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# indobert-finetuned-bapos

This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1239
- Precision: 0.9616
- Recall: 0.9634
- F1: 0.9625
- Accuracy: 0.9653

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2748        | 1.0   | 500  | 0.1450          | 0.9511    | 0.9529 | 0.9520 | 0.9559   |
| 0.0917        | 2.0   | 1000 | 0.1220          | 0.9585    | 0.9627 | 0.9606 | 0.9638   |
| 0.0612        | 3.0   | 1500 | 0.1239          | 0.9616    | 0.9634 | 0.9625 | 0.9653   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "indobert-finetuned-bapos", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "config": "bapos", "split": "train", "args": "bapos"}, "metrics": [{"type": "precision", "value": 0.9616493964320051, "name": "Precision"}, {"type": "recall", "value": 0.9633646060000713, "name": "Recall"}, {"type": "f1", "value": 0.9625062370803336, "name": "F1"}, {"type": "accuracy", "value": 0.9653301071552022, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert token-classification generated_from_trainer dataset:indonlu license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

indobert-finetuned-bapos
========================


This model is a fine-tuned version of indobenchmark/indobert-base-p2 on the indonlu dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1239
* Precision: 0.9616
* Recall: 0.9634
* F1: 0.9625
* Accuracy: 0.9653


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #token-classification #generated_from_trainer #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.25.1
* Pytorch 1.13.1+cu116
* Datasets 2.8.0
* Tokenizers 0.13.2