Create README

README.md

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+---
+language: id
+tags:
+- roberta
+license: mit
+datasets:
+- indonlu
+widget:
+- text: "Hal-hal baik akan datang."
+---
+
+## Indo-roberta-emotion-classifier
+
+Indo-roberta-emotion-classifier is emotion classifier based on [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) model. It was trained on the trained on [IndoNLU EmoT](https://huggingface.co/datasets/indonlu) dataset. The model used was [Indo-roberta](https://huggingface.co/flax-community/indonesian-roberta-base) and was transfer-learned to an emotion classifier model. Based from the [IndoNLU bencmark](https://www.indobenchmark.com/), 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
+```python
+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 Sentiment Classifier was trained and evaluated by [Steven Limcorn](https://github.com/stevenlimcorn). All computation and development are done on Google Colaboratory using their free GPU access.