---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
datasets:
- universal_dependencies
metrics:
- accuracy
- precision
- recall
model-index:
- name: mdeberta-v3-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: 0.9934846474601972
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. -->

# mdeberta-v3-ud-thai-pud-upos

This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the universal_dependencies dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0303
- Macro avg precision: 0.9235
- Macro avg recall: 0.9228
- Macro avg f1: 0.9231
- Weighted avg precision: 0.9935
- Weighted avg recall: 0.9935
- Weighted avg f1: 0.9935
- Accuracy: 0.9935

## 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/mdeberta-v3-ud-thai-pud-upos")
tokenizer = AutoTokenizer.from_pretrained("Pavarissy/mdeberta-v3-ud-thai-pud-upos")

pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer, grouped_entities=True)
outputs = pipeline("ประเทศไทย อยู่ใน ทวีป เอเชีย")
print(outputs)
# [{'entity_group': 'PROPN', 'score': 0.9946701, 'word': 'ประเทศไทย', 'start': 0, 'end': 9}, {'entity_group': 'VERB', 'score': 0.85809743, 'word': 'อยู่ใน', 'start': 9, 'end': 16}, {'entity_group': 'NOUN', 'score': 0.99632, 'word': 'ทวีป', 'start': 16, 'end': 21}, {'entity_group': 'PROPN', 'score': 0.9961184, '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  | 0.3898          | 0.8417              | 0.7849           | 0.8078       | 0.9119                 | 0.9112              | 0.9101          | 0.9112   |
| No log        | 2.0   | 250  | 0.1768          | 0.8765              | 0.8683           | 0.8720       | 0.9561                 | 0.9560              | 0.9559          | 0.9560   |
| No log        | 3.0   | 375  | 0.1217          | 0.8972              | 0.8892           | 0.8929       | 0.9701                 | 0.9701              | 0.9699          | 0.9701   |
| 0.4709        | 4.0   | 500  | 0.0841          | 0.9057              | 0.9064           | 0.9059       | 0.9802                 | 0.9800              | 0.9800          | 0.9800   |
| 0.4709        | 5.0   | 625  | 0.0649          | 0.9128              | 0.9133           | 0.9130       | 0.9854                 | 0.9853              | 0.9853          | 0.9853   |
| 0.4709        | 6.0   | 750  | 0.0513          | 0.9147              | 0.9170           | 0.9158       | 0.9878                 | 0.9877              | 0.9877          | 0.9877   |
| 0.4709        | 7.0   | 875  | 0.0423          | 0.9199              | 0.9180           | 0.9189       | 0.9900                 | 0.9900              | 0.9900          | 0.9900   |
| 0.0857        | 8.0   | 1000 | 0.0350          | 0.9226              | 0.9207           | 0.9216       | 0.9921                 | 0.9921              | 0.9921          | 0.9921   |
| 0.0857        | 9.0   | 1125 | 0.0318          | 0.9237              | 0.9219           | 0.9228       | 0.9932                 | 0.9932              | 0.9932          | 0.9932   |
| 0.0857        | 10.0  | 1250 | 0.0303          | 0.9235              | 0.9228           | 0.9231       | 0.9935                 | 0.9935              | 0.9935          | 0.9935   |


### Framework versions

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