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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: HealthScienceBARTMainSections
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. -->
# HealthScienceBARTMainSections
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: 3.8753
- Rouge1: 57.7808
- Rouge2: 23.8942
- Rougel: 42.2385
- Rougelsum: 54.2817
- Bertscore Precision: 83.4992
- Bertscore Recall: 85.0665
- Bertscore F1: 84.2729
- Bleu: 0.1888
- Gen Len: 234.2871
## 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 |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:|
| 5.7855 | 0.0835 | 100 | 5.6539 | 48.5675 | 17.0972 | 33.6039 | 45.6212 | 80.1089 | 82.0896 | 81.0838 | 0.1318 | 234.2871 |
| 5.2884 | 0.1671 | 200 | 5.1333 | 51.1112 | 18.5297 | 35.438 | 47.6657 | 80.5123 | 82.7897 | 81.6311 | 0.1454 | 234.2871 |
| 4.9931 | 0.2506 | 300 | 4.8074 | 52.1485 | 19.5231 | 36.7572 | 48.8489 | 81.0844 | 83.2739 | 82.1612 | 0.1534 | 234.2871 |
| 4.6538 | 0.3342 | 400 | 4.5657 | 52.6901 | 20.3073 | 37.8168 | 49.4253 | 81.3851 | 83.5259 | 82.438 | 0.1580 | 234.2871 |
| 4.435 | 0.4177 | 500 | 4.3963 | 54.6727 | 20.9559 | 39.1501 | 51.7226 | 82.5534 | 83.8521 | 83.1954 | 0.1618 | 234.2871 |
| 4.4327 | 0.5013 | 600 | 4.2367 | 55.3497 | 21.849 | 39.8267 | 51.9765 | 82.6498 | 84.2237 | 83.4265 | 0.1698 | 234.2871 |
| 4.2704 | 0.5848 | 700 | 4.1312 | 56.2031 | 22.5317 | 40.6962 | 52.8736 | 82.966 | 84.4887 | 83.7178 | 0.1762 | 234.2871 |
| 4.2211 | 0.6684 | 800 | 4.0373 | 56.1405 | 22.9558 | 41.2482 | 52.772 | 82.9397 | 84.6224 | 83.7695 | 0.1800 | 234.2871 |
| 4.0727 | 0.7519 | 900 | 3.9672 | 57.5881 | 23.6311 | 41.712 | 53.9676 | 83.2595 | 84.8583 | 84.0486 | 0.1850 | 234.2871 |
| 4.0741 | 0.8355 | 1000 | 3.9182 | 57.2156 | 23.6916 | 42.074 | 53.7327 | 83.3537 | 84.9605 | 84.1466 | 0.1868 | 234.2871 |
| 3.8563 | 0.9190 | 1100 | 3.8753 | 57.7808 | 23.8942 | 42.2385 | 54.2817 | 83.4992 | 85.0665 | 84.2729 | 0.1888 | 234.2871 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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