Create README.md

README.md

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+---
+language: lo
+tags:
+  - lao-roberta-base-pos-tagger
+license: mit
+widget:
+  - text: "ຮ້ອງ ມ່ວນ ແທ້ ສຽງດີ ອິຫຼີ"
+---
+
+## Lao RoBERTa Base POS Tagger
+
+Lao RoBERTa Base POS 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 [Lao RoBERTa Base](https://huggingface.co/w11wo/lao-roberta-base) model, which is then fine-tuned on the [`Yunshan Cup 2020`](https://github.com/GKLMIP/Yunshan-Cup-2020) dataset consisting of tag-labelled Lao corpus.
+
+After training, the model achieved an evaluation accuracy of 83.14%. On the benchmark test set, the model achieved an accuracy of 83.30%.
+
+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) |
+| ----------------------------- | ------- | ------------ | ------------------------------- |
+| `lao-roberta-base-pos-tagger` | 124M    | RoBERTa Base | `Yunshan Cup 2020`              |
+
+## Evaluation Results
+
+The model was trained for 15 epochs, with a batch size of 8, a learning rate of 5e-5, with cosine annealing to 0. The best model was loaded at the end.
+
+| Epoch | Training Loss | Validation Loss | Accuracy |
+| ----- | ------------- | --------------- | -------- |
+| 1     | 1.026100      | 0.733780        | 0.746021 |
+| 2     | 0.646900      | 0.659625        | 0.775688 |
+| 3     | 0.500400      | 0.576214        | 0.798523 |
+| 4     | 0.385400      | 0.606503        | 0.805269 |
+| 5     | 0.288000      | 0.652493        | 0.809092 |
+| 6     | 0.204600      | 0.671678        | 0.815216 |
+| 7     | 0.145200      | 0.704693        | 0.818209 |
+| 8     | 0.098700      | 0.830561        | 0.816998 |
+| 9     | 0.066100      | 0.883329        | 0.825232 |
+| 10    | 0.043900      | 0.933347        | 0.825664 |
+| 11    | 0.027200      | 0.992055        | 0.828449 |
+| 12    | 0.017300      | 1.054874        | 0.830819 |
+| 13    | 0.011500      | 1.081638        | 0.830940 |
+| 14    | 0.008500      | 1.094252        | 0.831304 |
+| 15    | 0.007400      | 1.097428        | 0.831442 |
+
+## How to Use
+
+### As Token Classifier
+
+```python
+from transformers import pipeline
+
+pretrained_name = "w11wo/lao-roberta-base-pos-tagger"
+
+nlp = pipeline(
+    "token-classification",
+    model=pretrained_name,
+    tokenizer=pretrained_name
+)
+
+nlp("ຮ້ອງ ມ່ວນ ແທ້ ສຽງດີ ອິຫຼີ")
+```
+
+## Disclaimer
+
+Do consider the biases which come from both the pre-trained RoBERTa model and the `Yunshan Cup 2020` dataset that may be carried over into the results of this model.
+
+## Author
+
+Lao RoBERTa Base POS 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.