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---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: bart-samsum-finetuned
results: []
metrics:
- bertscore
- bleu
---
<!-- 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. -->
# bart-samsum-finetuned
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1326
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1196 | 1.0 | 74 | 0.1362 |
| 0.0948 | 2.0 | 148 | 0.1334 |
| 0.0738 | 3.0 | 222 | 0.1326 |
### Evaluation results
Rouge Scores:
| Metric | Precision | Recall | F-Measure |
|:----------:|:-----------------:|:-----------------:|:--------------------:|
| rouge1 | Low - 0.2923 | Low - 0.5755 | Low - 0.3645 |
| | Mid - 0.3012 | Mid - 0.5881 | Mid - 0.3722 |
| | High - 0.3108 | High - 0.6011 | High - 0.3811 |
| rouge2 | Low - 0.1185 | Low - 0.2418 | Low - 0.1481 |
| | Mid - 0.1252 | Mid - 0.2545 | Mid - 0.1555 |
| | High - 0.1321 | High - 0.2682 | High - 0.1632 |
| rougeL | Low - 0.2182 | Low - 0.4434 | Low - 0.2744 |
| | Mid - 0.2251 | Mid - 0.4547 | Mid - 0.2810 |
| | High - 0.2328 | High - 0.4679 | High - 0.2886 |
| rougeLsum | Low - 0.2178 | Low - 0.4425 | Low - 0.2739 |
| | Mid - 0.2249 | Mid - 0.4546 | Mid - 0.2807 |
| | High - 0.2321 | High - 0.4679 | High - 0.2883 |
BERTScore:
| Precision | Recall | F1 |
|:---------:|:---------:|:---------:|
| 0.6054495 | 0.6918860 | 0.6425597 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |