---
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- summarization
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: t5-small-Abstractive-Summarizer
  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: 15.7032
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# t5-small-Abstractive-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: 2.7737
- Rouge1: 15.7032
- Rouge2: 5.2433
- Rougel: 12.282
- Rougelsum: 14.0946

## 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: 0.00056
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.118         | 1.0   | 113  | 2.7677          | 15.1343 | 4.7712 | 11.8812 | 13.386    |
| 2.7857        | 2.0   | 226  | 2.7609          | 15.7641 | 4.8705 | 12.0955 | 13.9779   |
| 2.6158        | 3.0   | 339  | 2.7494          | 15.1515 | 4.4523 | 11.7147 | 13.4181   |
| 2.4962        | 4.0   | 452  | 2.7743          | 15.344  | 5.1073 | 12.1574 | 13.7917   |
| 2.4304        | 5.0   | 565  | 2.7737          | 15.7032 | 5.2433 | 12.282  | 14.0946   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1