---
base_model: airesearch/wangchanberta-base-att-spm-uncased
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- accuracy
- recall
- precision
- f1
model-index:
- name: wangchanberta-ud-thai-pud-upos
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: universal_dependencies
      type: universal_dependencies
      config: th_pud
      split: test
      args: th_pud
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9883334914161055
widget:
- text: นักวิจัยกล่าวว่าการวิเคราะห์ดีเอ็นเอของเนื้องอกอาจช่วยอธิบายถึงสาเหตุที่แท้จริงของมะเร็งชนิดอื่นๆ ได้
  example_title: test_example_1
- text: >-
    คือผมไม่ได้ชอบกดดันพวกคุณหรอกนะ แต่ชะตากรรมของสาธารณรัฐอยู่ในกำมือคุณ
  example_title: test_example_2
language:
- th
library_name: transformers
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# wangchanberta-ud-thai-pud-upos

This model is a fine-tuned version of [airesearch/wangchanberta-base-att-spm-uncased](https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncased) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0442
- Macro avg precision: 0.9221
- Macro avg recall: 0.9178
- Macro avg f1: 0.9199
- Weighted avg precision: 0.9883
- Weighted avg recall: 0.9883
- Weighted avg f1: 0.9883
- Accuracy: 0.9883

## Model description

This model is train on thai UD Thai PUD corpus with `Universal Part-of-speech (UPOS)` tag to help with pos tagging in Thai language.

## Example
```python
from transformers import AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline

model = AutoModelForTokenClassification.from_pretrained("Pavarissy/wangchanberta-ud-thai-pud-upos")
tokenizer = AutoTokenizer.from_pretrained("Pavarissy/wangchanberta-ud-thai-pud-upos")

pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer, grouped_entities=True)
outputs = pipeline("ประเทศไทย อยู่ใน ทวีป เอเชีย")
print(outputs)
# [{'entity_group': 'NOUN', 'score': 0.419697, 'word': '', 'start': 0, 'end': 1}, {'entity_group': 'PROPN', 'score': 0.8809489, 'word': 'ประเทศไทย', 'start': 0, 'end': 9}, {'entity_group': 'VERB', 'score': 0.7754166, 'word': 'อยู่ใน', 'start': 9, 'end': 16}, {'entity_group': 'NOUN', 'score': 0.9976932, 'word': 'ทวีป', 'start': 16, 'end': 21}, {'entity_group': 'PROPN', 'score': 0.97770107, 'word': 'เอเชีย', 'start': 21, 'end': 28}]
```

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro avg precision | Macro avg recall | Macro avg f1 | Weighted avg precision | Weighted avg recall | Weighted avg f1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------------:|:-------------------:|:---------------:|:--------:|
| No log        | 1.0   | 125  | 0.5563          | 0.8103              | 0.7235           | 0.7552       | 0.8574                 | 0.8522              | 0.8495          | 0.8522   |
| No log        | 2.0   | 250  | 0.2316          | 0.8701              | 0.8460           | 0.8564       | 0.9320                 | 0.9315              | 0.9310          | 0.9315   |
| No log        | 3.0   | 375  | 0.1635          | 0.8903              | 0.8729           | 0.8809       | 0.9511                 | 0.9511              | 0.9508          | 0.9511   |
| 0.5782        | 4.0   | 500  | 0.1112          | 0.9037              | 0.8964           | 0.8998       | 0.9687                 | 0.9685              | 0.9685          | 0.9685   |
| 0.5782        | 5.0   | 625  | 0.0860          | 0.9110              | 0.9050           | 0.9079       | 0.9752                 | 0.9752              | 0.9751          | 0.9752   |
| 0.5782        | 6.0   | 750  | 0.0675          | 0.9160              | 0.9103           | 0.9131       | 0.9815                 | 0.9814              | 0.9814          | 0.9814   |
| 0.5782        | 7.0   | 875  | 0.0588          | 0.9189              | 0.9138           | 0.9163       | 0.9839                 | 0.9839              | 0.9839          | 0.9839   |
| 0.1073        | 8.0   | 1000 | 0.0514          | 0.9214              | 0.9155           | 0.9184       | 0.9858                 | 0.9858              | 0.9858          | 0.9858   |
| 0.1073        | 9.0   | 1125 | 0.0463          | 0.9225              | 0.9171           | 0.9197       | 0.9877                 | 0.9876              | 0.9876          | 0.9876   |
| 0.1073        | 10.0  | 1250 | 0.0442          | 0.9221              | 0.9178           | 0.9199       | 0.9883                 | 0.9883              | 0.9883          | 0.9883   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1