Indo RoBERTa Emotion Classifier

Indo RoBERTa Emotion Classifier is emotion classifier based on Indo-roberta model. It was trained on the trained on IndoNLU EmoT dataset. The model used was Indo-roberta and was transfer-learned to an emotion classifier model. Based from the IndoNLU bencmark, the model achieve an f1-macro of 72.05%, accuracy of 71.81%, precision of 72.47% and recall of 71.94%.

Model

The model was trained on 7 epochs with learning rate 2e-5. Achieved different metrics as shown below.

Epoch	Training Loss	Validation Loss	Accuracy	F1	Precision	Recall
1	1.300700	1.005149	0.622727	0.601846	0.640845	0.611144
2	0.806300	0.841953	0.686364	0.694096	0.701984	0.696657
3	0.591900	0.796794	0.686364	0.696573	0.707520	0.691671
4	0.441200	0.782094	0.722727	0.724359	0.725985	0.730229
5	0.334700	0.809931	0.711364	0.720550	0.718318	0.724608
6	0.268400	0.812771	0.718182	0.724192	0.721222	0.729195
7	0.226000	0.828461	0.725000	0.733625	0.731709	0.735800

How to Use

As Text Classifier

from transformers import pipeline
pretrained_name = "StevenLimcorn/indonesian-roberta-base-emotion-classifier"
nlp = pipeline(
    "sentiment-analysis",
    model=pretrained_name,
    tokenizer=pretrained_name
)
nlp("Hal-hal baik akan datang.")

Disclaimer

Do consider the biases which come from both the pre-trained RoBERTa model and the EmoT dataset that may be carried over into the results of this model.

Author

Indonesian RoBERTa Base Emotion Classifier was trained and evaluated by Steven Limcorn. All computation and development are done on Google Colaboratory using their free GPU access.

If used, please cite

@misc {steven_limcorn_2023,
	author       = { {Steven Limcorn} },
	title        = { indonesian-roberta-base-emotion-classifier (Revision e8a9cb9) },
	year         = 2023,
	url          = { https://huggingface.co/StevenLimcorn/indonesian-roberta-base-emotion-classifier },
	doi          = { 10.57967/hf/0681 },
	publisher    = { Hugging Face }
}

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