0962 | 195.1640 |
| 5.1614        | 0.6571 | 500  | 5.1269          | 44.5045 | 13.3887 | 31.1505 | 41.1205   | 78.727              | 82.0655          | 80.3557      | 0.0994 | 195.1640 |
| 5.0558        | 0.7885 | 600  | 4.9610          | 46.7823 | 14.4367 | 32.4159 | 43.2551   | 79.6807             | 82.5047          | 81.0632      | 0.1059 | 195.1640 |
| 4.9749        | 0.9199 | 700  | 4.8587          | 48.4993 | 14.8435 | 33.0264 | 44.9256   | 80.3517             | 82.7128          | 81.5112      | 0.1092 | 195.1640 |


### Framework versions

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