Update README.md

README.md

@@ -14,52 +14,51 @@ tags:
 size_categories:
 - 1M<n<10M
 task_categories:
-- text2text-generation
+- text-generation
 ---
 
-# normalisation-S2S-training
+# Tibetan Normalisation - S2S Training Data
 
-A large-scale parallel training dataset for **Classical Tibetan text normalisation**, containing approximately 2 million line pairs mapping diplomatic (non-standard, abbreviated) Tibetan manuscript text to Standard Classical Tibetan. This dataset was used to train the sequence-to-sequence normalisation models released as part of the [PaganTibet](https://www.pagantibet.com/) project.
+A large-scale parallel training dataset for Tibetan text normalisation, containing approximately 2 million line pairs mapping diplomatic (non-standard, abbreviated) Tibetan manuscript text to Standard Classical Tibetan. This dataset was used to train the 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)) released as part of the [PaganTibet](https://www.pagantibet.com/) project.
 
-The dataset combines a manually curated gold-standard corpus with extensively augmented data generated using four complementary strategies designed to simulate the scribal variation, abbreviation, and orthographic inconsistency characteristic of historical Tibetan manuscripts.
+The dataset combines a manually curated gold-standard corpus with extensively augmented data generated using four complementary strategies designed to simulate the scribal variation, abbreviation, and orthographic inconsistency characteristic of historical Tibetan manuscripts. For more on Data Augmentation, see our [GitHub repository](https://github.com/pagantibet/normalisation/tree/main/Data_Augmentation). 
 
 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.
+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 Description
+## Dataset Overview
 
 Classical Tibetan manuscripts present significant challenges for automatic normalisation: texts are riddled with abbreviations, non-standard spellings, diacritic variation, and scribal idiosyncrasies, while parallel training data — pairs of diplomatic input alongside normalised output — is extremely scarce. This dataset addresses that scarcity through systematic data augmentation, expanding a small gold-standard collection into a training corpus of over 2 million examples.
 
-Each row in the dataset is a single line of Tibetan text. The dataset is structured as a **source–target parallel corpus**: source lines contain diplomatic or non-standard Tibetan, and target lines contain the corresponding Standard Classical Tibetan normalisation. Because the augmentation pipeline generates source-side variation from known target-side text, source and target lines are paired and must be used together during training.
+Each row in the dataset is a single line of Tibetan text (UTF-8 encoded). The dataset is structured as a **source–target parallel corpus**: source lines contain diplomatic or non-standard Tibetan, and target lines contain the corresponding Standard Classical Tibetan normalisation. Because the augmentation pipeline generates source-side variation from known target-side text, source and target lines are paired and must be used together during training.
 
-The dataset is provided in its **non-tokenised** form. A tokenised version was also used in experiments (see Meelen & Griffiths 2026) but is not separately released, as tokenisation can be applied at training time using the scripts provided in the [Data_Preparation](https://github.com/pagantibet/normalisation/tree/main/Data_Preparation) directory.
+The dataset is provided in its non-tokenised form. A tokenised version was also used in experiments (see Meelen & Griffiths 2026) but is not separately released, as tokenisation can be applied at training time using the scripts provided in the [Data_Preparation](https://github.com/pagantibet/normalisation/tree/main/Data_Preparation) directory.
 
-### Dataset Statistics
-
-| Split | Rows |
-|---|---|
-| train | ~2,028,816 |
-
----
 
 ## Data Sources
 
 The dataset draws on three underlying sources:
 
 **1. Gold-standard parallel data (PaganTibet corpus)**
-A collection of 7,421 manually normalised line pairs from the PaganTibet corpus, representing real diplomatic Tibetan manuscript text alongside its Standard Classical Tibetan normalisation. This is the only portion of the dataset containing genuine diplomatic source text; all other source-side material is synthetically generated from standard text.
+
+A collection of 7,421 manually normalised line pairs from the PaganTibet corpus, representing diplomatic Tibetan manuscript text alongside its Standard Classical Tibetan normalisation. This is the only portion of the dataset containing genuine diplomatic source text; all other source-side material is synthetically generated from standard text.
 
