README.md

---
language: ["ko", "en", "ja", "zh", "es", "fr", "de", "pt", "it", "ru", "ar", "hi", "th", "vi", "id", "tr", "nl", "pl"]
tags:
- sentence-transformers
- intent-classification
- multilingual
- distillation
- layer-pruning
library_name: sentence-transformers
pipeline_tag: sentence-similarity
license: apache-2.0
---

# Intent Classifier Student: L6_top

Distilled multilingual sentence encoder for intent classification (Action / Recall / Other).

Created by **layer pruning** from `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`.

## Model Details

| Property | Value |
|----------|-------|
| Teacher | paraphrase-multilingual-MiniLM-L12-v2 |
| Architecture | XLM-RoBERTa (pruned) |
| Hidden dim | 384 |
| Layers | 6 (from 12) |
| Layer indices | [6, 7, 8, 9, 10, 11] |
| Strategy | 6 layers, top half (semantic-focused) |
| Est. params | 106,825,344 |
| Est. FP32 | 407.5MB |
| Est. INT8 | 101.9MB |
| Est. INT8 + vocab pruned | 30.5MB |

## Supported Languages (18)

ko, en, ja, zh, es, fr, de, pt, it, ru, ar, hi, th, vi, id, tr, nl, pl

## Intended Use

This is a **student encoder** designed to be used as the backbone for a lightweight
3-class intent classifier (Action / Recall / Other) in multilingual dialogue systems.

- **Action**: User requests an action (book, order, change settings, etc.)
- **Recall**: User asks about past events or stored information
- **Other**: Greetings, chitchat, emotions, etc.

## Usage

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("L6_top")
embeddings = model.encode(["예약 좀 해줘", "지난번 주문 뭐였지?", "안녕하세요"])
print(embeddings.shape)  # (3, 384)
```

## MTEB Results

### MassiveIntentClassification

**Average: 43.47%**

| Language | Score |
|----------|-------|
| ar | 30.77% |
| en | 55.96% |
| es | 40.81% |
| ko | 46.34% |

### MassiveScenarioClassification

**Average: 47.62%**

| Language | Score |
|----------|-------|
| ar | 33.99% |
| en | 62.04% |
| es | 46.12% |
| ko | 48.34% |



## Training / Distillation

This model was created via **layer pruning** (no additional training):
1. Load teacher: `paraphrase-multilingual-MiniLM-L12-v2` (12 layers, 384 hidden)
2. Select layers: `[6, 7, 8, 9, 10, 11]`
3. Copy embedding weights + selected layer weights
4. Wrap with mean pooling for sentence embeddings

For deployment, vocabulary pruning (250K → ~55K tokens) and INT8 quantization
are applied to meet the ≤50MB size constraint.

## Limitations

- Layer pruning without fine-tuning may lose some quality vs. proper knowledge distillation
- Vocabulary pruning limits the model to the target 18 languages
- Designed for short dialogue utterances, not long documents