---
license: apache-2.0
base_model: sshleifer/distilbart-cnn-12-6
tags:
- generated_from_trainer
datasets:
- dialogstudio
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: dialogstudio
      type: dialogstudio
      config: TweetSumm
      split: test
      args: TweetSumm
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.4187
---

<!-- 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. -->

# my_awesome_billsum_model

This model is a fine-tuned version of [sshleifer/distilbart-cnn-12-6](https://huggingface.co/sshleifer/distilbart-cnn-12-6) on the dialogstudio dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9811
- Rouge1: 0.4187
- Rouge2: 0.1911
- Rougel: 0.3373
- Rougelsum: 0.338
- Gen Len: 65.1636

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log        | 1.0   | 55   | 2.0591          | 0.4232 | 0.1899 | 0.3412 | 0.342     | 64.8545 |
| No log        | 2.0   | 110  | 1.9802          | 0.4125 | 0.19   | 0.3329 | 0.3334    | 66.7545 |
| No log        | 3.0   | 165  | 1.9671          | 0.4172 | 0.1927 | 0.3348 | 0.3357    | 65.3545 |
| No log        | 4.0   | 220  | 1.9811          | 0.4187 | 0.1911 | 0.3373 | 0.338     | 65.1636 |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1