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README.md
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model-index:
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- name: er-model
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# er-model
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This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on
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It achieves the following results on the evaluation set:
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- Loss: 0.6762
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- Accuracy: 0.6981
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## Model description
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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## Training procedure
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### Training hyperparameters
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- Transformers 4.41.2
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- Pytorch 2.1.2
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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model-index:
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- name: er-model
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results: []
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datasets:
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- SEACrowd/prdect_id
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language:
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- id
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widget:
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- text: Ini toko korup.,ga sesuai sama isinya..not recommended
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example_title: Contoh
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# er-model
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This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on [The PRDECT-ID Dataset](https://www.kaggle.com/datasets/jocelyndumlao/prdect-id-indonesian-emotion-classification), it is a compilation of Indonesian product reviews that come with emotion and sentiment labels. These reviews were gathered from one of Indonesia's largest e-commerce platforms, Tokopedia..
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It achieves the following results on the evaluation set:
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- Loss: 0.6762
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- Accuracy: 0.6981
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## Model description
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It has been trained to classify text into six different emotion categories: happy, sadness, anger, love, and fear.
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## Training and evaluation data
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I split my dataframe df into training, validation, and testing sets (train_df, val_df, test_df) using the train_test_split function from sklearn.model_selection. I set the test size to 20% for the initial split and further divided the remaining data equally between validation and testing sets. This process ensures that each split (val_df and test_df) maintains the same class distribution as the original dataset (stratify=df['label']).
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### Training hyperparameters
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- Transformers 4.41.2
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- Pytorch 2.1.2
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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