---
license: apache-2.0
base_model: sshleifer/distilbart-cnn-6-6
tags:
- generated_from_trainer
datasets:
- wcep-10
metrics:
- rouge
model-index:
- name: thesis-bart-finetuned-on-original-wcep
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: wcep-10
      type: wcep-10
      config: roberta
      split: validation
      args: roberta
    metrics:
    - name: Rouge1
      type: rouge
      value: 37.2224
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# thesis-bart-finetuned-on-original-wcep

This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on the wcep-10 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9981
- Rouge1: 37.2224
- Rouge2: 16.5575
- Rougel: 26.7904
- Rougelsum: 30.3497
- Gen Len: 67.5627

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.0801        | 1.0   | 510  | 2.0119          | 36.4915 | 16.0165 | 26.3565 | 29.7397   | 67.9882 |
| 1.7597        | 2.0   | 1020 | 1.9868          | 36.9513 | 16.3776 | 26.4974 | 30.1234   | 68.3961 |
| 1.5997        | 3.0   | 1530 | 1.9981          | 37.2224 | 16.5575 | 26.7904 | 30.3497   | 67.5627 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2