Text Classification
Transformers
PyTorch
Safetensors
Indonesian
albert
indobert
indobenchmark
indonlu
Instructions to use tyqiangz/indobert-lite-large-p2-smsa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tyqiangz/indobert-lite-large-p2-smsa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tyqiangz/indobert-lite-large-p2-smsa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tyqiangz/indobert-lite-large-p2-smsa") model = AutoModelForSequenceClassification.from_pretrained("tyqiangz/indobert-lite-large-p2-smsa") - Notebooks
- Google Colab
- Kaggle
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README.md
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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
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Natural Language Understanding](https://arxiv.org/pdf/2009.05387.pdf).
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**Finetuning hyperparameters:**
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- learning rate: 2e-5
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- batch size: 16
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- no. of epochs: 5
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- max sequence length: 512
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- random seed: 42
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**Classes:**
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- 0: positive
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- 1: neutral
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- 2: negative
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Validation accuracy: 0.94
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Validation F1: 0.91
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Validation Recall: 0.91
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Validation Precision: 0.93
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## How to use
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### Load model and tokenizer
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```python
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from transformers import pipeline
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classifier = pipeline("text-classification",
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{'label': 'negative', 'score': 0.987165629863739}]]
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"""
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```
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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
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Natural Language Understanding](https://arxiv.org/pdf/2009.05387.pdf).
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## How to use
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```python
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from transformers import pipeline
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classifier = pipeline("text-classification",
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{'label': 'negative', 'score': 0.987165629863739}]]
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"""
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```
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**Finetuning hyperparameters:**
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- learning rate: 2e-5
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- batch size: 16
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- no. of epochs: 5
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- max sequence length: 512
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- random seed: 42
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**Classes:**
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- 0: positive
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- 1: neutral
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- 2: negative
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**Performance metrics on SmSA validation dataset**
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- Validation accuracy: 0.94
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- Validation F1: 0.91
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- Validation Recall: 0.91
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- Validation Precision: 0.93
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