unbias-plus 0.1.2

Bias detection and debiasing using a single LLM

unbias-plus

Bias detection and debiasing using a single LLM. Analyze text for biased language, get structured results (binary label, severity, biased segments with replacements and reasoning), and a neutral rewrite—all via one fine-tuned causal language model.

Overview

Single-model pipeline: one HuggingFace causal LM does both detection and debiasing. Input text → prompt → LLM → JSON → validated BiasResult (and optional CLI/API formatting). Entry points: CLI (unbias-plus), REST API (FastAPI + demo UI), or Python (UnBiasPlus).

Project structure:

unbias-plus/
├── src/unbias_plus/
│   ├── __init__.py      # UnBiasPlus, BiasResult, BiasedSegment, serve
│   ├── cli.py           # unbias-plus entry point (--text, --file, --serve)
│   ├── api.py           # FastAPI app, /health, /analyze, serve()
│   ├── pipeline.py      # UnBiasPlus: prompt → model → parse → result
│   ├── model.py         # UnBiasModel: load LM, generate(), 4-bit optional
│   ├── prompt.py        # build_prompt(text), system prompt
│   ├── parser.py        # parse_llm_output() → BiasResult
│   ├── schema.py        # BiasResult, BiasedSegment (Pydantic)
│   ├── formatter.py     # format_cli, format_dict, format_json
│   └── demo/            # bundled web UI (served at / when using --serve)
│       ├── static/      # script.js, style.css
│       └── templates/   # index.html
├── tests/
│   ├── conftest.py      # fixtures (sample_result, sample_json, …)
│   └── unbias_plus/     # test_api, test_pipeline, test_parser, …
├── pyproject.toml
└── README.md

Features

• Single-model pipeline: One HuggingFace causal LM handles both detection and debiasing (no separate classifier + generator).
• Structured output: Pydantic-validated results with binary_label (biased/unbiased), overall severity (1–5), biased_segments (original phrase, replacement, severity, bias type, reasoning, character offsets), and full unbiased_text.
• Demo UI: --serve launches a FastAPI server that also serves a visual web interface at http://localhost:8000 — no separate frontend server needed.
• CLI: Analyze from command line with --text, --file, or start the API + UI with --serve. Optional 4-bit quantization and JSON output.
• REST API: FastAPI server with /health and /analyze (POST JSON {"text": "..."}). Model loaded at startup via lifespan.
• Python API: Use UnBiasPlus in code; call analyze(), analyze_to_cli(), analyze_to_dict(), or analyze_to_json().

Requirements

• Python ≥3.10, <3.12
• CUDA 12.4 recommended (PyTorch + CUDA deps in pyproject.toml). CPU is supported with device="cpu".

Installation

The project uses uv for dependency management. Install uv, then from the project root:

uv sync
source .venv/bin/activate   # or .venv\Scripts\activate on Windows

For development (tests, linting, type checking):

uv sync --dev
source .venv/bin/activate

Optional: flash-attn (GPU only) For training or faster inference with flash attention, install the train extra (requires CUDA/nvcc to build):

uv sync --extra train
# On HPC: load CUDA first, e.g. module load cuda/12.4.0

Default uv sync does not install flash-attn, so CI and CPU-only setups work without it.

Usage

Command line

# Analyze a string
unbias-plus --text "Women are too emotional to lead."

# Analyze a file, output JSON
unbias-plus --file article.txt --json

# Start API server + demo UI (default model, port 8000)
unbias-plus --serve
unbias-plus --serve --model path/to/model --port 8000
unbias-plus --serve --load-in-4bit   # reduce VRAM

Options: --model, --load-in-4bit, --max-new-tokens, --host, --port, --json.

Test the model (CLI)

After uv sync (and optionally uv sync --extra train on a GPU machine), verify the pipeline with:

# Default install (no flash-attn); use a small model or --load-in-4bit on GPU
uv run unbias-plus --text "Women are too emotional to lead."

# With your own model path
uv run unbias-plus --text "Some biased sentence." --model path/to/your/model

# JSON output
uv run unbias-plus --text "Test." --json

Or in Python (same env):

uv run python -c "
from unbias_plus import UnBiasPlus
pipe = UnBiasPlus()  # or UnBiasPlus('your-model-id', load_in_4bit=True)
text = 'Women are too emotional to lead.'
print(pipe.analyze_to_cli(text))
"

REST API + Demo UI

Start the server with unbias-plus --serve (or serve() in Python). This starts a single FastAPI server that:

• Serves the visual demo UI at http://localhost:8000/
• Exposes GET /health → {"status": "ok", "model": "<model_name_or_path>"}
• Exposes POST /analyze → Body: {"text": "Your text here"}. Returns JSON matching BiasResult.

Programmatic start:

from unbias_plus import serve
serve("your-hf-model-id", port=8000, load_in_4bit=False)

Running on a remote server or HPC node: If the server is running on a remote machine, use SSH port forwarding to access the UI in your browser:

ssh -L 8000:localhost:8000 [e-mail]
# or through a login node to a compute node:
ssh -L 8000:gpu-node-hostname:8000 [e-mail]

Then open http://localhost:8000. If port 8000 is already in use locally, use a different local port (e.g. -L 8001:...) and open http://localhost:8001.

If you're using VS Code remote SSH, port forwarding is handled automatically via the Ports tab.

Python API

from unbias_plus import UnBiasPlus, BiasResult, BiasedSegment

pipe = UnBiasPlus("your-hf-model-id", load_in_4bit=False)
result = pipe.analyze("Women are too emotional to lead.")

print(result.binary_label)   # "biased" | "unbiased"
print(result.severity)       # 1–5
print(result.bias_found)     # bool

for seg in result.biased_segments:
    print(seg.original, seg.replacement, seg.severity, seg.bias_type, seg.reasoning)
    print(seg.start, seg.end)  # character offsets in original text

print(result.unbiased_text)  # full neutral rewrite

# Formatted outputs
cli_str  = pipe.analyze_to_cli("...")    # human-readable colored terminal output
d        = pipe.analyze_to_dict("...")   # plain dict
json_str = pipe.analyze_to_json("...")   # pretty-printed JSON string

Development

• Tests: pytest (see pyproject.toml for markers). Run from repo root: uv run pytest tests/.
• Linting / formatting: ruff (format + lint), config in pyproject.toml.
• Type checking: mypy with strict options, mypy_path = "src".

👥 Team

Developed by the AI Engineering team at the Vector Institute.

Ahmed Y. Radwan	Sindhuja Chaduvula	Shaina Raza
Vector Institute	Vector Institute	Vector Institute

Acknowledgement

Resources used in preparing this research are provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.

This research is also supported by the European Union's Horizon Europe research and innovation programme under the AIXPERT project (Grant Agreement No. 101214389).

License

Licensed under the Apache License 2.0. See LICENSE in the repository.

Support

• Open an issue on GitHub: https://github.com/VectorInstitute/unbias-plus/issues