Initial model/dataset card

README.md

@@ -0,0 +1,55 @@
+---
+license: apache-2.0
+language:
+- en
+- code
+task_categories:
+- translation
+- text-generation
+tags:
+- code
+- code-translation
+- nebula
+size_categories:
+- 100K<n<1M
+---
+
+# nebula-8lang-203k
+
+Training pairs for fine-tuning code translation models on [Nebula](https://github.com/colinc86/nebula), a universal code intermediate language. Each example is a (Nebula → target language) pair across 8 languages: Python, JavaScript, TypeScript, Go, Swift, Kotlin, Rust, C.
+
+## Pipeline
+
+Source code is harvested from [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata) (and [The Stack](https://huggingface.co/datasets/bigcode/the-stack) for Swift), parsed into individual functions, then converted to Nebula via the Nebula compiler's `from-{lang}` ingesters. Pairs that fail validation (trivial, error markers, length filters) are dropped.
+
+## Format
+
+Each line in `train.jsonl` / `val.jsonl` is a chat-formatted SFT example:
+
+```json
+{"messages": [
+  {"role": "system", "content": "You are a code translator. Given code in Nebula (a universal intermediate language), produce the equivalent idiomatic <Language> code. Output only the <Language> code, no explanations."},
+  {"role": "user", "content": "<nebula source>"},
+  {"role": "assistant", "content": "<target language source>"}
+]}
+```
+
+## Sizes
+
+| Split | Examples |
+|---|---|
+| Train | 203,336 |
+| Val | ~22,600 |
+| Per language | ~30,000 (8 langs) |
+
+~30% of examples are multi-function programs (vs the single-function pairs in `nebula-8lang-68k`).
+
+Split: 90% train / 10% val.
+
+## Models trained on this dataset
+
+- [`electrocampbell/nebula-8lang-14b`](https://huggingface.co/electrocampbell/nebula-8lang-14b)
+
+## License
+
+Apache 2.0. Source data is from StarCoderData / The Stack, used under their respective licenses (permissively-licensed code only).