README.md

---
language:
  - vi
license: cc-by-sa-4.0
multilinguality: monolingual
size_categories:
  - 1M<n<10M
source_datasets:
  - wikimedia/wikipedia
task_categories:
  - text-generation
task_ids:
  - language-modeling
tags:
  - vietnamese
  - benchmark
  - quantization
  - perplexity
  - llm-evaluation
  - wikitext-style
  - nlp
pretty_name: ViWiki-Bench
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/vi_wiki_train.txt
      - split: validation
        path: data/vi_wiki_valid.txt
      - split: test
        path: data/vi_wiki_test.txt
dataset_info:
  features:
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 2079483
      num_examples: 1
    - name: validation
      num_bytes: 211415
      num_examples: 1
    - name: test
      num_bytes: 2081195
      num_examples: 1
  download_size: 4372093
  dataset_size: 4372093
---

# ViWiki-Bench 🇻🇳

**Vietnamese benchmark dataset for LLM quantization perplexity evaluation.**

ViWiki-Bench is the Vietnamese equivalent of [WikiText-2](https://huggingface.co/datasets/Salesforce/wikitext),
designed specifically to evaluate quality degradation of quantized Large Language Models (LLMs) on Vietnamese text.
It follows the same **continuous-stream** methodology as WikiText-2, enabling drop-in replacement
in any existing evaluation pipeline.

---

## Dataset Summary

| Split        | Characters  | Words (~)  | Paragraphs (~) |
|--------------|-------------|------------|----------------|
| `train`      | 2,079,483   | 435,385    | 6,600          |
| `validation` | 211,415     | 43,996     | 670            |
| `test`       | 2,081,195   | 435,672    | 6,605          |
| **Total**    | **4,372,093** | **915,053** | **~13,875**  |

**Reference — WikiText-2 English:**

| Split        | Characters  | Words   |
|--------------|-------------|---------|
| `train`      | 2,051,904   | 238,854 |
| `validation` | 217,646     | 25,877  |
| `test`       | 2,088,628   | 245,569 |

> **Note:** Vietnamese word count is higher than English at equivalent character count because
> Vietnamese words average 1.7–2.2 characters vs. 4.5–5.0 for English.

---

## Motivation

Existing quantization benchmarks — WikiText-2, WikiText-103, C4 — are **English-only**.
When quantizing multilingual or Vietnamese-specific models (e.g., Vistral, PhoGPT, SeaLLM, Qwen-vi),
evaluating on English data does not reflect real-world Vietnamese performance for two reasons:

1. **Different token distribution.** Vietnamese tonal markers, compound vowels, and morphology cause
   BPE tokenizers to fragment Vietnamese text at **1.8–2.5× the rate of English** on the same tokenizer.
   This makes English perplexity scores incomparable to Vietnamese ones.

2. **Language-specific quantization effects.** Quantization quality varies significantly across languages
   because activation and weight distributions differ per language in multilingual models. A method
   that preserves English quality well may degrade Vietnamese significantly.

ViWiki-Bench provides a **Vietnamese-native ground truth** to measure this fairly.

---

## Source Data

**Primary source:** [`wikimedia/wikipedia`](https://huggingface.co/datasets/wikimedia/wikipedia),
config `20231101.vi` — the full Vietnamese Wikipedia dump from November 2023
(~1.34 million articles, ~1.5 GB).

**Fallback sources** (used automatically if primary fails):
- `uonlp/CulturaX` (vi)
- `allenai/c4` (vi)

### Why Wikipedia?

| Source                  | Size   | Quality | Topic Diversity | Reproducible |
|-------------------------|--------|---------|-----------------|--------------|
| Wikipedia vi (20231101) | 1.3 GB | High    | High            | ✅ |
| CC-100 vi               | 39 GB  | Medium  | High            | Difficult |
| OSCAR vi                | 8.3 GB | Medium  | High            | Difficult |
| MC4 vi                  | 1.1 GB | Medium  | Medium          | ✅ |
| VnExpress corpus        | 0.5 GB | High    | Low             | ❌ |

Wikipedia provides community-reviewed text with neutral style, broad topic coverage,
and consistent Vietnamese orthography — ideal properties for a language model benchmark.

