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---
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 |