---
library_name: transformers
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-2409-1947-lr-0.0003-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-2409-1947-lr-0.0003-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: 7.3898
- Rouge/rouge1: 0.3035
- Rouge/rouge2: 0.072
- Rouge/rougel: 0.2428
- Rouge/rougelsum: 0.2429
- Bertscore/bertscore-precision: 0.8724
- Bertscore/bertscore-recall: 0.8571
- Bertscore/bertscore-f1: 0.8646
- Meteor: 0.2108
- Gen Len: 29.0

## 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: 0.0003
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.3901        | 1.0   | 109  | 6.5833          | 0.2377       | 0.0309       | 0.193        | 0.1932          | 0.8496                        | 0.853                      | 0.8512                 | 0.2159 | 45.0    |
| 0.3274        | 2.0   | 218  | 6.5583          | 0.2439       | 0.0504       | 0.2065       | 0.2067          | 0.8544                        | 0.8581                     | 0.8562                 | 0.229  | 45.0    |
| 0.3098        | 3.0   | 327  | 6.9294          | 0.2613       | 0.0803       | 0.214        | 0.2142          | 0.8711                        | 0.8469                     | 0.8588                 | 0.2102 | 25.0    |
| 0.2625        | 4.0   | 436  | 7.0223          | 0.3008       | 0.0767       | 0.229        | 0.2292          | 0.858                         | 0.8674                     | 0.8626                 | 0.2167 | 41.0    |
| 0.2379        | 5.0   | 545  | 7.2276          | 0.3035       | 0.072        | 0.2428       | 0.2429          | 0.8724                        | 0.8571                     | 0.8646                 | 0.2108 | 29.0    |
| 0.2168        | 6.0   | 654  | 7.3898          | 0.3035       | 0.072        | 0.2428       | 0.2429          | 0.8724                        | 0.8571                     | 0.8646                 | 0.2108 | 29.0    |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1