---
license: cc-by-nc-sa-4.0
language:
- bo
tags:
- classical-tibetan
- historical-text
- normalisation
- kenlm
- n-gram
- language-model
- corpus
- low-resource
- digital-humanities
size_categories:
- 10M<n<100M
task_categories:
- text-generation
---

# Tibetan Normalisation - KenLM ACTib Training Data

A large-scale corpus of Standard Classical Tibetan text prepared specifically for training character-level KenLM n-gram language models for use in the PaganTibet normalisation pipeline. The dataset contains approximately 17.7 million lines of cleaned, line-split ACTib text in two versions: non-tokenised and tokenised (via a [customised version](https://github.com/pagantibet/normalisation/blob/main/Data_Preparation/botokenise_ReadMe.md) of the [Botok Tibetan tokeniser](https://www.github.com/OpenPecha/botok)). These two files are the direct training corpora for [`pagantibet/5gram-kenLM_char`](https://huggingface.co/pagantibet/5gram-kenLM_char) and [`pagantibet/5gram-kenLM_char-tok`](https://huggingface.co/pagantibet/5gram-kenLM_char-tok) respectively.

The KenLM models trained on this corpus serve as re-rankers for beam search candidates generated by the PaganTibet sequence-to-sequence normalisation models ([tokenised S2S model](https://huggingface.co/pagantibet/normalisationS2S-tokenised) and [non-tokenised S2S model](https://huggingface.co/pagantibet/normalisationS2S-nontokenised)), selecting the hypothesis most consistent with fluent Standard Classical Tibetan.

This dataset is part of the [PaganTibet](https://www.pagantibet.com/) project and accompanies the paper:

Meelen, M. & Griffiths, R.M. (2026) 'Historical Tibetan Normalisation: rule-based vs neural & n-gram LM methods for extremely low-resource languages' in *Proceedings of the AI4CHIEF conference*, Springer.

Please cite the paper and the [code repository](https://github.com/pagantibet/normalisation) when using this dataset.

---

## Dataset Overview

The dataset contains two plain text files, each representing the same underlying ACTib corpus (Annotated Corpus of Tibetan; >180 million words of Standard Classical Tibetan), available from [Zenodo (Meelen & Roux 2020)](https://zenodo.org/records/3951503). Each file 
is prepared in a different form:

| File | Size | Description |
|---|---|---|
| `unsegACTib_lines_kenlm_20260103_220309.txt` | 2.16 GB | Non-tokenised ACTib lines — used to train [`pagantibet/5gram-kenLM_char`](https://huggingface.co/pagantibet/5gram-kenLM_char) |
| `unsegACTib-tok_lines_kenlm_20260103_220119.txt` | 2.32 GB | Tokenised ACTib lines — used to train [`pagantibet/5gram-kenLM_char-tok`](https://huggingface.co/pagantibet/5gram-kenLM_char-tok) |


*Note: the Hugging Face Dataset Viewer displays the dataset as a single train split — this is a technical default.*

Both files are UTF-8 encoded plain text with one line per sequence. In the tokenised file, tokens are separated by spaces as produced by the customised Botok tokeniser (see [Data_Preparation](https://github.com/pagantibet/normalisation/tree/main/Data_Preparation)).

The ACTib does not contain natural linebreaks and includes non-Tibetan material (e.g. page numbers, section markers) that is unsuitable for language model training. Before use, the corpus was cleaned and split into artificial manuscript-length lines of varying length using the `createTiblines.py` script (see [Data_Preparation](https://github.com/pagantibet/normalisation/tree/main/Data_Preparation)). This produces lines of realistic, varied lengths that match the sequence units encountered during normalisation at inference time.

---

## Usage

The training notebook [`KenLM_trainforNormalisation.ipynb`](https://github.com/pagantibet/normalisation/blob/main/Training/KenLM_trainforNormalisation.ipynb) walks through the full training process and can be run on Google Colab. The key steps are as follows:

### Step 1 — Install KenLM

```bash
sudo apt-get install -y build-essential cmake libboost-all-dev \
    libboost-program-options-dev libboost-system-dev \
    libboost-thread-dev libboost-test-dev libeigen3-dev zlib1g-dev
git clone https://github.com/kpu/kenlm.git
cd kenlm && mkdir build && cd build
cmake .. && make -j 4
```