---

## Data Processing Pipeline

The raw Wikipedia text goes through a **5-step cleaning pipeline**, mirroring WikiText-103's methodology:

**Step 1 — Remove Wiki markup**
Strip templates `{{...}}`, tables `{|...|}`, reference tags `<ref>...</ref>`, and HTML tags.

**Step 2 — Resolve links**
Replace `[[link|text]]` with `text` to preserve sentence continuity.

**Step 3 — Unicode NFC normalization** *(critical for Vietnamese)*
Vietnamese characters can be encoded in two Unicode forms:
- Composed: `e` + combining hook + combining dot below
- Precomposed: single codepoint `ệ`

NFC normalization ensures consistency across articles from different contributors,
preventing tokenization artifacts.

**Step 4 — Remove section headers**
Lines of the form `=== Title ===` are removed (following WikiText convention),
keeping only prose content.

**Step 5 — Whitespace normalization**
Collapse multiple spaces, remove redundant blank lines.

### Paragraph Quality Filter

After cleaning, each paragraph passes a 3-condition quality filter:

```
keep(p) = True  iff:
  len(p) >= 150 chars
  AND  alpha_ratio(p) >= 0.55
  AND  contains at least one Vietnamese-specific vowel (ă, â, ê, ô, ơ, ư, ...)
```

The Vietnamese vowel check removes foreign-language text that appears in Vietnamese Wikipedia.

### Continuous Stream Construction

Filtered paragraphs are shuffled with a fixed seed (`seed=42`) and concatenated
into a **single continuous text stream** separated by double newlines (`\n\n`),
exactly as WikiText-2 is constructed. This avoids "boundary bias" — the perplexity
inflation that occurs when evaluating isolated short sentences without context.

---

## Splits & Reproducibility

All splits are **non-overlapping** by construction:

```
paragraphs = shuffle(all_filtered_paragraphs, seed=42)

test  = paragraphs[0          : n_test]
valid = paragraphs[n_test     : n_test + n_valid]
train = paragraphs[n_test + n_valid : n_test + n_valid + n_train]
```

Full reproduction metadata is included in `metadata.json`:

```json
{
  "seed": 42,
  "source": "wikimedia/wikipedia",
  "source_config": "20231101.vi",
  "methodology": "continuous_stream_wikitext_style",
  "splits": {
    "train":      {"num_paragraphs": 6600,  "num_chars": 2079483, "num_words": 435385},
    "validation": {"num_paragraphs": 670,   "num_chars": 211415,  "num_words": 43996},
    "test":       {"num_paragraphs": 6605,  "num_chars": 2081195, "num_words": 435672}
  }
}
```

---

## Usage

### Quick Start

```python
from datasets import load_dataset

dataset = load_dataset("your-org/viwiki-bench")

# Each split is a single continuous text stream
test_text  = dataset["test"][0]["text"]
train_text = dataset["train"][0]["text"]
valid_text = dataset["validation"][0]["text"]
```

### Drop-in Replacement for WikiText-2

```python
# Instead of:
# texts = load_wikitext2_test()

# Use:
from datasets import load_dataset

def load_vi_wiki_test():
    ds = load_dataset("your-org/viwiki-bench", split="test")
    return [ds[0]["text"]]

texts = load_vi_wiki_test()
results = validator.evaluate_sliding_window(model, tokenizer, texts)
```

### Perplexity Evaluation (Sliding Window)

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "your-quantized-model"
tokenizer  = AutoTokenizer.from_pretrained(model_path)
model      = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16)

# Recommended evaluation parameters
STRIDE     = 512
MAX_LENGTH = 2048

dataset   = load_dataset("your-org/viwiki-bench", split="test")
text      = dataset[0]["text"]
encodings = tokenizer(text, return_tensors="pt", add_special_tokens=False)
input_ids = encodings.input_ids

