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msoler18
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rossetta

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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 174, in _generate_tables
                  df = pandas_read_json(f)
                       ^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 246, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 177, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
                  pa_table = paj.read_json(
                             ^^^^^^^^^^^^^^
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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Rossetta — Prompt Compilation Dataset

Rossetta is a dataset created to train models that transform raw human prompts into structured, optimized, and enriched instructions. It is designed for research in prompt engineering, instruction rewriting, and natural-language-to-instruction compilation.

Dataset Details

Dataset Description

Rossetta provides paired examples that map a raw, unrefined human prompt (prompt_original) to a structured, context-rich, machine-ready instruction (prompt_compiled). The dataset emphasizes clarity, role definition, explicit requirements, and consistent output formats so models can infer missing context, reduce ambiguity, and produce precise instructions.

  • Curated by: msoler18
  • License: MIT
  • Language(s) (NLP): English (en), Spanish (es)

Intended Use

Direct use:

  • Training models for prompt compilation, instruction rewriting, and LLM instruction tuning.
  • Research on prompt engineering techniques and automated instruction enrichment.

Out-of-scope / Misuse:

  • Not intended for training models that generate harmful, illegal, or unsafe content. Users should filter sensitive content before use.

Dataset Structure

Recommended JSONL schema for each record:

{
  "prompt_original": "string",
  "prompt_compiled": "string",
  "metadata": {
    "domain": "coding | ux | business | general",
    "complexity": "low | mid | high",
    "language": "en | es"
  }
}

Fields

  • prompt_original (string): Raw human prompt.
  • prompt_compiled (string): Optimized and structured instruction suitable for programmatic consumption.
  • metadata (object, optional): Additional tags such as domain, complexity, and language.

Dataset Creation

Curation Rationale

The goal is to build a compact dataset that teaches models to: infer missing context, clarify objectives, define roles, enforce consistent output formats, and improve clarity and precision in instructions.

Source Data

This dataset contains synthetic and human-authored prompts collected and edited to demonstrate common prompt refinement patterns across domains like coding, UX, and general instruction writing.

Example

prompt_original

help me create a pomodoro app with react

prompt_compiled

Act as a senior frontend engineer specialized in React.
Objective: Build a Pomodoro timer using React with hooks.
Requirements:

- 25/5 timer
- Start/Pause controls
  Output format:

1. Vite setup commands
2. Component code
3. Explanation after each block
   Language: English

Usage

Basic example using the datasets library:

from datasets import load_dataset

ds = load_dataset("msoler18/rossetta", split="train")
print(ds[0])

Roadmap

  • Expand domains (AI, business, UX, engineering)
  • Add English-only version
  • Add multi-turn prompt compilation examples
  • Provide train/validation/test splits

Metadata

  • license: mit
  • languages: en, es
  • task_categories: text-generation
  • tags: prompt-engineering, instruction-rewriting, llm-training, nlproc, english, spanish
  • pretty_name: Rossetta — Prompt Compilation Dataset
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