reqlint-smollm3-3b

HuggingFaceTB/SmolLM3-3B fine-tuned with LoRA to check a requirement for common writing defects and rewrite it in EARS form. Where the rewrite needs information the original does not give, such as a time limit or the system that acts, it leaves a <placeholder> instead of inventing one.

[!WARNING] Review aid only. Not a substitute for requirements review under DO-178C, ISO 26262, EN 50128, IEC 61508 or any other process. A clean result does not mean a requirement is correct, complete or verifiable.

🚀 Usage

import json

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "jgalego/reqlint-smollm3-3b"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto")

messages = [
    {"role": "system", "content": "Check the requirement for these defects: compound, escape, no_unit, passive, pronoun, tbd, vague, weak_verb. Answer with JSON: {\"defects\": [...], \"rewrite\": [...]}. The rewrite lists one EARS requirement per line, with a \u003cplaceholder\u003e wherever information is missing. Leave both lists empty when there are no defects."},
    {"role": "user", "content": "The battery management system shall open the main contactor within 2 s and measure the cell voltages every 1."},
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    enable_thinking=False,
    return_tensors="pt",
    return_dict=True,
)
out = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(json.loads(tokenizer.decode(out[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)))

Expected output:

{"defects": ["compound", "no_unit"], "rewrite": ["The battery management system shall open the main contactor within 2 s.", "The battery management system shall measure the cell voltages every 1 <unit>."]}

🔎 Defects

🏋️ Training

The generator writes requirements in the six EARS patterns for rail, automotive, aviation, space and energy systems, then injects up to two defects into each. Labels and rewrites come from the same structure, so they are exact.

📊 Results

Defect detection, micro-F1 over the 8 classes. rules is a keyword and regex linter built from the word lists the generator uses for training.

• Real: hand-labelled requirements from public-domain and openly licensed documents, in jgalego/reqlint-real.
• Synthetic, unseen: maritime and medical systems, with vague words, escape clauses, weak verbs and placeholders that never appear in training.
• Synthetic, seen: same domains and word lists as training, different requirements.

Per-defect F1 on real requirements:

⚠️ Limitations

• One requirement at a time, in English. It does not check consistency across a specification.
• It flags how a requirement is written, not whether it is technically right.
• The training data is synthetic. The real set is small, so treat its scores as indicative.