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---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-2-8
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. -->
# bart-large-cnn-finetuned-roundup-2-8
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: 1.1855
- Rouge1: 53.552
- Rouge2: 34.9077
- Rougel: 38.0158
- Rougelsum: 50.7179
- Gen Len: 142.0
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| No log | 1.0 | 167 | 1.2085 | 50.8706 | 32.0069 | 32.9241 | 47.9805 | 142.0 |
| No log | 2.0 | 334 | 1.0897 | 53.2218 | 34.1317 | 34.4827 | 50.4795 | 139.4286 |
| 1.0256 | 3.0 | 501 | 1.0535 | 50.8882 | 30.2514 | 31.5051 | 47.9856 | 141.9048 |
| 1.0256 | 4.0 | 668 | 1.0515 | 54.9414 | 35.2309 | 36.006 | 52.0331 | 142.0 |
| 1.0256 | 5.0 | 835 | 1.0829 | 53.0709 | 33.4587 | 36.4223 | 50.1627 | 140.7619 |
| 0.4579 | 6.0 | 1002 | 1.1310 | 51.5274 | 30.7069 | 32.4146 | 48.8851 | 142.0 |
| 0.4579 | 7.0 | 1169 | 1.1670 | 52.1536 | 31.7158 | 35.7483 | 49.2678 | 142.0 |
| 0.4579 | 8.0 | 1336 | 1.1855 | 53.552 | 34.9077 | 38.0158 | 50.7179 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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