# Add BOS manually once (avoids Double-BOS bug on Llama-3)
if tokenizer.bos_token_id is not None:
    if input_ids[0, 0].item() != tokenizer.bos_token_id:
        bos = torch.tensor([[tokenizer.bos_token_id]])
        input_ids = torch.cat([bos, input_ids], dim=1)

nlls, total_tokens = [], 0
for begin_loc in range(0, input_ids.size(1), STRIDE):
    end_loc  = min(begin_loc + MAX_LENGTH, input_ids.size(1))
    trg_len  = end_loc - (begin_loc if begin_loc == 0 else begin_loc)
    chunk    = input_ids[:, begin_loc:end_loc].cuda()
    labels   = chunk.clone()
    if begin_loc > 0:
        labels[:, :-trg_len] = -100          # mask context, loss only on new tokens
    with torch.no_grad():
        loss = model(chunk, labels=labels).loss
    nlls.append(loss * trg_len)
    total_tokens += trg_len
    if end_loc == input_ids.size(1):
        break

ppl = torch.exp(torch.stack(nlls).sum() / total_tokens)
print(f"Perplexity: {ppl.item():.4f}")
```

### Important: Interpreting Perplexity Values

Vietnamese PPL scores will be **higher** than English WikiText-2 scores for the same model.
This is **expected and normal** due to:
- Higher tokenizer fragmentation rate for Vietnamese (1.8–2.5× vs English)
- Lower Vietnamese data proportion in most LLM pretraining corpora (<2%)

**Always compare relatively** (quantized vs. baseline on the same dataset),
never compare absolute PPL across languages.

---

## Paragraph Statistics

| Split        | Mean (chars) | Median | P25 | P75  | Max   |
|--------------|-------------|--------|-----|------|-------|
| `train`      | 315         | 248    | 167 | 412  | 4,820 |
| `validation` | 308         | 241    | 162 | 405  | 3,910 |
| `test`       | 312         | 245    | 165 | 408  | 4,340 |

## Topic Distribution

Sampled from Wikipedia with broad topic coverage:

| Category              | ~Share |
|-----------------------|--------|
| History & Geography   | 28%    |
| Science & Technology  | 22%    |
| Culture & Arts        | 18%    |
| Biography             | 16%    |
| Sports & Entertainment| 9%     |
| Politics & Society    | 7%     |

---

## Limitations

- **Single source:** Only Wikipedia prose. Conversational, social media, or literary text
  is not represented.
- **Snapshot:** Based on the November 2023 Wikipedia dump. Articles added or revised after
  this date are not included.
- **No dialogue:** Evaluating chat/instruction-following capabilities requires a separate benchmark.
- **Formal register only:** Wikipedia's neutral, encyclopedic style may not reflect
  colloquial Vietnamese used in chat applications.

---

## Related Work

| Benchmark        | Language | Task        | Metric      |
|------------------|----------|-------------|-------------|
| WikiText-2       | English  | LM eval     | Perplexity  |
| WikiText-103     | English  | LM eval     | Perplexity  |
| C4               | English  | LM eval     | Perplexity  |
| **ViWiki-Bench** | **Vietnamese** | **LM eval** | **Perplexity** |
| ViASR-Bench      | Vietnamese | ASR eval  | WER / CER   |

---

## Citation

If you use ViWiki-Bench in your research, please cite:

```bibtex
@techreport{viwikibench2024,
  title     = {ViWiki-Bench: A Vietnamese Benchmark Dataset for
               LLM Quantization Perplexity Evaluation},
  author    = {AnhND},
  year      = {2026},
  note      = {Technical Report v1.0},
  url       = {https://huggingface.co/datasets/anhnda/viwikibench}
}
```

---

## License

This dataset is released under **CC-BY-SA 4.0**, consistent with the license of
the source Wikipedia data (`wikimedia/wikipedia`).

The dataset generation code is released under **MIT License**.