### Step 2 — Convert to Space-Separated Characters

KenLM requires space-separated tokens. For character-level modelling, each character must be separated by a space. Apply this to whichever file you are using:

```python
# For the non-tokenised file
with open("unsegACTib_lines_kenlm_20260103_220309.txt") as f_in, \
     open("actib_char.txt", "w") as f_out:
    for line in f_in:
        f_out.write(" ".join(line.strip()) + "\n")

# For the tokenised file
with open("unsegACTib-tok_lines_kenlm_20260103_220119.txt") as f_in, \
     open("actib_char_tok.txt", "w") as f_out:
    for line in f_in:
        f_out.write(" ".join(line.strip()) + "\n")
```

### Step 3 — Train the 5-gram KenLM

Using the parameters from Meelen & Griffiths (2026):

```bash
# Non-tokenised model
./kenlm/build/bin/lmplz \
    -o 5 \
    --prune 0 1 1 2 2 \
    --discount_fallback \
    < actib_char.txt \
    > model_5gram_char.arpa

# Tokenised model
./kenlm/build/bin/lmplz \
    -o 5 \
    --prune 0 1 1 2 2 \
    --discount_fallback \
    < actib_char_tok.txt \
    > model_5gram_char_tok.arpa
```

**Parameter notes:**

- `-o 5` — 5-gram order
- `--prune 0 1 1 2 2` — pruning thresholds per n-gram order (unigrams kept, higher-order singletons pruned)
- `--discount_fallback` — modified Kneser-Ney discounting with fallback

Training completes in under 5 minutes on Google Colab. Full parameter settings are reported in the Appendix of Meelen & Griffiths (2026).

### Step 4 — (Optional) Convert to Binary

For faster loading with the compiled KenLM Python backend:

```bash
./kenlm/build/bin/build_binary model_5gram_char.arpa model_5gram_char.bin
```

*Note: The pure Python ARPA backend used in the inference scripts requires `.arpa` format. The `.bin` format is only compatible with the compiled `kenlm` Python package.*

---

## Relationship to Other Resources

This corpus sits at the base of the KenLM training pipeline. The diagram below shows how it connects to the rest of the PaganTibet normalisation stack:

```
ACTib corpus (Zenodo)
       │
       ▼
createTiblines.py  ──→  unsegACTib_lines_kenlm_*.txt       (this dataset, non-tokenised file)
       │
       ▼
botokenise_src-tgt.py  ──→  unsegACTib-tok_lines_kenlm_*.txt  (this dataset, tokenised file)
       │
       ├──→  lmplz (KenLM)  ──→  5gram-kenLM_char        (non-tokenised KenLM model)
       └──→  lmplz (KenLM)  ──→  5gram-kenLM_char-tok    (tokenised KenLM model)
                                        │
                                        ▼
                              Used as beam search re-ranker
                              in normalisationS2S inference
```

---

## Related Models and Resources

| Resource | Link |
|---|---|
| Non-tokenised KenLM model (trained on this data) | [`pagantibet/5gram-kenLM_char`](https://huggingface.co/pagantibet/5gram-kenLM_char) |
| Tokenised KenLM model (trained on this data) | [`pagantibet/5gram-kenLM_char-tok`](https://huggingface.co/pagantibet/5gram-kenLM_char-tok) |
| Non-tokenised Seq2Seq model | [`pagantibet/normalisationS2S-nontokenised`](https://huggingface.co/pagantibet/normalisationS2S-nontokenised) |
| Tokenised Seq2Seq model | [`pagantibet/normalisationS2S-tokenised`](https://huggingface.co/pagantibet/normalisationS2S-tokenised) |
| S2S training dataset | [`pagantibet/normalisation-S2S-training`](https://huggingface.co/datasets/pagantibet/normalisation-S2S-training) |
| KenLM training notebook | [`KenLM_trainforNormalisation.ipynb`](https://github.com/pagantibet/normalisation/blob/main/Training/KenLM_trainforNormalisation.ipynb) |
| Data preparation scripts & ReadMes | [github.com/pagantibet/normalisation/Data_Preparation](https://github.com/pagantibet/normalisation/tree/main/Data_Preparation) |
| Inference scripts & ReadMe | [github.com/pagantibet/normalisation/Inference](https://github.com/pagantibet/normalisation/tree/main/Inference) |
| ACTib corpus | [Zenodo (Meelen & Roux 2020)](https://zenodo.org/records/3951503) |
| PaganTibet project | [pagantibet.com](https://www.pagantibet.com/) |

---

## License

This dataset is released under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). It may be used freely for non-commercial research and educational purposes, with attribution and under the same licence terms.

---

## Funding

This work was partially funded by the European Union (ERC, Pagan Tibet, grant no. 101097364). Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Research Council Executive Agency.