---
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-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-2409-1947-lr-0.0003-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: 7.1441
- 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 0.955         | 1.0   | 434  | 5.7740          | 0.2485       | 0.0542       | 0.1867       | 0.1867          | 0.8491                        | 0.8557                     | 0.8523                 | 0.2552 | 54.0    |
| 0.8434        | 2.0   | 868  | 5.6927          | 0.2599       | 0.0592       | 0.202        | 0.2017          | 0.8553                        | 0.8626                     | 0.8589                 | 0.2583 | 46.0    |
| 0.5948        | 3.0   | 1302 | 5.8907          | 0.2473       | 0.0528       | 0.1834       | 0.1833          | 0.8458                        | 0.856                      | 0.8508                 | 0.2569 | 54.0    |
| 0.4377        | 4.0   | 1736 | 6.5736          | 0.2591       | 0.0557       | 0.2009       | 0.2012          | 0.859                         | 0.8605                     | 0.8597                 | 0.2511 | 48.0    |
| 0.3416        | 5.0   | 2170 | 6.8597          | 0.3035       | 0.072        | 0.2428       | 0.2429          | 0.8724                        | 0.8571                     | 0.8646                 | 0.2108 | 29.0    |
| 0.268         | 6.0   | 2604 | 7.1441          | 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