---
base_model: google/pegasus-cnn_dailymail
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: pegasuscnn-dailymail_billsum_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: billsum
      type: billsum
      config: default
      split: ca_test
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.4804
---

<!-- 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. -->

# pegasuscnn-dailymail_billsum_model

This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6747
- Rouge1: 0.4804
- Rouge2: 0.2362
- Rougel: 0.3218
- Rougelsum: 0.3218
- Gen Len: 123.3669

## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| 2.6227        | 1.0   | 198  | 1.9091          | 0.4289 | 0.1938 | 0.2945 | 0.2947    | 120.1855 |
| 1.9714        | 2.0   | 396  | 1.8147          | 0.4517 | 0.2093 | 0.3059 | 0.3061    | 120.7742 |
| 1.903         | 3.0   | 594  | 1.7646          | 0.4607 | 0.2207 | 0.3098 | 0.3102    | 121.121  |
| 1.7973        | 4.0   | 792  | 1.7362          | 0.4719 | 0.2264 | 0.3179 | 0.3178    | 122.3185 |
| 1.7868        | 5.0   | 990  | 1.7137          | 0.4779 | 0.2314 | 0.3191 | 0.3192    | 123.2379 |
| 1.7457        | 6.0   | 1188 | 1.6958          | 0.4748 | 0.2296 | 0.3171 | 0.317     | 123.2056 |
| 1.6687        | 7.0   | 1386 | 1.6873          | 0.4795 | 0.2352 | 0.3216 | 0.3216    | 123.2702 |
| 1.6751        | 8.0   | 1584 | 1.6806          | 0.4835 | 0.2384 | 0.3248 | 0.3245    | 122.8266 |
| 1.6564        | 9.0   | 1782 | 1.6758          | 0.4814 | 0.2359 | 0.3217 | 0.3216    | 123.2984 |
| 1.6333        | 10.0  | 1980 | 1.6747          | 0.4804 | 0.2362 | 0.3218 | 0.3218    | 123.3669 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1