---
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-3e-05-bs-2-maxep-10
  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-3e-05-bs-2-maxep-10

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: 3.6542
- Rouge/rouge1: 0.4733
- Rouge/rouge2: 0.2228
- Rouge/rougel: 0.409
- Rouge/rougelsum: 0.4103
- Bertscore/bertscore-precision: 0.8957
- Bertscore/bertscore-recall: 0.8945
- Bertscore/bertscore-f1: 0.8949
- Meteor: 0.4257
- Gen Len: 38.0727

## 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: 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: 10
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 2.3299        | 1.0   | 434  | 2.1997          | 0.4484       | 0.2127       | 0.3842       | 0.3856          | 0.8939                        | 0.8904                     | 0.892                  | 0.4107 | 38.4273 |
| 1.6174        | 2.0   | 868  | 2.0581          | 0.4617       | 0.2158       | 0.3919       | 0.393           | 0.8972                        | 0.8907                     | 0.8938                 | 0.4049 | 36.0545 |
| 1.1854        | 3.0   | 1302 | 2.1888          | 0.4631       | 0.2153       | 0.3893       | 0.3914          | 0.9                           | 0.891                      | 0.8953                 | 0.4044 | 35.8273 |
| 0.8568        | 4.0   | 1736 | 2.3776          | 0.4518       | 0.201        | 0.3822       | 0.3839          | 0.8973                        | 0.8895                     | 0.8932                 | 0.3907 | 34.7636 |
| 0.6056        | 5.0   | 2170 | 2.6468          | 0.4731       | 0.2207       | 0.4031       | 0.4036          | 0.8974                        | 0.8946                     | 0.8959                 | 0.4173 | 37.5818 |
| 0.4235        | 6.0   | 2604 | 2.9226          | 0.4816       | 0.2258       | 0.4055       | 0.4064          | 0.8989                        | 0.8955                     | 0.897                  | 0.4317 | 36.8909 |
| 0.2995        | 7.0   | 3038 | 3.1938          | 0.4541       | 0.1989       | 0.3839       | 0.3843          | 0.8941                        | 0.8916                     | 0.8927                 | 0.4071 | 37.0091 |
| 0.2079        | 8.0   | 3472 | 3.4178          | 0.4684       | 0.2094       | 0.3926       | 0.3931          | 0.8941                        | 0.8942                     | 0.894                  | 0.4177 | 39.4545 |
| 0.1527        | 9.0   | 3906 | 3.5347          | 0.4716       | 0.2151       | 0.3989       | 0.4009          | 0.8937                        | 0.8942                     | 0.8938                 | 0.4277 | 39.5909 |
| 0.1203        | 10.0  | 4340 | 3.6542          | 0.4733       | 0.2228       | 0.409        | 0.4103          | 0.8957                        | 0.8945                     | 0.8949                 | 0.4257 | 38.0727 |


### Framework versions

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