Create README.md

README.md

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+---
+language: id
+tags:
+  - indonesian-roberta-base-posp-tagger
+license: mit
+datasets:
+  - indonlu
+widget:
+  - text: "Budi sedang pergi ke pasar."
+---
+
+## Indonesian RoBERTa Base POSP Tagger
+
+Indonesian RoBERTa Base POSP Tagger is a part-of-speech token-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 `POSP` dataset consisting of tag-labelled news.
+
+After training, the model achieved an evaluation F1-macro of 95.34%. On the benchmark test set, the model achieved an accuracy of 93.99% and F1-macro of 88.93%.
+
+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-posp-tagger` | 124M    | RoBERTa Base | `POSP`                          |
+
+## Evaluation Results
+
+The model was trained for 10 epochs and the best model was loaded at the end.
+
+| Epoch | Training Loss | Validation Loss | Precision | Recall   | F1       | Accuracy |
+| ----- | ------------- | --------------- | --------- | -------- | -------- | -------- |
+| 1     | 0.898400      | 0.343731        | 0.894324  | 0.894324 | 0.894324 | 0.894324 |
+| 2     | 0.294700      | 0.236619        | 0.929620  | 0.929620 | 0.929620 | 0.929620 |
+| 3     | 0.214100      | 0.202723        | 0.938349  | 0.938349 | 0.938349 | 0.938349 |
+| 4     | 0.171100      | 0.183630        | 0.945264  | 0.945264 | 0.945264 | 0.945264 |
+| 5     | 0.143300      | 0.169744        | 0.948469  | 0.948469 | 0.948469 | 0.948469 |
+| 6     | 0.124700      | 0.174946        | 0.947963  | 0.947963 | 0.947963 | 0.947963 |
+| 7     | 0.109800      | 0.167450        | 0.951590  | 0.951590 | 0.951590 | 0.951590 |
+| 8     | 0.101300      | 0.163191        | 0.952475  | 0.952475 | 0.952475 | 0.952475 |
+| 9     | 0.093500      | 0.163255        | 0.953361  | 0.953361 | 0.953361 | 0.953361 |
+| 10    | 0.089000      | 0.164673        | 0.953445  | 0.953445 | 0.953445 | 0.953445 |
+
+## How to Use
+
+### As Token Classifier
+
+```python
+from transformers import pipeline
+
+pretrained_name = "w11wo/indonesian-roberta-base-posp-tagger"
+
+nlp = pipeline(
+    "token-classification",
+    model=pretrained_name,
+    tokenizer=pretrained_name
+)
+
+nlp("Budi sedang pergi ke pasar.")
+```
+
+## Disclaimer
+
+Do consider the biases which come from both the pre-trained RoBERTa model and the `POSP` dataset that may be carried over into the results of this model.
+
+## Author
+
+Indonesian RoBERTa Base POSP Tagger 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.