Update README.md

README.md

@@ -1,30 +1,9 @@
 ---
-language: id
-tags:
-  - indonesian-roberta-base-indolem-sentiment-classifier-fold-0
-license: mit
-datasets:
-  - indolem
-widget:
-  - text: "Pelayanan hotel ini sangat baik."
+language:
+- en
+license: apache-2.0
 ---
 
-## Indonesian RoBERTa Base IndoLEM Sentiment Classifier
-
-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).
-
-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.
-
-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.
-
-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-indolem-sentiment-classifier-fold-0` | 124M    | RoBERTa Base | `IndoLEM`'s Sentiment Analysis  |
-
 ## Evaluation Results
 
 The model was trained for 10 epochs and the best model was loaded at the end.
@@ -59,11 +38,3 @@ nlp = pipeline(
 
 nlp("Pelayanan hotel ini sangat baik.")
 ```
-
-## Disclaimer
-
-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.
-
-## Author
-
-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.