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---
license: mit
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: bart-large-cnn-small-billsum-3epochs
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.5409
---
<!-- 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-large-cnn-small-billsum-3epochs
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7523
- Rouge1: 0.5409
- Rouge2: 0.3112
- Rougel: 0.3929
- Rougelsum: 0.4633
## 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: 2.5764683748161164e-05
- train_batch_size: 4
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 16
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.7132 | 0.32 | 8 | 2.2000 | 0.4619 | 0.2328 | 0.3201 | 0.3939 |
| 2.236 | 0.64 | 16 | 1.9705 | 0.499 | 0.2768 | 0.3651 | 0.4216 |
| 2.1109 | 0.96 | 24 | 1.8845 | 0.5214 | 0.2974 | 0.3844 | 0.4417 |
| 1.7663 | 1.28 | 32 | 1.8211 | 0.5226 | 0.2935 | 0.3718 | 0.4479 |
| 1.7838 | 1.6 | 40 | 1.7981 | 0.5338 | 0.3001 | 0.383 | 0.4466 |
| 1.5229 | 1.92 | 48 | 1.7625 | 0.5299 | 0.3012 | 0.3839 | 0.4494 |
| 1.5221 | 2.24 | 56 | 1.7532 | 0.5384 | 0.3117 | 0.3939 | 0.4637 |
| 1.2879 | 2.56 | 64 | 1.7560 | 0.5338 | 0.3075 | 0.3865 | 0.4584 |
| 1.4046 | 2.88 | 72 | 1.7523 | 0.5409 | 0.3112 | 0.3929 | 0.4633 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2
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