Update README.md

README.md

@@ -9,62 +9,8 @@ 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 94.36% and F1-macro of 92.42%. On the benchmark test set, the model achieved an accuracy of 93.2% and F1-macro of 91.02%.
-
-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     | 0.342600      | 0.213551        | 0.928571 | 0.898539 | 0.909803  | 0.890694 |
-| 2     | 0.190700      | 0.213466        | 0.934127 | 0.901135 | 0.925297  | 0.882757 |
-| 3     | 0.125500      | 0.219539        | 0.942857 | 0.920901 | 0.927511  | 0.915193 |
-| 4     | 0.083600      | 0.235232        | 0.943651 | 0.924227 | 0.926494  | 0.922048 |
-| 5     | 0.059200      | 0.262473        | 0.942063 | 0.920583 | 0.924084  | 0.917351 |
-
-## 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.
-
 ## Citation
 
-If used, please cite the following:
-
 ```bibtex
 @misc {wilson_wongso_2023,
 	author       = { {Wilson Wongso} },