---
language: ja
tags:
- modernbert
- japanese
- emergency-call
- phase-detection
- boundary-detection
license: apache-2.0
datasets:
- custom
metrics:
- accuracy
- f1
---

# 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

```python
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