Blame for README.md

---
license: mit
tags:
- masked-language-modeling
- dialogue
- speaker-aware
- transformer
- saute
- pytorch
datasets:
- SODA
language:
- en
pipeline_tag: fill-mask
model_type: saute
library_name: transformers
---

# 👨‍🍳 SAUTE: Speaker-Aware Utterance Embedding Unit

**SAUTE** is a lightweight, speaker-aware transformer architecture designed for effective modeling of multi-speaker dialogues. It combines **EDU-level utterance embeddings**, **speaker-sensitive memory**, and **efficient linear attention** to encode rich conversational context with minimal overhead.

---

## 🧠 Overview

SAUTE is tailored for:
- 🗣️ Multi-turn conversations  
- 👥 Multi-speaker interactions  
- 🧵 Long-range dialog dependencies  

It avoids the quadratic cost of full self-attention by summarizing per-speaker memory from EDU embeddings and injecting contextual information through lightweight linear attention mechanisms.

---

## 🧱 Architecture

> 🔍 SAUTE contextualizes each token with speaker-specific memory summaries built from utterance-level embeddings.

- **EDU-Level Encoder**: Mean-pooled BERT outputs per utterance.
- **Speaker Memory**: Outer-product-based accumulation per speaker.
- **Contextualization Layer**: Integrates memory summaries with current token representations.

![saute-architecture](https://github.com/user-attachments/assets/7f18d5b8-9c6b-4577-b718-206a34d84535)

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## 🚀 Key Features

- 🧠 **Speaker-Aware Memory**: Structured per-speaker representation of dialogue context.
- ⚡ **Linear Attention**: Efficient and scalable to long dialogues.
- 🧩 **Pretrained Transformer Compatible**: Can plug into frozen or fine-tuned BERT models.
- 🪶 **Lightweight**: ~4M parameters less than 2-layer with strong MLM performance improvements.

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## 📈 Performance (on SODA, Masked Language Modeling)


| Model                      | Avg MLM Acc | Best MLM Acc |
|---------------------------|-------------|--------------|
| BERT-base (frozen)        | 33.45       | 45.89        |
| + 1-layer Transformer     | 68.20       | 76.69        |
| + 2-layer Transformer     | 71.81       | 79.54        |
| **+ 1-layer SAUTE (Ours)**        | **72.05**   | **80.40%**   |
| + 3-layer Transformer| 73.5 | 80.84 |
| **+ 3-layer SAUTE (Ours)**| **75.65** | **85.55%**|

> SAUTE achieves the best accuracy using fewer parameters than multi-layer transformers.

---

## 📚 Citation / Paper

📄 [SAUTE: Speaker-Aware Utterance Embedding Unit (PDF)](https://github.com/user-attachments/files/20689695/SAUTE_Speaker_Aware_Utterance_Embedding_Unit.pdf)

---

## 🔧 How to Use

```python
from saute_model import SAUTEConfig, UtteranceEmbedings
from transformers import BertTokenizerFast

# Load tokenizer and model
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
model = UtteranceEmbedings.from_pretrained("JustinDuc/saute")

# Prepare inputs (example)
outputs = model(
    input_ids=input_ids,
    attention_mask=attention_mask,
    speaker_names=speaker_names
)