Update README.md

README.md

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 ---
+language:
+- si
 license: mit
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+pretty_name: UltraChat-Sinhala
+source_datasets:
+- HuggingFaceH4/ultrachat_200k
+tags:
+- sinhala
+- machine-translation
+- nllb
+- instruction-tuning
+- sft
+configs:
+- config_name: default
+  data_files:
+  - split: train_sft
+    path: data/train_sft.parquet
+  - split: test_sft
+    path: data/test_sft.parquet
+  - split: train_gen
+    path: data/train_gen.parquet
+  - split: test_gen
+    path: data/test_gen.parquet
+dataset_info:
+  features:
+  - name: prompt
+    dtype: string
+  - name: prompt_id
+    dtype: string
+  - name: messages
+    list:
+    - name: content
+      dtype: string
+    - name: role
+      dtype: string
+  splits:
+  - name: train_sft
+    num_bytes: 3220638735
+    num_examples: 207831
+  - name: test_sft
+    num_bytes: 356835963
+    num_examples: 23106
+  - name: train_gen
+    num_bytes: 3112868195
+    num_examples: 255974
+  - name: test_gen
+    num_bytes: 342615462
+    num_examples: 28300
+  download_size: 1568756226
+  dataset_size: 7032958355
 ---
+
+# Dataset Card for UltraChat-Sinhala
+
+## Dataset Description
+
+UltraChat-Sinhala is a **Sinhala (සිංහල) machine translation of
+[HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)**,
+built to supervised-fine-tune Sinhala large language models. It preserves the
+original dataset's structure, splits, and `prompt_id`s, so it is a drop-in
+Sinhala counterpart to the English source.
+
+The English dialogues were translated with
+[NLLB-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B)
+(`eng_Latn → sin_Sinh`) and then put through a Sinhala-specific cleaning
+pipeline (conjunct/ZWJ repair, masking-leak repair, filtering). The dataset
+contains **515,211 dialogues / ≈528M Sinhala tokens** (SinLLaMA tokenizer).
+
+## Dataset Creation
+
+1. **Translation.** Each dialogue turn was translated en→si with NLLB-200-3.3B
+   (greedy decoding). To respect the model's 512-token limit, turns were
+   sentence-segmented with the line layout preserved on reassembly. Spans that
+   must not be translated — fenced/inline code, URLs, e-mail addresses, HTML
+   tags, LaTeX/maths and markdown links — were masked before translation and
+   restored afterwards, so they pass through verbatim.
+
+2. **Sinhala conjunct (ZWJ) repair.** NLLB's SentencePiece normaliser strips the
+   Zero-Width Joiner (`U+200D`) from Sinhala conjunct clusters, emitting a space
+   instead (e.g. `ප් ර` for `ප්‍ර`). A lexicon-gated restorer — built from a
+   Sinhala corpus plus the tokenizer's vocabulary — re-inserts the joiner only
+   where attested. After repair, **~99.6–99.9% of dialogues carry conjunct
+   joiners**.
+
+3. **Masking-leak repair.** The placeholder used to mask the protected spans was
+   itself corrupted by SentencePiece (its rare brackets were stripped),
+   displacing each masked span to the end of its message with a stray digit left
+   behind. These were re-inserted in their correct positions by re-aligning each
+   message to its English source (≈83% reconstructed exactly in place; the rest
+   re-appended without any content loss).
+
+4. **Filtering.** Dialogues containing empty/whitespace-only turns were dropped
+   (a mid-conversation turn cannot be removed without breaking the
+   user/assistant alternation); `prompt_id`s were de-duplicated; and one
+   `prompt_id` shared between the SFT and GEN sets was removed for global
+   id-uniqueness.
+
+5. **Train/test split.** Membership is taken **verbatim from the original
+   ultrachat_200k split** by `prompt_id`, so the Sinhala split is identical to
+   the English source (≈10% test). It is **leak-checked**: no `prompt_id` and no
+   identical dialogue appears in both train and test, in either SFT or GEN.
+
+## Dataset Structure
+
+Like the source, the dataset has four splits, suitable for:
+
+* Supervised fine-tuning (`sft`).
+* Generation ranking (`gen`) via techniques like rejection sampling or PPO.
+
+| split | examples | tokens |
+|:----------|---------:|------------------:|
+| train_sft | 207,831  | 248,133,641 |
+| test_sft  | 23,106   | 27,467,232  |
+| train_gen | 255,974  | 227,432,690 |
+| test_gen  | 28,300   | 25,035,938  |
+| **total** | **515,211** | **528,069,501** |
+
+Tokens are raw message-content tokens (the SinLLaMA tokenizer, vocab 139,336, has
+no chat template; a real SFT run adds a small per-turn special-token overhead).
+
+The dataset is stored in parquet (zstd), schema-identical to the source:
+
+```
+{
+    "prompt": "ආහාර පිසීමේ ව්‍යාපාරයක් සඳහා වට්ටෝරු පොතක් නිර්මාණය කරන්න. ...",
+    "prompt_id": "7d86ffeefdea030c92138e0b964c304508bfebed4b23261c8a741630823e6f96",
+    "messages": [
+        {
+            "role": "user",
+            "content": "ආහාර පිසීමේ ව්‍යාපාරයක් සඳහා වට්ටෝරු පොතක් න��ර්මාණය කරන්න. ..."
+        },
+        {
+            "role": "assistant",
+            "content": "නම: රසවත් ආහාර පිසීමේ රහස්: රසවත් හා ලස්සන ආහාර පිසීමේ වට්ටෝරු පොත ..."
+        },
+        {
+            "role": "user",
+            "content": "ඔයා මේ වෙනකම් හදපු වට්ටෝරු පොත නම් නියමයි. ඔයාට පුලුවන්ද තව විස්තර එකතු කරන්න ..."
+        },
+        {
+            "role": "assistant",
+            "content": "..."
+        }
+    ]
+}
+```
+
+## Quality and Limitations
+
+Full-scan checks on the released data: **100% valid JSON**, correct
+user/assistant alternation, no empty turns, no duplicate `prompt_id`s.
+~**0.017%** of turns remain in Latin script (overwhelmingly fenced code, which is
+preserved by design), and ~**0.5%** of longer turns show NLLB repetition
+artifacts.
+
+This is **machine translation without human post-editing**. Expect:
+
+- translationese and occasional mistranslation, especially on idioms, named
+  entities, and technical content;
+- code, identifiers, URLs and maths intentionally left in their original form;
+- a minority of turns with NLLB repetition loops (~0.5%);
+- residual orthographic edge cases not covered by the conjunct lexicon.
+
+It is intended for instruction-tuning and research, not as a gold-standard
+reference translation.
+
+## Licensing
+
+Released under the **MIT License**, following the source dataset
+`HuggingFaceH4/ultrachat_200k`. The Sinhala text was produced by machine
+translation with NLLB-200.