Update README.md

README.md

@@ -15,22 +15,22 @@ tags:
 size_categories:
 - 1K<n<10K
 task_categories:
-- text2text-generation
+- text-generation
 ---
 
-# Tibetan-normalisation-testdata
+# Tibetan Normalisation - Test Data
 
-A collection of **evaluation datasets** for Classical Tibetan text normalisation, containing three distinct test sets designed to assess normalisation systems under different conditions: a manually curated gold-standard set of real diplomatic manuscript text, and two synthetic sets of Standard Classical Tibetan text with OCR-based noise applied. Together these test sets allow evaluation across a spectrum from clean, realistic manuscript normalisation to more controlled, large-scale noise correction scenarios.
+A collection of evaluation datasets for Classical Tibetan text normalisation, containing three distinct test sets designed to assess normalisation systems under different conditions: a manually curated gold-standard set of diplomatic manuscript text, and two synthetic sets of Standard Classical Tibetan text with OCR-based noise applied. Together these test sets allow evaluation across a spectrum from clean, realistic manuscript normalisation to more controlled, large-scale noise correction scenarios.
 
-All test sets are provided in both **non-tokenised** and **tokenised** forms (where available), to support evaluation of both the non-tokenised and tokenised model variants. Each test set consists of paired source and target files: source files contain the noisy or diplomatic input, target files contain the corresponding Standard Classical Tibetan reference.
+All test sets are provided in both non-tokenised and tokenised forms (where available), to support evaluation of both the non-tokenised and tokenised model variants. Each test set consists of paired source and target files: source files contain the noisy or diplomatic input, target files contain the corresponding Standard Classical Tibetan reference.
 
 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.
+Please cite the paper and the [code repository on GitHub](https://github.com/pagantibet/normalisation) when using this dataset.
 
-> **These datasets must not be used for training.** All training material is in [`pagantibet/normalisation-S2S-training`](https://huggingface.co/datasets/pagantibet/normalisation-S2S-training).
+ **These datasets must not be used for training.** All training material can be found in [`pagantibet/normalisation-S2S-training`](https://huggingface.co/datasets/pagantibet/normalisation-S2S-training).
 
 ---
 
@@ -38,7 +38,7 @@ Please cite the paper and the [code repository](https://github.com/pagantibet/no
 
 ### 1. GoldTest — Gold-Standard Diplomatic Tibetan
 
-The primary evaluation set, consisting of real diplomatic Classical Tibetan manuscript text alongside manually produced Standard Classical Tibetan normalisations. This is the most challenging and most meaningful test set: source lines contain genuine scribal variation, abbreviations, non-standard orthography, and diacritic inconsistencies drawn from the PaganTibet corpus, not synthetically generated noise.
+The primary evaluation set, consisting of diplomatic Classical Tibetan manuscript text alongside manually produced Standard Classical Tibetan normalisations. This is the most challenging and most meaningful test set: source lines contain genuine scribal variation, abbreviations, non-standard orthography, and diacritic inconsistencies drawn from the PaganTibet corpus, not synthetically generated noise.
 
 This set is held out from the training data and does not overlap with the gold-standard lines used in [`pagantibet/normalisation-S2S-training`](https://huggingface.co/datasets/pagantibet/normalisation-S2S-training).
 
@@ -49,7 +49,7 @@ This set is held out from the training data and does not overlap with the gold-s
 | `GoldTest_source-tok.txt` | Diplomatic source text (tokenised) |
 | `GoldTest_target-tok.txt` | Standard Classical Tibetan reference (tokenised) |
 
-Full evaluation results with bootstrapped confidence intervals for this test set are available in the repository:
+Full evaluation results with bootstrapped confidence intervals for this test set are available in the [PaganTibet GitHub repository](https://github.com/pagantibet/normalisation/tree/main):
 - [Evaluations/Gold-nontokenised-CI](https://github.com/pagantibet/normalisation/tree/main/Evaluations/Gold-nontokenised-CI)
 - [Evaluations/Gold-tokenised-CI](https://github.com/pagantibet/normalisation/tree/main/Evaluations/Gold-tokenised-CI)
 
@@ -59,7 +59,7 @@ Full evaluation results with bootstrapped confidence intervals for this test set
 
 A synthetic test set derived from the Standard Classical Tibetan ACTib corpus ([Meelen & Roux 2020](https://zenodo.org/records/3951503)), with OCR-realistic noise applied to the source side using the [nlpaug](https://github.com/makcedward/nlpaug) library. Source lines contain OCR-style character errors and distortions; target lines are the clean, original ACTib text. This set evaluates a model's ability to correct the specific character confusions and distortions that arise when digitising historical Tibetan documents.
 
-Because both source and target sides are derived from the ACTib (standard text with synthetic noise), this test set is more controlled than the GoldTest and allows for cleaner measurement of OCR correction capacity in isolation from other normalisation challenges.
+Because both source and target files are derived from the ACTib corpus, this test set is more controlled than the GoldTest above and allows for cleaner measurement of OCR correction capacity in isolation from other normalisation challenges.
 
 | File | Description |
 |---|---|
@@ -81,12 +81,12 @@ A larger-scale version of the ACTib OCR noise test set, containing 5,000 lines.
 
 ---
 
-## Dataset Statistics
+## Dataset Overview
 
 | Test Set | Lines | Source Type | Tokenised? |
 |---|---|---|---|
-| GoldTest | ~700 | Real diplomatic manuscript text | ✓ both |
-| ACTibOCRnoiseTest | ~700 | Synthetic OCR noise on ACTib | ✓ both |
+| GoldTest | 217 | Diplomatic manuscript text | ✓ both |
+| ACTibOCRnoiseTest | 216 | Synthetic OCR noise on ACTib | ✓ both |
 | 5000ACTibOCRnoiseTest | 5,000 | Synthetic OCR noise on ACTib | ✗ non-tok only |
 
 *Note: the Hugging Face Dataset Viewer displays the dataset as a single `train` split — this is a technical default. All files are evaluation data and must not be used for training.*
@@ -233,22 +233,6 @@ Full evaluation results for all inference modes across both tokenised and non-to
 
 ---
 
-## 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
 
 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.
@@ -257,4 +241,4 @@ This dataset is released under [CC BY-NC-SA 4.0](https://creativecommons.org/lic
 
 ## 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.
+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.