
# IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset

Finetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper [IndoNLU: Benchmark and Resources for Evaluating Indonesian
Natural Language Understanding](https://arxiv.org/pdf/2009.05387.pdf).

## How to use

```python
from transformers import pipeline
classifier = pipeline("text-classification", 
                      model='tyqiangz/indobert-lite-large-p2-smsa', 
                      return_all_scores=True)
text = "Penyakit koronavirus 2019"
prediction = classifier(text)
prediction

"""
Output:
[[{'label': 'positive', 'score': 0.0006000096909701824},
  {'label': 'neutral', 'score': 0.01223431620746851},
  {'label': 'negative', 'score': 0.987165629863739}]]
"""
```

**Finetuning hyperparameters:**
- learning rate: 2e-5
- batch size: 16
- no. of epochs: 5
- max sequence length: 512
- random seed: 42

**Classes:**
- 0: positive
- 1: neutral
- 2: negative

**Performance metrics on SmSA validation dataset**
- Validation accuracy: 0.94
- Validation F1: 0.91
- Validation Recall: 0.91
- Validation Precision: 0.93


{"language": "id", "license": "mit", "tags": ["indobert", "indobenchmark", "indonlu"], "datasets": ["Indo4B"], "inference": true}
transformers pytorch safetensors albert text-classification indobert indobenchmark indonlu id dataset:Indo4B arxiv:2009.05387 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #albert #text-classification #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset

Finetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper IndoNLU: Benchmark and Resources for Evaluating Indonesian
Natural Language Understanding.

## How to use



Finetuning hyperparameters:
- learning rate: 2e-5
- batch size: 16
- no. of epochs: 5
- max sequence length: 512
- random seed: 42

Classes:
- 0: positive
- 1: neutral
- 2: negative

Performance metrics on SmSA validation dataset
- Validation accuracy: 0.94
- Validation F1: 0.91
- Validation Recall: 0.91
- Validation Precision: 0.93


# IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset

Finetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper IndoNLU: Benchmark and Resources for Evaluating Indonesian
Natural Language Understanding. ## How to use



Finetuning hyperparameters:
- learning rate: 2e-5
- batch size: 16
- no. of epochs: 5
- max sequence length: 512
- random seed: 42

Classes:
- 0: positive
- 1: neutral
- 2: negative

Performance metrics on SmSA validation dataset
- Validation accuracy: 0.94
- Validation F1: 0.91
- Validation Recall: 0.91
- Validation Precision: 0.93
TAGS
#transformers #pytorch #safetensors #albert #text-classification #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset

Finetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper IndoNLU: Benchmark and Resources for Evaluating Indonesian
Natural Language Understanding. ## How to use



Finetuning hyperparameters:
- learning rate: 2e-5
- batch size: 16
- no. of epochs: 5
- max sequence length: 512
- random seed: 42

Classes:
- 0: positive
- 1: neutral
- 2: negative

Performance metrics on SmSA validation dataset
- Validation accuracy: 0.94
- Validation F1: 0.91
- Validation Recall: 0.91
- Validation Precision: 0.93
passage: TAGS
#transformers #pytorch #safetensors #albert #text-classification #indobert #indobenchmark #indonlu #id #dataset-Indo4B #arxiv-2009.05387 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# IndoBERT-Lite Large Model (phase2 - uncased) Finetuned on IndoNLU SmSA dataset

Finetuned the IndoBERT-Lite Large Model (phase2 - uncased) model on the IndoNLU SmSA dataset following the procedues stated in the paper IndoNLU: Benchmark and Resources for Evaluating Indonesian
Natural Language Understanding.## How to use



Finetuning hyperparameters:
- learning rate: 2e-5
- batch size: 16
- no. of epochs: 5
- max sequence length: 512
- random seed: 42

Classes:
- 0: positive
- 1: neutral
- 2: negative

Performance metrics on SmSA validation dataset
- Validation accuracy: 0.94
- Validation F1: 0.91
- Validation Recall: 0.91
- Validation Precision: 0.93