tensorboard generated_from_trainer id base_model:LazarusNLP/IndoNanoT5-base base_model:finetune:LazarusNLP/IndoNanoT5-base license:apache-2.0 region:us
---
base_model: LazarusNLP/IndoNanoT5-base
language:
- id
license: apache-2.0
metrics:
- rouge
tags:
- generated_from_trainer
model-index:
- name: summarization-seq_bn-4
  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. -->

# summarization-seq_bn-4

This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4653
- Rouge1: 0.3992
- Rouge2: 0.0
- Rougel: 0.4016
- Rougelsum: 0.4001
- Gen Len: 1.0

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.7807        | 1.0   | 892  | 0.5299          | 0.6861 | 0.0    | 0.6833 | 0.6836    | 1.0     |
| 0.6079        | 2.0   | 1784 | 0.4989          | 0.7141 | 0.0    | 0.7153 | 0.7147    | 1.0     |
| 0.5583        | 3.0   | 2676 | 0.4761          | 0.7153 | 0.0    | 0.7152 | 0.7139    | 1.0     |
| 0.5208        | 4.0   | 3568 | 0.4719          | 0.7433 | 0.0    | 0.743  | 0.7395    | 1.0     |
| 0.4912        | 5.0   | 4460 | 0.4653          | 0.7309 | 0.0    | 0.7284 | 0.7264    | 1.0     |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
