---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: summarizer
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: multi_news
      type: multi_news
      config: default
      split: test
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.1434
---

<!-- 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. -->

# summarizer

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7745
- Rouge1: 0.1434
- Rouge2: 0.0448
- Rougel: 0.1097
- Rougelsum: 0.1097
- Gen Len: 18.9968

## 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   | 352  | 2.8572          | 0.1386 | 0.0423 | 0.106  | 0.106     | 18.9968 |
| 3.2016        | 2.0   | 704  | 2.8029          | 0.1415 | 0.0435 | 0.108  | 0.108     | 18.9966 |
| 3.0361        | 3.0   | 1056 | 2.7814          | 0.143  | 0.0446 | 0.1093 | 0.1093    | 18.9968 |
| 3.0361        | 4.0   | 1408 | 2.7745          | 0.1434 | 0.0448 | 0.1097 | 0.1097    | 18.9968 |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2