|
--- |
|
license: apache-2.0 |
|
base_model: LazarusNLP/IndoNanoT5-base |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: indosum-base-0 |
|
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. --> |
|
|
|
# indosum-base-0 |
|
|
|
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: 0.7170 |
|
- Rouge1: 72.5364 |
|
- Rouge2: 65.2519 |
|
- Rougel: 69.5637 |
|
- Rougelsum: 71.6884 |
|
- Gen Len: 98.9053 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:| |
|
| 1.2132 | 1.0 | 892 | 0.7742 | 67.4414 | 59.7409 | 64.517 | 66.4918 | 94.092 | |
|
| 0.686 | 2.0 | 1784 | 0.6673 | 70.2138 | 62.8202 | 67.1553 | 69.3063 | 100.2933 | |
|
| 0.491 | 3.0 | 2676 | 0.6274 | 71.2142 | 63.9943 | 68.2722 | 70.2971 | 100.944 | |
|
| 0.343 | 4.0 | 3568 | 0.6469 | 71.7114 | 64.489 | 68.7214 | 70.7949 | 98.8227 | |
|
| 0.2059 | 5.0 | 4460 | 0.7170 | 72.5364 | 65.2519 | 69.5637 | 71.6884 | 98.9053 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.40.2 |
|
- Pytorch 2.3.1+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
|
|
