---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- arxiv_summarization_dataset
metrics:
- rouge
base_model: sshleifer/distilbart-cnn-12-6
model-index:
- name: distilbart-cnn-12-6-finetuned-30k-3epoch
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: arxiv_summarization_dataset
      type: arxiv_summarization_dataset
      config: section
      split: test[:2000]
      args: section
    metrics:
    - type: rouge
      value: 43.696
      name: Rouge1
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbart-cnn-12-6-finetuned-30k-3epoch

This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the arxiv_summarization_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3411
- Rouge1: 43.696
- Rouge2: 15.6681
- Rougel: 25.6889
- Rougelsum: 38.574
- Gen Len: 121.98

## 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: 2e-05
- 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.7304        | 1.0   | 3750  | 2.4322          | 43.0913 | 15.1302 | 25.2555 | 38.0346   | 122.3755 |
| 2.3518        | 2.0   | 7500  | 2.3613          | 43.8799 | 15.6977 | 25.6984 | 38.7646   | 122.6945 |
| 2.2318        | 3.0   | 11250 | 2.3411          | 43.696  | 15.6681 | 25.6889 | 38.574    | 121.98   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3