---
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: prophetnet_summarization_pretrained
  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: 0.4982
---

<!-- 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. -->

# prophetnet_summarization_pretrained

This model is a fine-tuned version of [microsoft/prophetnet-large-uncased](https://huggingface.co./microsoft/prophetnet-large-uncased) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3683
- Rouge1: 0.4982
- Rouge2: 0.2267
- Rougel: 0.2983
- Rougelsum: 0.2985
- Gen Len: 139.3831

## 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| No log        | 1.0   | 124  | 2.5178          | 0.4894 | 0.2223 | 0.2903 | 0.2903    | 139.8105 |
| No log        | 2.0   | 248  | 2.4170          | 0.4973 | 0.2279 | 0.2975 | 0.297     | 140.6492 |
| No log        | 3.0   | 372  | 2.3895          | 0.4964 | 0.2282 | 0.2984 | 0.2981    | 138.5323 |
| No log        | 4.0   | 496  | 2.3683          | 0.4982 | 0.2267 | 0.2983 | 0.2985    | 139.3831 |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3