 **2. Standard Classical Tibetan — ACTib corpus**
-The ACTib (>180 million words; [Meelen & Roux 2020](https://zenodo.org/records/3951503)) was used as the target-side basis for augmented examples. Lines were cleaned to remove non-Tibetan content (e.g. page numbers) and split into manuscript-length sequences using the `createTiblines.py` script, producing an 8-million-line pool from which training examples were drawn.
+
+The ACTib (>180 million words; [Meelen & Roux 2020](https://zenodo.org/records/3951503)) was used as the target-side basis for augmented examples. Lines were cleaned to remove non-Tibetan content (e.g. page numbers) and split into manuscript-length sequences using the [`createTiblines.py`](https://github.com/pagantibet/normalisation/tree/main/Data_Preparation) script, producing an 8-million-line pool from which training examples were drawn.
 
 **3. Tibetan abbreviation dictionary**
+
 A [custom-built abbreviation dictionary](https://huggingface.co/datasets/pagantibet/Tibetan-abbreviation-dictionary) of approximately 10,000 diplomatic abbreviation–expansion pairs, used in the dictionary-based augmentation strategy described below.
 
+
+*Note: the Hugging Face Dataset Viewer displays the dataset as a single train split — this is a technical default.*
+
 ---
 
 ## Data Augmentation
@@ -76,7 +75,7 @@ python3 Tibrandomnoiseaugmentation.py my_corpus.txt
 
 ### 2. OCR-Based Noise Simulation
 
-To model errors introduced during optical character recognition of Tibetan manuscripts, the [nlpaug](https://github.com/makcedward/nlpaug) library was used to generate OCR-realistic noise patterns. This augmentation strategy targets the specific character confusions and distortions that arise when digitising historical Tibetan documents.
+To model errors introduced during optical character recognition (OCR) of Tibetan manuscripts, the [nlpaug](https://github.com/makcedward/nlpaug) library was used to generate OCR-realistic noise patterns. This augmentation strategy targets the specific character confusions and distortions that arise when digitising historical Tibetan documents.
 
 ```bash
 python3 nlpaugtib.py --input <input_file.txt> --type nonsegmented [--aug_prob FLOAT]
@@ -87,12 +86,12 @@ python3 nlpaugtib.py --input <input_file.txt> --type nonsegmented [--aug_prob FL
 A targeted rule-based augmentation script applies character replacements reflecting common scribal conventions and variations found in historical Tibetan manuscripts. Transformations are applied stochastically at the character and syllable levels, with adjustable ratios to control the density of introduced variation.
 
 ```bash
-python3 tibrule_augmentation.py input.txt --char-ratio 0.1 --syllable-ratio 0.05
+python3 tibrule-augmentation.py input.txt --char-ratio 0.1 --syllable-ratio 0.05
 ```
 
 ### 4. Dictionary-Based Augmentation
 
-Entries from the Tibetan abbreviation dictionary are injected into random lines, exposing the model to a wide range of abbreviation–expansion pairs during training. This augmentation is particularly important for teaching the model to resolve the abbreviated forms that are among the most frequent and systematic deviations from standard orthography in diplomatic Tibetan texts.
+Entries from the [Tibetan abbreviation dictionary](https://huggingface.co/datasets/pagantibet/Tibetan-abbreviation-dictionary) are injected into random lines, exposing the model to a wide range of abbreviation–expansion pairs during training. This augmentation is particularly important for teaching the model to resolve the abbreviated forms that are among the most frequent and systematic deviations from standard orthography in diplomatic Tibetan texts.
 
 ```bash
 python3 dictionary-augmentation.py input.txt abbreviation-dictionary.txt
@@ -145,21 +144,6 @@ The dataset is not suitable for evaluating normalisation performance, as the aug
 
 ---
 
-## Citation
-
-If you use this dataset, please cite the accompanying paper and the code repository:
-
-```bibtex
-@inproceedings{meelen-griffiths-2026-tibetan-normalisation,
-  author    = {Meelen, Marieke and Griffiths, R.M.},
-  title     = {Historical Tibetan Normalisation: rule-based vs neural \& n-gram LM methods for extremely low-resource languages},
-  booktitle = {Proceedings of the AI4CHIEF conference},
-  publisher = {Springer},
-  year      = {2026}
-}
-```
-
----
 
 ## License