---
license: apache-2.0
base_model: Geotrend/bert-base-th-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: pos_thai
  results: []
language: th
widget:
- text: ภาษาไทย ง่าย นิดเดียว
  example_title: test1
- text: >-
    หนุ่ม เลี้ยง ควาย ใน อิสราเอล เผย รายได้ ต่อ เดือน ทำงาน 4 ปี สร้าง บ้าน ได้
    1 หลัง
  example_title: test2
datasets:
- lunarlist/tagging_thai
---
<!-- 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. -->

# pos_thai

This model is a fine-tuned version of [Geotrend/bert-base-th-cased](https://huggingface.co/Geotrend/bert-base-th-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0935
- Precision: 0.9525
- Recall: 0.9540
- F1: 0.9533
- Accuracy: 0.9693

## Model description

This model is train on thai pos_tag datasets to help with pos tagging in Thai language.

## Example

~~~
from transformers import AutoModelForTokenClassification, AutoTokenizer, TokenClassificationPipeline

model = AutoModelForTokenClassification.from_pretrained("lunarlist/pos_thai")
tokenizer = AutoTokenizer.from_pretrained("lunarlist/pos_thai")

pipeline = TokenClassificationPipeline(model=model, tokenizer=tokenizer, grouped_entities=True)
outputs = pipeline("ภาษาไทย ง่าย นิดเดียว")
print(outputs)
~~~


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1124        | 1.0   | 7344  | 0.1048          | 0.9505    | 0.9478 | 0.9492 | 0.9670   |
| 0.0866        | 2.0   | 14688 | 0.0935          | 0.9525    | 0.9540 | 0.9533 | 0.9693   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1