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README.md

@@ -1,85 +1,96 @@
-# DPST Replication
-
-Replication package for the EMNLP 2025 paper:
-
-> **Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees**
-> Stephen Meisenbacher, Maulik Chevli, Florian Matthes
-> [https://aclanthology.org/2025.emnlp-main.455/](https://aclanthology.org/2025.emnlp-main.455/)
-
-All credit goes to the original authors. This repository provides a replication environment for running the method.
-
-```bibtex
-@inproceedings{meisenbacher-etal-2025-leveraging,
-    title = "Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees",
-    author = "Meisenbacher, Stephen and Chevli, Maulik and Matthes, Florian",
-    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
-    month = nov,
-    year = "2025",
-    address = "Suzhou, China",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2025.emnlp-main.455/",
-    doi = "10.18653/v1/2025.emnlp-main.455",
-    pages = "8976--8992",
-}
-```
-
-## Setup
-
-**1. Clone this repository**
-
-```bash
-git clone https://huggingface.co/datasets/weijunl/dpst-replication
-cd dpst-replication
-```
-
-**2. Download and extract the Weaviate triple database**
-
-```bash
-tar -xzf weaviate-data.tar.gz
-```
-
-**3. Create conda environment**
-
-```bash
-conda create -n dpst python=3.10
-conda activate dpst
-
-pip install numpy pandas tqdm torch
-pip install transformers==4.52.4 datasets sentence-transformers==2.2.2
-pip install nltk einops datasketch
-pip install stanford-openie
-# install weaviate-client AFTER stanford-openie (protobuf conflict workaround)
-pip install weaviate-client==4.11.1
-```
-
-**4. Start Weaviate (first time only — creates the container)**
-
-```bash
-docker run -d --name weaviate -p 8080:8080 -p 50051:50051 \
-  -v ./weaviate-data:/var/lib/weaviate \
-  -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
-  -e PERSISTENCE_DATA_PATH=/var/lib/weaviate \
-  -e DEFAULT_VECTORIZER_MODULE=none \
-  cr.weaviate.io/semitechnologies/weaviate:1.26.4
-```
-
-After the first time, `run_dpst.sh` will automatically start the existing container if it is not running.
-
-## Usage
-
-```bash
-conda activate dpst
-bash run_dpst.sh <mode> <epsilon> [input_file] [output_file]
-
-  mode     : 50k | 100k | 200k
-  epsilon  : privacy budget (e.g. 0.1, 1.0, 10.0)
-  input    : path to a text file (one sentence per line); omit for demo texts
-  output   : path to save privatized output; omit to print to stdout
-```
-
-Example:
-```bash
-bash run_dpst.sh 200k 1.0 input.txt output.txt
-```
-
-A HuggingFace token is required for the generation model (Llama-3.2); `run_dpst.sh` will prompt for it at runtime.
+---
+pretty_name: DPST Replication
+language:
+- en
+tags:
+- local-differential-privacy
+- text-rewriting
+- nlp
+- replication
+---
+
+# DPST Replication
+
+Replication package for the EMNLP 2025 paper:
+
+> **Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees**  
+> Stephen Meisenbacher, Maulik Chevli, Florian Matthes  
+> [https://aclanthology.org/2025.emnlp-main.455/](https://aclanthology.org/2025.emnlp-main.455/)
+
+All credit goes to the original authors. This repository provides a replication environment for running the method, based on the official released code: https://github.com/sjmeis/DPST.
+
+```bibtex
+@inproceedings{meisenbacher-etal-2025-leveraging,
+    title = "Leveraging Semantic Triples for Private Document Generation with Local Differential Privacy Guarantees",
+    author = "Meisenbacher, Stephen and Chevli, Maulik and Matthes, Florian",
+    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2025",
+    address = "Suzhou, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2025.emnlp-main.455/",
+    doi = "10.18653/v1/2025.emnlp-main.455",
+    pages = "8976--8992",
+}
+```
+
+## Setup
+
+**1. Clone this repository**
+
+```bash
+git clone https://huggingface.co/datasets/weijunl/dpst-replication
+cd dpst-replication
+```
+
+**2. Download and extract the Weaviate triple database**
+
+```bash
+tar -xzf weaviate-data.tar.gz
+```
+
+**3. Create conda environment**
+
+```bash
+conda create -n dpst python=3.10
+conda activate dpst
+
+pip install numpy pandas tqdm torch
+pip install transformers==4.52.4 datasets sentence-transformers==2.2.2
+pip install nltk einops datasketch
+pip install stanford-openie
+# install weaviate-client AFTER stanford-openie (protobuf conflict workaround)
+pip install weaviate-client==4.11.1
+```
+
+**4. Start Weaviate (first time only — creates the container)**
+
+```bash
+docker run -d --name weaviate -p 8080:8080 -p 50051:50051 \
+  -v ./weaviate-data:/var/lib/weaviate \
+  -e AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED=true \
+  -e PERSISTENCE_DATA_PATH=/var/lib/weaviate \
+  -e DEFAULT_VECTORIZER_MODULE=none \
+  cr.weaviate.io/semitechnologies/weaviate:1.26.4
+```
+
+After the first time, `run_dpst.sh` will automatically start the existing container if it is not running.
+
+## Usage
+
+```bash
+conda activate dpst
+bash run_dpst.sh <mode> <epsilon> [input_file] [output_file]
+
+  mode     : 50k | 100k | 200k
+  epsilon  : privacy budget (e.g. 0.1, 1.0, 10.0)
+  input    : path to a text file (one sentence per line); omit for demo texts
+  output   : path to save privatized output; omit to print to stdout
+```
+
+Example:
+```bash
+bash run_dpst.sh 200k 1.0 input.txt output.txt
+```
+
+A HuggingFace token is required for the generation model (Llama-3.2); `run_dpst.sh` will prompt for it at runtime.