Create README.md

README.md

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+---
+language: id
+tags:
+  - indonesian-roberta-base-sentiment-classifier
+license: mit
+datasets:
+  - indonlu
+widget:
+  - text: "Jangan sampai saya telpon bos saya ya!"
+---
+
+## Indonesian RoBERTa Base Sentiment Classifier
+
+Indonesian RoBERTa Base 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 [`indonlu`](https://hf.co/datasets/indonlu)'s `SmSA` dataset consisting of Indonesian comments and reviews.
+
+After training, the model achieved an evaluation accuracy of 93.88% and F1-macro of 91.57%. On the benchmark test set, the model achieved an accuracy of 90.00% and F1-macro of 85.97%.
+
+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.
+
+## Model
+
+| Model                                          | #params | Arch.        | Training/Validation data (text) |
+| ---------------------------------------------- | ------- | ------------ | ------------------------------- |
+| `indonesian-roberta-base-sentiment-classifier` | 124M    | RoBERTa Base | `SmSA`                          |
+
+## Evaluation Results
+
+The model was trained for 5 epochs and the best model was loaded at the end.
+
+| Epoch | Training Loss | Validation Loss | Accuracy | F1       | Precision | Recall   |
+| ----- | ------------- | --------------- | -------- | -------- | --------- | -------- |
+| 1     | 0.346100      | 0.263456        | 0.915079 | 0.888680 | 0.877023  | 0.903502 |
+| 2     | 0.175200      | 0.215166        | 0.930952 | 0.908246 | 0.918557  | 0.898842 |
+| 3     | 0.111700      | 0.227525        | 0.932540 | 0.901823 | 0.916049  | 0.891263 |
+| 4     | 0.071800      | 0.244867        | 0.938889 | 0.915714 | 0.923105  | 0.909921 |
+| 5     | 0.055000      | 0.262004        | 0.935714 | 0.906755 | 0.918607  | 0.898044 |
+
+## How to Use
+
+### As Text Classifier
+
+```python
+from transformers import pipeline
+
+pretrained_name = "w11wo/indonesian-roberta-base-sentiment-classifier"
+
+nlp = pipeline(
+    "sentiment-analysis",
+    model=pretrained_name,
+    tokenizer=pretrained_name
+)
+
+nlp("Jangan sampai saya telpon bos saya ya!")
+```
+
+## Disclaimer
+
+Do consider the biases which come from both the pre-trained RoBERTa model and the `SmSA` dataset that may be carried over into the results of this model.
+
+## Author
+
+Indonesian RoBERTa Base 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.