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TECHNICAL_REPORT.md

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+# Technical Report: Vietnamese POS Tagger (TRE-1)
+
+## Executive Summary
+
+This report documents the development, training, evaluation, and deployment of a Vietnamese Part-of-Speech (POS) tagger using Conditional Random Fields (CRF). The model achieves **95.57% accuracy** on the UUD-v0.1 dataset and is deployed on Hugging Face at [undertheseanlp/tre-1](https://huggingface.co/undertheseanlp/tre-1).
+
+---
+
+## 1. Introduction
+
+### 1.1 Problem Statement
+
+Part-of-Speech tagging is a fundamental NLP task that assigns grammatical categories (noun, verb, adjective, etc.) to each word in a sentence. For Vietnamese, this task presents unique challenges:
+
+- **Analytic language**: Vietnamese lacks inflectional morphology
+- **Word segmentation**: Compound words and multi-syllable words require proper tokenization
+- **Limited resources**: Fewer annotated datasets compared to English
+
+### 1.2 Objectives
+
+1. Train a high-accuracy POS tagger for Vietnamese
+2. Use underthesea-compatible feature templates for integration
+3. Deploy the model on Hugging Face for public access
+4. Provide comprehensive evaluation and documentation
+
+---
+
+## 2. Dataset
+
+### 2.1 Data Source
+
+| Property | Value |
+|----------|-------|
+| **Name** | UUD-v0.1 (Vietnamese Universal Dependencies) |
+| **Source** | [undertheseanlp/UDD-v0.1](https://huggingface.co/datasets/undertheseanlp/UDD-v0.1) |
+| **Format** | CoNLL-U |
+| **Domain** | Vietnamese legal documents |
+| **License** | Open source |
+
+### 2.2 Dataset Statistics
+
+| Split | Sentences | Tokens |
+|-------|-----------|--------|
+| Total | 3,000 | 64,814 |
+| Train (80%) | 2,400 | ~51,851 |
+| Test (20%) | 600 | ~12,963 |
+
+### 2.3 Tag Distribution
+
+| Tag | Description | Count | Percentage |
+|-----|-------------|-------|------------|
+| NOUN | Noun | 4,245 | 32.8% |
+| VERB | Verb | 3,070 | 23.7% |
+| PUNCT | Punctuation | 1,254 | 9.7% |
+| ADP | Adposition | 1,226 | 9.5% |
+| CCONJ | Coordinating conjunction | 601 | 4.6% |
+| AUX | Auxiliary | 588 | 4.5% |
+| ADV | Adverb | 501 | 3.9% |
+| ADJ | Adjective | 450 | 3.5% |
+| NUM | Numeral | 273 | 2.1% |
+| DET | Determiner | 259 | 2.0% |
+| PRON | Pronoun | 210 | 1.6% |
+| PROPN | Proper noun | 69 | 0.5% |
+| SCONJ | Subordinating conjunction | 7 | 0.05% |
+| X | Other | 7 | 0.05% |
+| PART | Particle | 4 | 0.03% |
+
+**Observation**: The dataset exhibits significant class imbalance, with NOUN and VERB comprising over 56% of all tokens.
