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---
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
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