---
language:
  - th
license: mit
tags:
  - thai
  - nlp
  - ner
  - sentiment-analysis
  - question-answering
  - multi-task
  - transformer
  - pytorch
  - custom-model
datasets:
  - pythainlp/thainer-corpus-v2.2
  - pythainlp/wisesight_sentiment
  - iapp_wiki_qa_squad
pipeline_tag: token-classification
---

# 🇹🇭 Thai NLP Toolkit

A **multi-task NLP framework** for the Thai language built from scratch with PyTorch.

Uses a shared Transformer encoder backbone with three task-specific heads:

| Task | Head | Metric |
|------|------|--------|
| **Named Entity Recognition** | Token classification (7 labels) | Entity-level F1 |
| **Sentiment Analysis** | Sentence classification (3 labels) | Macro-F1 |
| **Question Answering** | Extractive span prediction | EM / F1 |

## Model Architecture

- **Tokenizer**: SentencePiece BPE (32K vocab) with Thai-specific preprocessing
- **Encoder**: 6-layer Transformer (d_model=256, 8 heads, d_ff=1024)
- **Max sequence length**: 512 tokens

## Usage

```python
# Clone the repository first
# git clone https://github.com/puttibenz/thai-nlp-toolkit.git

from inference.pipeline import ThaiNLPPipeline

pipeline = ThaiNLPPipeline(model_dir="path/to/downloaded/model", device="auto")

# NER
result = pipeline.predict("สมชายทำงานที่กรุงเทพ", task="ner")

# Sentiment Analysis
result = pipeline.predict("อาหารอร่อยมากครับ", task="sentiment")

# Question Answering
result = pipeline.predict(
    "กรุงเทพมหานครเป็นเมืองหลวงของประเทศไทย",
    task="qa",
    question="เมืองหลวงของประเทศไทยคืออะไร"
)
```

## Training Data

| Dataset | Task | Source |
|---------|------|--------|
| ThaiNER v2.2 | NER | `pythainlp/thainer-corpus-v2.2` |
| Wisesight Sentiment | Sentiment | `pythainlp/wisesight_sentiment` |
| iApp Thai Wiki QA | QA | `iapp_wiki_qa_squad` |

## Training Details

- **Framework**: PyTorch (custom implementation)
- **Training**: Multi-task learning with round-robin sampling
- **Optimizer**: AdamW with cosine LR schedule + warmup
- **Mixed Precision**: FP16 on CUDA
- **Batch Size**: 32 (×4 gradient accumulation = effective 128)

## File Structure

```
thai-nlp-toolkit/
├── checkpoint.pt              # Model weights
├── config.yaml                # Model architecture config
└── tokenizer/
    ├── thai_bpe.model         # SentencePiece BPE model
    └── tokenizer_config.json  # Tokenizer config
```

## Source Code

GitHub: [puttibenz/thai-nlp-toolkit](https://github.com/puttibenz/thai-nlp-toolkit)

## License

MIT