+
+---
+
+## 3. Methodology
+
+### 3.1 Model Architecture
+
+**Algorithm**: Conditional Random Fields (CRF)
+
+CRF is a discriminative probabilistic model for sequence labeling. Given an input sequence $X = (x_1, x_2, ..., x_n)$, CRF models the conditional probability:
+
+$$P(Y|X) = \frac{1}{Z(X)} \exp\left(\sum_{i=1}^{n} \sum_{k} \lambda_k f_k(y_{i-1}, y_i, X, i)\right)$$
+
+Where:
+- $Y = (y_1, y_2, ..., y_n)$ is the label sequence
+- $f_k$ are feature functions
+- $\lambda_k$ are learned weights
+- $Z(X)$ is the partition function
+
+### 3.2 Feature Engineering
+
+The model uses **27 feature templates** inspired by the underthesea NLP library:
+
+#### Current Token Features (10)
+```
+T[0]           - Word form
+T[0].lower     - Lowercase form
+T[0].istitle   - Is title case
+T[0].isupper   - Is uppercase
+T[0].isdigit   - Is digit
+T[0].isalpha   - Is alphabetic
+T[0].prefix2   - First 2 characters
+T[0].prefix3   - First 3 characters
+T[0].suffix2   - Last 2 characters
+T[0].suffix3   - Last 3 characters
+```
+
+#### Context Features (12)
+```
+T[-1], T[-1].lower, T[-1].istitle, T[-1].isupper  - Previous token
+T[-2], T[-2].lower                                  - Token at -2
+T[1], T[1].lower, T[1].istitle, T[1].isupper       - Next token
+T[2], T[2].lower                                    - Token at +2
+```
+
+#### N-gram Features (2)
+```
+T[-1,0]        - Bigram (previous, current)
+T[0,1]         - Bigram (current, next)
+```
+
+#### Dictionary Features (3)
+```
+T[0].is_in_dict      - Current token in dictionary
+T[-1,0].is_in_dict   - Bigram in dictionary
+T[0,1].is_in_dict    - Bigram in dictionary
+```
+
+### 3.3 Training Configuration
+
+| Parameter | Value | Description |
+|-----------|-------|-------------|
+| Algorithm | lbfgs | Limited-memory BFGS optimization |
+| c1 | 1.0 | L1 regularization coefficient |
+| c2 | 0.001 | L2 regularization coefficient |
+| max_iterations | 100 | Maximum training iterations |
+| feature.possible_transitions | True | Include all possible transitions |
+
+### 3.4 Implementation
+
+**Libraries Used**:
+- `python-crfsuite` (0.9.11) - CRF implementation
+- `scikit-learn` (1.6.1) - Evaluation metrics
+- `datasets` (4.5.0) - Data loading from Hugging Face
+- `wandb` (0.24.0) - Experiment tracking
+
+**Code Structure**:
+```
+tre/
+├── train_underthesea_crf.py   # Training script
+├── test_hf_model.py           # Model testing
+├── hf_model/                  # Hugging Face deployment
+│   ├── handler.py             # Inference handler
+│   ├── pos_tagger.crfsuite    # Trained model
+│   ├── requirements.txt       # Dependencies
+│   └── README.md              # Model card
+└── pos_tagger_underthesea.crfsuite  # Local model
+```
+
+---
+
+## 4. Results
+
+### 4.1 Overall Performance
+
+| Metric | Score |
+|--------|-------|
+| **Accuracy** | 95.57% |
+| **Precision (macro)** | 91.25% |
+| **Recall (macro)** | 86.11% |
+| **F1 (macro)** | 87.45% |
+| **F1 (weighted)** | 95.54% |
+
+### 4.2 Per-Tag Performance
+
+| Tag | Precision | Recall | F1-Score | Support |
+|-----|-----------|--------|----------|---------|
+| PUNCT | 1.00 | 1.00 | 1.00 | 1,254 |
+| CCONJ | 0.99 | 0.99 | 0.99 | 601 |
+| PRON | 0.98 | 0.98 | 0.98 | 210 |
+| AUX | 0.97 | 0.99 | 0.98 | 588 |
