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# indobert-base-uncased-finetuned-indonlu-smsa

This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2277
- Accuracy: 0.9302
- F1: 0.9066
- Precision: 0.8992
- Recall: 0.9147


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| No log        | 1.0   | 344  | 0.3831          | 0.8476   | 0.7715 | 0.7817    | 0.7627 |
| 0.4167        | 2.0   | 688  | 0.2809          | 0.8905   | 0.8406 | 0.8699    | 0.8185 |
| 0.2624        | 3.0   | 1032 | 0.2254          | 0.9230   | 0.8842 | 0.9004    | 0.8714 |
| 0.2624        | 4.0   | 1376 | 0.2378          | 0.9238   | 0.8797 | 0.9180    | 0.8594 |
| 0.1865        | 5.0   | 1720 | 0.2277          | 0.9302   | 0.9066 | 0.8992    | 0.9147 |
| 0.1217        | 6.0   | 2064 | 0.2444          | 0.9262   | 0.8981 | 0.9013    | 0.8957 |
| 0.1217        | 7.0   | 2408 | 0.2985          | 0.9286   | 0.8999 | 0.9035    | 0.8971 |
| 0.0847        | 8.0   | 2752 | 0.3397          | 0.9278   | 0.8969 | 0.9090    | 0.8871 |
| 0.0551        | 9.0   | 3096 | 0.3542          | 0.9270   | 0.8961 | 0.9010    | 0.8924 |
| 0.0551        | 10.0  | 3440 | 0.3862          | 0.9222   | 0.8895 | 0.8970    | 0.8846 |


### Framework versions

- Transformers 4.14.1
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
{"language": "id", "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["indonlu"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Entah mengapa saya merasakan ada sesuatu yang janggal di produk ini"}], "model-index": [{"name": "indobert-base-uncased-finetuned-indonlu-smsa", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "indonlu", "type": "indonlu", "args": "smsa"}, "metrics": [{"type": "accuracy", "value": 0.9301587301587302, "name": "Accuracy"}, {"type": "f1", "value": 0.9066105299178986, "name": "F1"}, {"type": "precision", "value": 0.8992078788375845, "name": "Precision"}, {"type": "recall", "value": 0.9147307323234121, "name": "Recall"}]}]}]}
transformers pytorch safetensors bert text-classification generated_from_trainer id dataset:indonlu license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

indobert-base-uncased-finetuned-indonlu-smsa
============================================


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


* Loss: 0.2277
* Accuracy: 0.9302
* F1: 0.9066
* Precision: 0.8992
* Recall: 0.9147


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.14.1
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.14.1
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.14.1
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #id #dataset-indonlu #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1500
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.14.1
* Pytorch 1.10.0+cu111
* Datasets 1.17.0
* Tokenizers 0.10.3