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---
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 |