ttt421/modernbert-phase-detector

NEC-119 ModernBERT Phase & Boundary Detector

Model Description

This model is fine-tuned from sbintuitions/modernbert-ja-310m for Japanese emergency call (119) transcript analysis. It performs two tasks simultaneously:

1. Phase Classification: Classifies conversation phases (INIT/LOC/INC/SUP)
2. Boundary Detection: Detects phase boundaries in conversation

Training Details

• Base Model: sbintuitions/modernbert-ja-310m
• Training Data: 45,483 instances from Japanese emergency call transcripts
• Validation Data: 4,984 instances
• Test Data: 9,605 instances
• Training Configuration:
  • Epochs: 5
  • Batch Size: 16 (effective 32 with gradient accumulation)
  • Learning Rate: 1e-5
  • Max Sequence Length: 1024 tokens
  • Optimizer: AdamW
  • Scheduler: Cosine

Performance

Test Set Results (After 1 epoch)

• Phase Classification Accuracy: 84.9%
• Boundary Detection Accuracy: 94.6%
• Phase F1-Macro: 0.813
• Boundary F1: 0.626
• Both Correct Accuracy: 81.8%

Usage

from transformers import AutoTokenizer, AutoModel
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/nec119-modernbert-phase-boundary")
model = AutoModel.from_pretrained("your-username/nec119-modernbert-phase-boundary")

# Prepare input
context = "previous conversation text"
current_utterance = "current line to classify"
inputs = tokenizer(context, current_utterance, return_tensors="pt", max_length=1024, truncation=True)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)
    # Extract predictions from outputs

Phase Labels

• INIT (0): Initial phase
• LOC (1): Location identification phase
• INC (2): Incident details phase
• SUP (3): Support/supplementary phase

Limitations

This model is specifically trained for Japanese emergency call transcripts and may not generalize well to other domains or conversation types.

License

Apache 2.0