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--- |
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language: id |
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tags: |
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- indonesian-roberta-base-indolem-sentiment-classifier-fold-0 |
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license: mit |
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datasets: |
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- indolem |
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widget: |
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- text: "Pelayanan hotel ini sangat baik." |
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--- |
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## Indonesian RoBERTa Base IndoLEM Sentiment Classifier |
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Indonesian RoBERTa Base IndoLEM Sentiment Classifier is a sentiment-text-classification model based on the [RoBERTa](https://arxiv.org/abs/1907.11692) model. The model was originally the pre-trained [Indonesian RoBERTa Base](https://hf.co/flax-community/indonesian-roberta-base) model, which is then fine-tuned on [`indolem`](https://indolem.github.io/)'s [Sentiment Analysis](https://github.com/indolem/indolem/tree/main/sentiment) dataset consisting of Indonesian tweets and hotel reviews (Koto et al., 2020). |
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A 5-fold cross-validation experiment was performed, with splits provided by the original dataset authors. This model was trained on fold 0. You can find models trained on [fold 0](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0), [fold 1](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-1), [fold 2](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-2), [fold 3](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-3), and [fold 4](https://huggingface.co/w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-4), in their respective links. |
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On **fold 0**, the model achieved an F1 of 86.42% on dev/validation and 83.12% on test. On all **5 folds**, the models achieved an average F1 of 84.14% on dev/validation and 84.64% on test. |
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Hugging Face's `Trainer` class from the [Transformers](https://huggingface.co/transformers) library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless. |
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## Model |
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| Model | #params | Arch. | Training/Validation data (text) | |
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| ------------------------------------------------------------- | ------- | ------------ | ------------------------------- | |
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| `indonesian-roberta-base-indolem-sentiment-classifier-fold-0` | 124M | RoBERTa Base | `IndoLEM`'s Sentiment Analysis | |
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## Evaluation Results |
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The model was trained for 10 epochs and the best model was loaded at the end. |
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| Epoch | Training Loss | Validation Loss | Accuracy | F1 | Precision | Recall | |
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| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- | |
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| 1 | 0.563500 | 0.420457 | 0.796992 | 0.626728 | 0.680000 | 0.581197 | |
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| 2 | 0.293600 | 0.281360 | 0.884712 | 0.811475 | 0.779528 | 0.846154 | |
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| 3 | 0.163000 | 0.267922 | 0.904762 | 0.844262 | 0.811024 | 0.880342 | |
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| 4 | 0.090200 | 0.335411 | 0.899749 | 0.838710 | 0.793893 | 0.888889 | |
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| 5 | 0.065200 | 0.462526 | 0.897243 | 0.835341 | 0.787879 | 0.888889 | |
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| 6 | 0.039200 | 0.423001 | 0.912281 | 0.859438 | 0.810606 | 0.914530 | |
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| 7 | 0.025300 | 0.452100 | 0.912281 | 0.859438 | 0.810606 | 0.914530 | |
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| 8 | 0.010400 | 0.525200 | 0.914787 | 0.855932 | 0.848739 | 0.863248 | |
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| 9 | 0.007100 | 0.513585 | 0.909774 | 0.850000 | 0.829268 | 0.871795 | |
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| 10 | 0.007200 | 0.537254 | 0.917293 | 0.864198 | 0.833333 | 0.897436 | |
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## How to Use |
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### As Text Classifier |
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```python |
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from transformers import pipeline |
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pretrained_name = "w11wo/indonesian-roberta-base-indolem-sentiment-classifier-fold-0" |
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nlp = pipeline( |
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"sentiment-analysis", |
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model=pretrained_name, |
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tokenizer=pretrained_name |
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) |
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nlp("Pelayanan hotel ini sangat baik.") |
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``` |
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## Disclaimer |
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Do consider the biases which come from both the pre-trained RoBERTa model and `IndoLEM`'s Sentiment Analysis dataset that may be carried over into the results of this model. |
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## Author |
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Indonesian RoBERTa Base IndoLEM Sentiment Classifier was trained and evaluated by [Wilson Wongso](https://w11wo.github.io/). All computation and development are done on Google Colaboratory using their free GPU access. |
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