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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: bart-samsung-5
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: samsum
type: samsum
config: samsum
split: train
args: samsum
metrics:
- name: Rouge1
type: rouge
value: 48.4734
---
<!-- 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-samsung-5
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4959
- Rouge1: 48.4734
- Rouge2: 25.3475
- Rougel: 40.9144
- Rougelsum: 44.7797
- Gen Len: 18.22
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.6107 | 1.0 | 1841 | 1.5390 | 47.1407 | 24.384 | 40.4826 | 43.4437 | 17.5513 |
| 1.5528 | 2.0 | 3682 | 1.4971 | 48.5483 | 25.1562 | 41.1806 | 44.7254 | 18.3521 |
| 1.4225 | 3.0 | 5523 | 1.5013 | 48.2461 | 25.2181 | 40.9022 | 44.4942 | 18.0844 |
| 1.3266 | 4.0 | 7364 | 1.4976 | 48.8949 | 25.4367 | 41.2355 | 45.0961 | 18.2359 |
| 1.2635 | 5.0 | 9205 | 1.4959 | 48.4734 | 25.3475 | 40.9144 | 44.7797 | 18.22 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
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