JustinDuc
/

saute

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 ---
+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)
+---
+## 🚀 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**: Adds ~2M parameters with strong MLM performance improvements.
+---
+## 📈 Performance (on SODA, Masked Language Modeling)
+| Model Variant             | Avg MLM Accuracy | Best MLM Accuracy |
+|---------------------------|------------------|-------------------|
+| BERT-base (frozen)        | 33.45            | 45.89             |
+| + 1-layer Transformer     | 68.20            | 76.69             |
+| + 2-layer Transformer     | 71.81            | 79.54             |
+| **+ SAUTE (Ours)**        | **72.05**        | **80.40**         |
+> 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/20640425/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
+)