---
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-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-2409-1947-lr-0.0003-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: 6.3423
- Rouge/rouge1: 0.2439
- Rouge/rouge2: 0.0504
- Rouge/rougel: 0.2065
- Rouge/rougelsum: 0.2067
- Bertscore/bertscore-precision: 0.8544
- Bertscore/bertscore-recall: 0.8581
- Bertscore/bertscore-f1: 0.8562
- Meteor: 0.229
- Gen Len: 46.0

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:------:|:-------:|
| 2.1377        | 1.0   | 217  | 3.9220          | 0.3283       | 0.1009       | 0.2612       | 0.2612          | 0.8785                        | 0.8638                     | 0.871                  | 0.2695 | 33.0    |
| 3.3219        | 2.0   | 434  | 3.7523          | 0.2756       | 0.0805       | 0.2368       | 0.237           | 0.8845                        | 0.8545                     | 0.8692                 | 0.2111 | 25.0    |
| 2.115         | 3.0   | 651  | 4.0783          | 0.282        | 0.0747       | 0.2116       | 0.2118          | 0.8663                        | 0.8623                     | 0.8642                 | 0.2582 | 41.0    |
| 1.1461        | 4.0   | 868  | 4.8795          | 0.251        | 0.0501       | 0.21         | 0.2102          | 0.8497                        | 0.8506                     | 0.8501                 | 0.2025 | 37.0    |
| 0.6272        | 5.0   | 1085 | 5.8094          | 0.2811       | 0.0751       | 0.229        | 0.2293          | 0.8628                        | 0.8693                     | 0.866                  | 0.2058 | 44.0    |
| 0.3841        | 6.0   | 1302 | 6.3423          | 0.2439       | 0.0504       | 0.2065       | 0.2067          | 0.8544                        | 0.8581                     | 0.8562                 | 0.229  | 46.0    |


### Framework versions

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