You can deploy this model and start earning money today!

JonusNattapong/
OpenThai-NER

Name: OpenThai-NER
Availability: InStock

phayathaibert-thainer

JonusNattapong/OpenThai-NER

Pavarissy/phayathaibert-thainer

Views

No views yet

OpenThai-NER

License: CC BY 3.0

Python

OpenThai-NER is a production-grade Named Entity Recognition (NER) library and model for the Thai language. It builds upon Pavarissy/phayathaibert-thainer and fine-tunes on the multi-domain OpenThai-NER-Corpus with subword span reconstruction, numerical stability fixes, an optional linear-chain CRF decoding layer, and INT8 ONNX export for low-latency CPU deployment.

Key Features

Numerical Stability: Eliminates training instability (eval_loss: NaN) by replacing mixed-precision fp16 with bf16/fp32, applying gradient clipping (max_grad_norm=1.0), and strictly masking padding/special tokens (-100) in DataCollatorForTokenClassification.
Dataset Quality: Normalized 8,601 valid samples across 407 domains. Resolved syntax typos (DTAE → DATE) and consolidated synonym tags (LOC → LOCATION, ORG → ORGANIZATION, PER → PERSON).
Subword Span Reconstruction: Automatically aggregates SentencePiece subword fragments into complete Thai word spans with exact character start and end offsets.
Decoding Architecture: Implements a pure PyTorch linear-chain CRF layer with Viterbi decoding and BIO transition constraints to prevent structural tag violations.
Class Imbalance Mitigation: Implements Focal Loss with smoothed inverse class frequency weights to improve recall on rare entity categories.
Hardware Acceleration: Provides dynamic INT8 quantization via ONNX Runtime, reducing latency by 3.8× on CPU environments.

Benchmark & Evaluation

Evaluated on data/test.jsonl (1,092 test sequences across 407 domains) using seqeval strict span-level matching:

Metric Summary

Metric	OpenThai-NER (Final)	WangchanBERTa Base	PyThaiNLP ThaiNER-v2	PhayaThaiBERT Baseline
Strict Span F1	79.28%	78.10%	76.40%	71.20%
Precision	78.87%	78.20%	75.90%	70.80%
Recall	79.70%	78.00%	76.90%	71.60%
Token Accuracy	90.76%	89.90%	88.50%	85.20%
Validation Loss	0.3697	0.3850	N/A	NaN (Unstable)
CPU Latency (INT8)	11.2 ms/seq	18.5 ms/seq	15.1 ms/seq	42.6 ms/seq

Directory Structure

text

OpenThai/
├── openthai_ner/                 # Core Python package
│   ├── __init__.py               # Package entrypoint
│   ├── pipeline.py               # Inference pipeline & span reconstruction
│   ├── crf.py                    # Linear-chain CRF with Viterbi decoding
│   ├── losses.py                 # Focal Loss & class weighting
│   ├── model_crf.py              # Combined Transformer + CRF model class
│   └── utils.py                  # Offset alignment & HTML rendering
├── scripts/
│   ├── clean_dataset.py          # Dataset cleaner & stratified split
│   ├── export_onnx.py            # ONNX export & INT8 quantization
│   ├── evaluate_benchmark.py     # Evaluation script (seqeval)
│   ├── benchmark_sota.py         # Multi-model comparative benchmark
│   └── build_package.py          # Packaging script for PyPI release
├── notebooks/
│   └── OpenThai_NER_FineTuning_Final.ipynb  # Google Colab GPU training notebook
├── space_deploy/                 # Standalone Hugging Face Space application
│   ├── app.py
│   ├── README.md
│   └── requirements.txt
├── data/                         # Processed data splits
│   ├── train.jsonl               # 6,792 training sequences
│   ├── val.jsonl                 # 717 validation sequences
│   ├── test.jsonl                # 1,092 test sequences
│   └── label_map.json            # Canonical 128-tag label mapping
├── train_ner.py                  # Self-contained training script
├── app.py                        # Local Gradio web demo
├── pyproject.toml                # Build configuration
└── README.md

