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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: SocialScienceBARTMainSections
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. -->
# SocialScienceBARTMainSections
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: 4.5267
- Rouge1: 51.5502
- Rouge2: 19.1289
- Rougel: 36.9981
- Rougelsum: 48.0056
- Bertscore Precision: 81.4667
- Bertscore Recall: 83.8704
- Bertscore F1: 82.647
- Bleu: 0.1571
- Gen Len: 194.5169
## 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 5.9423 | 0.1332 | 100 | 5.8537 | 44.7886 | 15.5028 | 32.2161 | 41.724 | 78.6884 | 82.051 | 80.3281 | 0.1282 | 194.5169 |
| 5.5219 | 0.2664 | 200 | 5.3814 | 45.5534 | 16.1718 | 32.9346 | 42.758 | 79.0971 | 82.3765 | 80.6972 | 0.1323 | 194.5169 |
| 5.1742 | 0.3997 | 300 | 5.0879 | 48.1215 | 17.0033 | 34.0793 | 44.5274 | 79.1451 | 82.8315 | 80.939 | 0.1387 | 194.5169 |
| 5.0337 | 0.5329 | 400 | 4.9042 | 49.2783 | 17.5741 | 34.9739 | 45.4337 | 80.0738 | 83.2616 | 81.6311 | 0.1447 | 194.5169 |
| 4.8596 | 0.6661 | 500 | 4.7692 | 50.3917 | 17.9196 | 35.6188 | 47.0232 | 80.8885 | 83.4241 | 82.1326 | 0.1475 | 194.5169 |
| 4.7917 | 0.7993 | 600 | 4.6321 | 51.7348 | 19.0125 | 36.6567 | 47.9429 | 81.3534 | 83.827 | 82.5677 | 0.1557 | 194.5169 |
| 4.5184 | 0.9326 | 700 | 4.5267 | 51.5502 | 19.1289 | 36.9981 | 48.0056 | 81.4667 | 83.8704 | 82.647 | 0.1571 | 194.5169 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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