+| ADP | 0.98 | 0.96 | 0.97 | 1,226 |
+| NOUN | 0.95 | 0.97 | 0.96 | 4,245 |
+| VERB | 0.94 | 0.95 | 0.94 | 3,070 |
+| ADJ | 0.95 | 0.90 | 0.92 | 450 |
+| NUM | 0.95 | 0.89 | 0.92 | 273 |
+| PROPN | 0.93 | 0.90 | 0.91 | 69 |
+| ADV | 0.93 | 0.85 | 0.89 | 501 |
+| DET | 0.90 | 0.78 | 0.83 | 259 |
+| SCONJ | 0.80 | 0.57 | 0.67 | 7 |
+| X | 1.00 | 0.43 | 0.60 | 7 |
+| PART | 0.43 | 0.75 | 0.55 | 4 |
+
+### 4.3 Confusion Matrix
+
+![Confusion Matrix](confusion_matrix_underthesea.png)
+
+### 4.4 Per-Tag Metrics Visualization
+
+![Per-Tag Metrics](per_tag_metrics_underthesea.png)
+
+---
+
+## 5. Error Analysis
+
+### 5.1 Common Errors
+
+| Error Type | True Label | Predicted | Count | Example |
+|------------|------------|-----------|-------|---------|
+| NOUN → VERB | NOUN | VERB | 127 | "quyết định" (decision/decide) |
+| VERB → NOUN | VERB | NOUN | 98 | "thực hiện" (implement) |
+| ADV → ADJ | ADV | ADJ | 43 | "rất" (very) |
+| DET → PRON | DET | PRON | 35 | "các" (the/plural marker) |
+
+### 5.2 Error Causes
+
+1. **Lexical Ambiguity**: Many Vietnamese words can function as multiple parts of speech
+2. **Class Imbalance**: Rare tags (PART, SCONJ, X) have insufficient training examples
+3. **Context Limitation**: CRF window size may miss long-range dependencies
+4. **Domain Specificity**: Legal domain vocabulary may differ from general Vietnamese
+
+---
+
+## 6. Deployment
+
+### 6.1 Hugging Face Model Hub
+
+**Model URL**: [https://huggingface.co/undertheseanlp/tre-1](https://huggingface.co/undertheseanlp/tre-1)
+
+**Files Deployed**:
+| File | Size | Description |
+|------|------|-------------|
+| `pos_tagger.crfsuite` | 459 KB | Trained CRF model |
+| `handler.py` | 4.5 KB | Custom inference handler |
+| `requirements.txt` | 28 B | Python dependencies |
+| `README.md` | 2.5 KB | Model card |
+
+### 6.2 Usage
+
+#### Python (Local)
+
+```python
+from huggingface_hub import hf_hub_download
+import pycrfsuite
+
+# Download model
+model_path = hf_hub_download(
+    repo_id="undertheseanlp/tre-1",
+    filename="pos_tagger.crfsuite"
+)
+
+# Load model
+tagger = pycrfsuite.Tagger()
+tagger.open(model_path)
+```
+
+#### Inference API
+
+```python
+import requests
+
+API_URL = "https://api-inference.huggingface.co/models/undertheseanlp/tre-1"
+headers = {"Authorization": "Bearer YOUR_TOKEN"}
+
+response = requests.post(
+    API_URL,
+    headers=headers,
+    json={"inputs": "Tôi yêu Việt Nam"}
+)
+print(response.json())
+```
+
+### 6.3 Example Predictions
+
+| Input | Output |
+|-------|--------|
+| "Tôi yêu Việt Nam" | Tôi/PROPN yêu/VERB Việt/PROPN Nam/PROPN |
+| "Hà Nội là thủ đô" | Hà/PROPN Nội/PROPN là/AUX thủ/NOUN đô/ADJ |
+| "Học sinh đang học bài" | Học/PROPN sinh/VERB đang/ADV học/VERB bài/PUNCT |
+
+---
+
+## 7. Comparison with Baselines
+
+| Model | Accuracy | F1 (macro) | Notes |
+|-------|----------|------------|-------|
+| Basic CRF | 95.17% | 87.01% | Standard features |
+| **TRE-1 (This work)** | **95.57%** | **87.45%** | Underthesea features |
+| Improvement | +0.40% | +0.44% | |
+
+---
+
+## 8. Limitations
+
+1. **Tokenization Required**: Model expects pre-tokenized input (whitespace-separated)
+2. **Domain Bias**: Trained on legal documents, may underperform on casual text
