---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- indosum
metrics:
- rouge
model-index:
- name: my_awesome_indosum_model
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: indosum
      type: indosum
      config: indosum_fold0_source
      split: test
      args: indosum_fold0_source
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.1739
---

<!-- 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_indosum_model

This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the indosum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9401
- Rouge1: 0.1739
- Rouge2: 0.1376
- Rougel: 0.1741
- Rougelsum: 0.1737
- 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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   | 19   | 1.3632          | 0.1491 | 0.1079 | 0.1489 | 0.1485    | 19.0    |
| No log        | 2.0   | 38   | 1.0416          | 0.1676 | 0.1255 | 0.1674 | 0.1661    | 19.0    |
| No log        | 3.0   | 57   | 0.9622          | 0.1735 | 0.1357 | 0.1733 | 0.1727    | 19.0    |
| No log        | 4.0   | 76   | 0.9401          | 0.1739 | 0.1376 | 0.1741 | 0.1737    | 19.0    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0