| --- |
| license: mit |
| base_model: indobenchmark/indobart-v2 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - squad |
| metrics: |
| - rouge |
| model-index: |
| - name: modified |
| results: |
| - task: |
| name: Sequence-to-sequence Language Modeling |
| type: text2text-generation |
| dataset: |
| name: squad |
| type: squad |
| config: plain_text |
| split: train[:1000] |
| args: plain_text |
| metrics: |
| - name: Rouge1 |
| type: rouge |
| value: 15.4275 |
| --- |
| |
| <!-- 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. --> |
|
|
| # modified |
|
|
| This model is a fine-tuned version of [indobenchmark/indobart-v2](https://huggingface.co/indobenchmark/indobart-v2) on the squad dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.6035 |
| - Rouge1: 15.4275 |
| - Rouge2: 14.2367 |
| - Rougel: 15.4625 |
| - Rougelsum: 15.4954 |
| - Gen Len: 20.0 |
|
|
| ## 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: 3e-05 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 100 |
| - num_epochs: 1 |
| - label_smoothing_factor: 0.1 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| |
| | 1.4719 | 1.0 | 200 | 1.6035 | 15.4275 | 14.2367 | 15.4625 | 15.4954 | 20.0 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.31.0 |
| - Pytorch 2.0.1+cu117 |
| - Datasets 2.14.2 |
| - Tokenizers 0.13.3 |
| |
