---
license: apache-2.0
base_model: LazarusNLP/IndoNanoT5-base
tags:
- generated_from_trainer
language:
- ind
datasets:
- GEM/indonlg
metrics:
- rouge
model-index:
- name: LazarusNLP/IndoNanoT5-base-IndoSum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: indonlg
      type: indonlg
      config: indosum
      split: test
      args: indosum
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.7529
    - name: Rouge2
      type: rouge
      value: 0.7123
    - name: RougeL
      type: rouge
      value: 0.733
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# LazarusNLP/IndoNanoT5-base-IndoSum

This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on the indonlg dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1086
- Rouge1: 0.7529
- Rouge2: 0.7123
- Rougel: 0.733
- Rougelsum: 0.733
- Gen Len: 110.0391

## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 16
- 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 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.3004        | 1.0   | 1761 | 0.1682          | 0.258  | 0.2277 | 0.2549 | 0.255     | 19.0    |
| 0.1463        | 2.0   | 3522 | 0.1318          | 0.2596 | 0.2305 | 0.2563 | 0.2565    | 19.0    |
| 0.095         | 3.0   | 5283 | 0.1272          | 0.2602 | 0.2314 | 0.2571 | 0.257     | 19.0    |
| 0.0705        | 4.0   | 7044 | 0.1186          | 0.2622 | 0.2338 | 0.2592 | 0.2592    | 19.0    |
| 0.0436        | 5.0   | 8805 | 0.1236          | 0.2625 | 0.2342 | 0.2594 | 0.2596    | 19.0    |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1