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---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-3e-06-bs-2-maxep-6
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-abs-1509-0313-lr-3e-06-bs-2-maxep-6
This model is a fine-tuned version of [sshleifer/distilbart-xsum-12-6](https://huggingface.co/sshleifer/distilbart-xsum-12-6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5722
- Rouge/rouge1: 0.3111
- Rouge/rouge2: 0.0793
- Rouge/rougel: 0.2212
- Rouge/rougelsum: 0.2213
- Bertscore/bertscore-precision: 0.8659
- Bertscore/bertscore-recall: 0.864
- Bertscore/bertscore-f1: 0.8649
- Meteor: 0.228
- Gen Len: 36.0
## 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: 3e-06
- 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: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge/rouge1 | Rouge/rouge2 | Rouge/rougel | Rouge/rougelsum | Bertscore/bertscore-precision | Bertscore/bertscore-recall | Bertscore/bertscore-f1 | Meteor | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.3628 | 1.0 | 434 | 6.2314 | 0.2519 | 0.0551 | 0.191 | 0.191 | 0.8502 | 0.8569 | 0.8535 | 0.2501 | 50.8 |
| 0.3799 | 2.0 | 868 | 6.4498 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 |
| 0.4173 | 3.0 | 1302 | 6.4553 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 |
| 0.3921 | 4.0 | 1736 | 6.5283 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 |
| 0.3833 | 5.0 | 2170 | 6.5582 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 |
| 0.378 | 6.0 | 2604 | 6.5722 | 0.3111 | 0.0793 | 0.2212 | 0.2213 | 0.8659 | 0.864 | 0.8649 | 0.228 | 36.0 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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