Text Classification
PEFT
Safetensors
decision-model
calibrated-probabilities
lora
qwen3
typesafe-compatible
Instructions to use Suparva/leo-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Suparva/leo-1 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download leo/render.py from Suparva/leo-1: direct link, hf CLI and curl.
- Browser
- Download file 4.46 kB
-
https://huggingface.co/Suparva/leo-1/resolve/main/leo/render.py
- Command line
-
hf download hf://Suparva/leo-1/leo/render.py
-
curl -L -o render.py https://huggingface.co/Suparva/leo-1/resolve/main/leo/render.py
4.46 kB
| """Turn JSON state, instructions and option descriptions into text the model reads. | |
| Objects and arrays are flattened into ``path: value`` lines using the same dot-and-index paths | |
| TypeSafe questions use to point at parts of the state (``ticket.messages[0].text``), so a question | |
| that names `ticket.messages[0].text` finds that exact string next to its value. | |
| """ | |
| from __future__ import annotations | |
| import json | |
| from typing import Any | |
| def _leaf(value: Any) -> str: | |
| if isinstance(value, str): | |
| return value | |
| if value is None: | |
| return "null" | |
| if isinstance(value, bool): | |
| return "true" if value else "false" | |
| return json.dumps(value, ensure_ascii=False) | |
| def _walk(value: Any, path: str, out: list[str]) -> None: | |
| if isinstance(value, dict): | |
| if not value: | |
| out.append(f"{path}: {{}}" if path else "{}") | |
| for k, v in value.items(): | |
| _walk(v, f"{path}.{k}" if path else str(k), out) | |
| elif isinstance(value, list): | |
| if not value: | |
| out.append(f"{path}: []" if path else "[]") | |
| for i, v in enumerate(value): | |
| _walk(v, f"{path}[{i}]", out) | |
| else: | |
| out.append(f"{path}: {_leaf(value)}" if path else _leaf(value)) | |
| def render(value: Any) -> str: | |
| """Render any EntryType. Strings pass through unchanged; JSON is flattened to path lines.""" | |
| if value is None: | |
| return "" | |
| if isinstance(value, str): | |
| return value | |
| lines: list[str] = [] | |
| _walk(value, "", lines) | |
| return "\n".join(lines) | |
| # ------------------------------------------------------------------------------ yes/no conditions | |
| _QUESTION_WORDS = {"is", "are", "was", "were", "am", "do", "does", "did", "can", "could", "will", "would", "should", | |
| "shall", "has", "have", "had", "may", "might", "must", "which", "what", "who", "whom", "whose", | |
| "when", "where", "why", "how"} | |
| _SUBJECTS = {"the", "this", "that", "these", "those", "it", "its", "they", "he", "she", "i", "we", "you", "my", "our", | |
| "their", "his", "her", "a", "an", "there", "someone", "somebody", "everyone", "nobody", "user", "customer"} | |
| CONDITION_TEMPLATES = { | |
| "input": "Is the input {c}?", | |
| "describe": "Does this describe the input: {c}?", | |
| "true_of": "Is this true of the input: {c}?", | |
| } | |
| def condition_form(text: str) -> str: | |
| """Classify a yes/no instruction: "question", "statement" or "fragment" ("angry", "asking for a refund", | |
| "Contains personal data"). Only fragments are rewritten by ``canonical_condition``.""" | |
| s = text.strip() | |
| if not s: | |
| return "fragment" | |
| words = s.split() | |
| first = words[0].lower().strip("\"'([") | |
| if s.endswith("?") or first in _QUESTION_WORDS: | |
| return "question" | |
| if len(words) >= 3 and (first in _SUBJECTS or s.endswith(".")) and len(words) <= 60: | |
| return "statement" | |
| # Long rule-like fragments ("Contains hate speech, slurs, ...") read fine as sent; rewriting them did not help | |
| # on scripts/condition_probe.py, so only short conditions count as fragments. | |
| return "fragment" if len(words) <= 8 else "statement" | |
| def canonical_condition(instructions: Any, template: str = "input") -> Any: | |
| """Rewrite a bare yes/no condition into a full question, keeping the caller's words verbatim. | |
| A question without its "?" gets one; statements and structured instructions pass through unchanged. | |
| """ | |
| if not isinstance(instructions, str): | |
| return instructions | |
| s = instructions.strip() | |
| form = condition_form(s) | |
| if form == "question": | |
| return s if s.endswith("?") else s.rstrip(".") + "?" | |
| if form == "statement": | |
| return instructions | |
| c = s.rstrip(".?!").strip() | |
| if c[:1].isupper() and not c[:2].isupper(): | |
| c = c[0].lower() + c[1:] | |
| return CONDITION_TEMPLATES[template].format(c=c) | |
| def option_text(qtype: str, key: str, description: Any) -> str: | |
| """What the model reads inside one option block. | |
| Choice shows ``key: description`` (or the bare key). Score levels show only their description: | |
| TypeSafe states that levels are judged without their number. Noul shows yes/no plus the optional | |
| criteria text. | |
| """ | |
| desc = render(description).strip() | |
| if qtype == "score": | |
| return desc | |
| if qtype == "noul": | |
| word = "yes" if key == "true" else "no" | |
| return f"{word}: {desc}" if desc else word | |
| return f"{key}: {desc}" if desc else key | |