---
license: apache-2.0
base_model: stevhliu/my_awesome_billsum_model
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: my_awesome_multinews_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: multi_news
      type: multi_news
      config: default
      split: validation
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.1416
---

<!-- 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_multinews_model

This model is a fine-tuned version of [stevhliu/my_awesome_billsum_model](https://huggingface.co/stevhliu/my_awesome_billsum_model) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8031
- Rouge1: 0.1416
- Rouge2: 0.0452
- Rougel: 0.1098
- Rougelsum: 0.1099
- Gen Len: 19.0

## 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   | 282  | 2.8803          | 0.1378 | 0.0427 | 0.1067 | 0.1067    | 19.0    |
| 3.1546        | 2.0   | 564  | 2.8260          | 0.1393 | 0.043  | 0.1077 | 0.1077    | 19.0    |
| 3.1546        | 3.0   | 846  | 2.8089          | 0.1418 | 0.0452 | 0.1096 | 0.1096    | 19.0    |
| 3.0357        | 4.0   | 1128 | 2.8031          | 0.1416 | 0.0452 | 0.1098 | 0.1099    | 19.0    |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1