modernbert-primary_topic

modernbert-primary_topic

Fine-tuned ModernBERT-base classifier that assigns a primary legal topic to a multi-turn conversation.

Part of the Legal QA collection · Try the interactive demo →

Model description

Stage 2 of a two-model encoder routing pipeline:

modernbert-primary_topic predicts one of 14 legal topic labels, plus a (non-guidance) class for conversations where the user is not seeking legal help. In practice, run modernbert-seeks_guidance first and only trust the topic label when it predicts True.

Input preprocessing: full user–assistant conversation, serialized as Role: content lines per turn.

Topic taxonomy

Selection rules: use CONTRACTS only when the issue is mainly about a civil agreement and is not better captured by WORK, HOUSING, CONSUMER_DEBT, or BUSINESS. Use INTERNATIONAL_CROSS_BORDER only when the cross-border or jurisdictional aspect is central, not merely incidental.

Results

Joint pipeline (with modernbert-seeks_guidance, max length 4096, full input, test): legal 87.9% · topic 78.5% · joint 78.5%.

Usage

Pipeline (recommended)

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

def serialize(messages, input_mode="full"):
    lines = []
    for msg in messages:
        role = msg["role"]
        if input_mode == "user" and role != "user":
            continue
        lines.append(f"{role.capitalize()}: {msg['content']}")
    return "\n".join(lines)

def predict(model_id, text):
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForSequenceClassification.from_pretrained(model_id)
    enc = tokenizer(text, truncation=True, max_length=4096, return_tensors="pt")
    with torch.no_grad():
        pred_id = model(**enc).logits.argmax(dim=-1).item()
    label = model.config.id2label[str(pred_id)]
    return label or "(non-guidance)"

conversation = [
    {"role": "user", "content": "Can my landlord evict me without notice?"},
    {"role": "assistant", "content": "Eviction rules depend on your jurisdiction..."},
    {"role": "user", "content": "I'm in California on a month-to-month lease."},
]

topic = predict(
    "AmirMohseni/modernbert-primary_topic",
    serialize(conversation, input_mode="full"),
)
print(topic)  # e.g. HOUSING

Pair with modernbert-seeks_guidance for the full routing pipeline (see that model card for a complete example).

Single model

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="AmirMohseni/modernbert-primary_topic",
)
text = "User: Can my landlord evict me?\nAssistant: Eviction rules depend on your jurisdiction...\nUser: I'm in California on a month-to-month lease."
print(classifier(text))

Intended uses & limitations

Use for: assigning a topic label to user queries already flagged as seeking legal guidance.

Do not use for: legal advice, or as a standalone filter for legal intent (use the seeks_guidance model first).

Caveats: silver labels from GPT-5.4; English only.

Training data

Dataset: AmirMohseni/WildChat-Legal-Classification-V2-Balanced

• Balanced legal / non-legal rows from WildChat-1M with GPT-5.4 structured labels
• Splits: train 1909 · val 106 · test 107
• Target field: primary_topic (empty for non-guidance rows)

Training procedure

Framework versions

• Transformers 5.8.1 · PyTorch 2.10.0 · Datasets 4.8.5