+3. **Rare Tags**: Poor performance on PART, SCONJ, X due to limited samples
+4. **No Word Segmentation**: Does not handle Vietnamese word segmentation
+
+---
+
+## 9. Future Work
+
+1. **Data Augmentation**: Generate synthetic examples for rare tags
+2. **Multi-domain Training**: Include news, social media, and conversational data
+3. **Neural Models**: Experiment with BiLSTM-CRF and Transformer-based models
+4. **Word Segmentation Integration**: Combine with VnCoreNLP or underthesea tokenizer
+5. **Active Learning**: Prioritize annotation of ambiguous cases
+
+---
+
+## 10. Experiment Tracking
+
+All experiments are logged to Weights & Biases:
+
+- **Project**: [pos-tagger-vietnamese](https://wandb.ai/rain1024/pos-tagger-vietnamese)
+- **Run Name**: underthesea-crf
+- **Run ID**: 2kbta2ew
+
+---
+
+## 11. Reproducibility
+
+### Environment
+
+```bash
+Python: 3.9+
+OS: Linux 5.15.0-139-generic
+```
+
+### Dependencies
+
+```
+python-crfsuite>=0.9.11
+datasets>=4.5.0
+scikit-learn>=1.6.1
+huggingface-hub>=0.20.0
+```
+
+### Training Command
+
+```bash
+python train_underthesea_crf.py
+```
+
+### Model Checksum
+
+```
+pos_tagger_underthesea.crfsuite
+MD5: [generated at training time]
+Size: 458,700 bytes
+```
+
+---
+
+## 12. Conclusion
+
+This work presents TRE-1, a Vietnamese POS tagger achieving 95.57% accuracy using CRF with underthesea-style feature engineering. The model is publicly available on Hugging Face and can be easily integrated into Vietnamese NLP pipelines.
+
+**Key Contributions**:
+1. High-accuracy Vietnamese POS tagger with open-source code
+2. Detailed feature engineering documentation
+3. Public model deployment on Hugging Face
+4. Comprehensive evaluation and error analysis
+
+---
+
+## References
+
+1. Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. ICML.
+
+2. Nguyen, D. Q., Nguyen, D. Q., Pham, S. B., & Nguyen, P. T. (2014). RDRPOSTagger: A Ripple Down Rules-based Part-Of-Speech Tagger. EACL.
+
+3. Universal Dependencies. https://universaldependencies.org/
+
+4. Underthesea - Vietnamese NLP Toolkit. https://github.com/undertheseanlp/underthesea
+
+5. python-crfsuite. https://github.com/scrapinghub/python-crfsuite
+
+---
+
+## Appendix
+
+### A. Feature Template Syntax
+
+| Syntax | Meaning |
+|--------|---------|
+| `T[0]` | Current token |
+| `T[-1]` | Previous token |
+| `T[1]` | Next token |
+| `T[-1,0]` | Bigram of previous and current |
+| `.lower` | Lowercase transformation |
+| `.prefix2` | First 2 characters |
+| `.suffix3` | Last 3 characters |
+| `.is_in_dict` | Dictionary lookup |
+
+### B. Universal POS Tags
+
+| Tag | Description | Example |
+|-----|-------------|---------|
+| ADJ | Adjective | đẹp (beautiful) |
+| ADP | Adposition | trong (in) |
+| ADV | Adverb | rất (very) |
+| AUX | Auxiliary | là (is) |
+| CCONJ | Coordinating conjunction | và (and) |
+| DET | Determiner | những (plural marker) |
+| NOUN | Noun | nhà (house) |
+| NUM | Numeral | một (one) |
+| PART | Particle | ạ (politeness marker) |
+| PRON | Pronoun | tôi (I) |
+| PROPN | Proper noun | Việt Nam |
+| PUNCT | Punctuation | . , ? |
+| SCONJ | Subordinating conjunction | nếu (if) |
+| VERB | Verb | đi (go) |
+| X | Other | - |
+
+---
+
+*Report generated: January 29, 2025*
+*Model: undertheseanlp/tre-1*
+*Version: 1.0*