---
license: apache-2.0
base_model: sshleifer/distilbart-xsum-12-6
tags:
- generated_from_trainer
model-index:
- name: bart-abs-1509-0313-lr-3e-05-bs-4-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-05-bs-4-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: 2.3615
- Rouge/rouge1: 0.4759
- Rouge/rouge2: 0.2278
- Rouge/rougel: 0.407
- Rouge/rougelsum: 0.4084
- Bertscore/bertscore-precision: 0.898
- Bertscore/bertscore-recall: 0.8937
- Bertscore/bertscore-f1: 0.8957
- Meteor: 0.4311
- Gen Len: 37.0

## 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-05
- train_batch_size: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 2.3606        | 1.0   | 217  | 2.0792          | 0.44         | 0.2028       | 0.3696       | 0.3707          | 0.8966                        | 0.8883                     | 0.8923                 | 0.3866 | 37.3636 |
| 1.6544        | 2.0   | 434  | 1.9952          | 0.4521       | 0.2171       | 0.3902       | 0.3909          | 0.8991                        | 0.8912                     | 0.895                  | 0.4007 | 34.6636 |
| 1.2907        | 3.0   | 651  | 2.0614          | 0.4661       | 0.2212       | 0.399        | 0.4008          | 0.9006                        | 0.8929                     | 0.8966                 | 0.4128 | 35.2636 |
| 1.0179        | 4.0   | 868  | 2.1396          | 0.479        | 0.2295       | 0.4121       | 0.4137          | 0.9024                        | 0.8933                     | 0.8977                 | 0.4142 | 34.7182 |
| 0.8112        | 5.0   | 1085 | 2.2658          | 0.4737       | 0.2237       | 0.4046       | 0.405           | 0.8989                        | 0.8931                     | 0.8959                 | 0.4126 | 35.4273 |
| 0.6745        | 6.0   | 1302 | 2.3615          | 0.4759       | 0.2278       | 0.407        | 0.4084          | 0.898                         | 0.8937                     | 0.8957                 | 0.4311 | 37.0    |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1