---
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-8-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-8-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: 6.6809
- 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.2815        | 1.0   | 109  | 6.6245          | 0.323        | 0.081        | 0.2516       | 0.2521          | 0.8764                        | 0.8641                     | 0.8702                 | 0.2583 | 36.0    |
| 0.3942        | 2.0   | 218  | 6.6890          | 0.2515       | 0.0697       | 0.2094       | 0.21            | 0.8374                        | 0.8631                     | 0.85                   | 0.2474 | 48.0    |
| 0.3862        | 3.0   | 327  | 6.6692          | 0.3111       | 0.0793       | 0.2212       | 0.2213          | 0.8659                        | 0.864                      | 0.8649                 | 0.228  | 36.0    |
| 0.3708        | 4.0   | 436  | 6.6624          | 0.2439       | 0.0504       | 0.2065       | 0.2067          | 0.8544                        | 0.8581                     | 0.8562                 | 0.229  | 45.0    |
| 0.3679        | 5.0   | 545  | 6.6795          | 0.3111       | 0.0793       | 0.2212       | 0.2213          | 0.8659                        | 0.864                      | 0.8649                 | 0.228  | 36.0    |
| 0.3629        | 6.0   | 654  | 6.6809          | 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