
## 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.
{"language": "id", "license": "mit", "tags": ["indonesian-roberta-base-posp-tagger"], "datasets": ["indonlu"], "widget": [{"text": "Budi sedang pergi ke pasar."}]}
transformers pytorch tf safetensors roberta token-classification indonesian-roberta-base-posp-tagger id dataset:indonlu arxiv:1907.11692 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #safetensors #roberta #token-classification #indonesian-roberta-base-posp-tagger #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us 

Indonesian RoBERTa Base POSP Tagger
-----------------------------------


Indonesian RoBERTa Base POSP Tagger is a part-of-speech token-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on '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 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
-----



Evaluation Results
------------------


The model was trained for 10 epochs and the best model was loaded at the end.



How to Use
----------


### As Token Classifier


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. All computation and development are done on Google Colaboratory using their free GPU access.



### As Token Classifier


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. All computation and development are done on Google Colaboratory using their free GPU access.
TAGS
#transformers #pytorch #tf #safetensors #roberta #token-classification #indonesian-roberta-base-posp-tagger #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### As Token Classifier


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. All computation and development are done on Google Colaboratory using their free GPU access.
passage: TAGS
#transformers #pytorch #tf #safetensors #roberta #token-classification #indonesian-roberta-base-posp-tagger #id #dataset-indonlu #arxiv-1907.11692 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### As Token Classifier


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. All computation and development are done on Google Colaboratory using their free GPU access.