---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- multi_news
model-index:
- name: t5-small_multinews_model
  results: []
---

<!-- 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. -->

# t5-small_multinews_model

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: 2.6269
- Rouge Rouge1: 0.1471
- Rouge Rouge2: 0.0483
- Rouge Rougel: 0.1131
- Rouge Rougelsum: 0.1131
- Bleu Bleu: 0.0003
- Bleu Precisions: [0.5848502090652357, 0.18492208339182928, 0.08486295668446923, 0.04842115016777968]
- Bleu Brevity Penalty: 0.0022
- Bleu Length Ratio: 0.1408
- Bleu Translation Length: 191567
- Bleu Reference Length: 1360656

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge Rouge1 | Rouge Rouge2 | Rouge Rougel | Rouge Rougelsum | Bleu Bleu | Bleu Precisions                                                                     | Bleu Brevity Penalty | Bleu Length Ratio | Bleu Translation Length | Bleu Reference Length |
|:-------------:|:-----:|:-----:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:---------:|:-----------------------------------------------------------------------------------:|:--------------------:|:-----------------:|:-----------------------:|:---------------------:|
| 2.9189        | 1.0   | 7870  | 2.6869          | 0.1448       | 0.0474       | 0.1117       | 0.1117          | 0.0003    | [0.5827522821123012, 0.1820493433028088, 0.08242051182628926, 0.04574874477953644]  | 0.0023               | 0.1411            | 192037                  | 1360656               |
| 2.8435        | 2.0   | 15740 | 2.6535          | 0.1460       | 0.0474       | 0.1122       | 0.1122          | 0.0003    | [0.5809636959568958, 0.18126278620071182, 0.08254004826406995, 0.04636911719064694] | 0.0023               | 0.1410            | 191907                  | 1360656               |
| 2.7922        | 3.0   | 23610 | 2.6389          | 0.1461       | 0.0477       | 0.1124       | 0.1124          | 0.0003    | [0.581669805398619, 0.18257649098318213, 0.08343485040444401, 0.0471782007379682]   | 0.0022               | 0.1405            | 191160                  | 1360656               |
| 2.814         | 4.0   | 31480 | 2.6280          | 0.1468       | 0.0478       | 0.1129       | 0.1129          | 0.0003    | [0.5844809737428239, 0.18360803285143726, 0.08381524001996615, 0.04753093788548009] | 0.0022               | 0.1406            | 191262                  | 1360656               |
| 2.7869        | 5.0   | 39350 | 2.6269          | 0.1471       | 0.0483       | 0.1131       | 0.1131          | 0.0003    | [0.5848502090652357, 0.18492208339182928, 0.08486295668446923, 0.04842115016777968] | 0.0022               | 0.1408            | 191567                  | 1360656               |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3