Installation

From Source

bash

git clone https://github.com/JonusNattapong/OpenThai.git
cd OpenThai
pip install -r requirements.txt

Install as Package

bash

pip install .
# Or directly via Git:
pip install git+https://github.com/JonusNattapong/OpenThai.git

Quickstart

Basic Inference

python

from openthai_ner import OpenThaiNER

ner = OpenThaiNER("JonusNattapong/OpenThai-NER")

text = "นายสมชาย เข็มกลัด เดินทางไปประชุมที่กระทรวงการคลัง ถนนพระราม 6 ในวันที่ 15 มกราคม"
entities = ner.predict(text, threshold=0.5)

for ent in entities:
    print(f"[{ent['entity']}] '{ent['word']}' (Span: {ent['start']}:{ent['end']}, Score: {ent['score']:.4f})")

Output:

text

[PERSON] 'นายสมชาย เข็มกลัด' (Span: 0:17, Score: 0.9812)
[ORGANIZATION] 'กระทรวงการคลัง' (Span: 36:50, Score: 0.9924)
[LOCATION] 'ถนนพระราม 6' (Span: 51:62, Score: 0.9540)
[DATE] 'วันที่ 15 มกราคม' (Span: 66:82, Score: 0.9715)

HTML Rendering (Notebooks & Web)

python

html_output = ner.render_html(text)
# In Jupyter Notebook:
# from IPython.display import HTML; display(HTML(html_output))

Fast Inference with ONNX Runtime

python

from openthai_ner import OpenThaiNER

ner_onnx = OpenThaiNER(
    "JonusNattapong/OpenThai-NER",
    onnx_path="models/onnx/openthai_ner_quantized.onnx"
)
results = ner_onnx.predict("ธนาคารแห่งประเทศไทย ประกาศปรับลดอัตราดอกเบี้ย")

Training

The training script is self-contained and handles dataset downloading, subword alignment, and metric logging automatically.

Run on Local GPU or Colab

bash

python train_ner.py \
  --model_name Pavarissy/phayathaibert-thainer \
  --data_dir data \
  --output_dir models/openthai-ner-final \
  --epochs 3 \
  --batch_size 16 \
  --learning_rate 2e-5

Advanced Options (CRF & Focal Loss)

bash

# Train with Linear-Chain CRF Layer
python train_ner.py --use_crf --output_dir models/openthai-ner-crf

# Train with Focal Loss for class imbalance
python train_ner.py --loss_type focal --focal_gamma 2.0

Google Colab Execution

Run directly via the Google Colab CLI:

colab run --gpu T4 train_ner.py

Or open notebooks/OpenThai_NER_FineTuning_Final.ipynb in Google Colab.

Model Export & Optimization

ONNX Export & INT8 Quantization

bash

python scripts/export_onnx.py \
  --model JonusNattapong/OpenThai-NER \
  --output_dir models/onnx

Multi-Model Benchmark

python scripts/benchmark_sota.py --test_file data/test.jsonl --samples 100

Web Demo

Run the interactive Gradio demo locally:

python app.py

To deploy directly to Hugging Face Spaces, push the contents of space_deploy/ to your Space repository.

Supported Entities

The canonical schema covers 128 BIO labels across the following core categories:

Agents: PERSON, ORGANIZATION
Locations & Facilities: LOCATION, FACILITY
Temporal & Quantities: DATE, TIME, MONEY, PERCENT
Identifiers & Contact: ID, ACCOUNT, PHONE, EMAIL, URL
Specialized Domains: LAW, PRODUCT, DISEASE, TECHNOLOGY

Citation

bibtex

@software{openthai_ner2026,
  author = {Nattapong Tapachoom},
  title = {OpenThai-NER: Production-Ready Thai Named Entity Recognition},
  url = {https://github.com/JonusNattapong/OpenThai},
  version = {0.1.0},
  year = {2026}
}

License

This project is released under the Creative Commons Attribution 3.0 Unported (CC BY 3.0) license.