Update README with dataset viewer configs

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README.md

@@ -12,17 +12,22 @@ tags:
 - robustness
 - llm
 - injection
+configs:
+- config_name: default
+  data_files:
+  - split: all_urls
+    path: "webpage_links.csv"
 ---
 
-## The *RAGDOLL* E-Commerce Webpage Dataset
+# The *RAGDOLL* E-Commerce Webpage Dataset
 
 This repository contains the ***RAGDOLL*** (Retrieval-Augmented Generation Deceived Ordering via AdversariaL materiaLs) dataset as well as its LLM-automated collection pipeline.
 
 The ***RAGDOLL*** dataset is from the paper [*Ranking Manipulation for Conversational Search Engines*](https://arxiv.org/pdf/2406.03589) from Samuel Pfrommer, Yatong Bai, Tanmay Gautam, and Somayeh Sojoudi. For experiment code associated with this paper, please refer to [this repository](https://github.com/spfrommer/cse-ranking-manipulation).
 
-The dataset consists of 10 product categories (see [`dataset/categories.md`](https://github.com/spfrommer/ragdoll-data-pipeline/blob/main/dataset/categories.md)), with at least 8 brands for each category and 1-3 products per brand, summing to 1147 products in total. The evaluations in our paper are performed with a balanced subset with precicely 8 brands per category and 1 product per brand.
+The dataset consists of 10 product categories (see [`categories.md`](https://huggingface.co/datasets/Bai-YT/RAGDOLL/blob/main/README.md)), with at least 8 brands for each category and 1-3 products per brand, summing to 1147 products in total. The evaluations in our paper are performed with a balanced subset with precicely 8 brands per category and 1 product per brand.
 
-The URLs of the full 1147 products are shared at [`dataset/webpage_links`](https://github.com/spfrommer/ragdoll-data-pipeline/tree/main/dataset/webpages_links). We additionally share the downloaded webpages associated with the data subset used in our paper at [`dataset/final_dataset.zip`](https://github.com/spfrommer/ragdoll-data-pipeline/tree/main/dataset/final_dataset.zip) for reproducibility.
+The URLs of the full 1147 products are shared at [`webpage_links.csv`](https://huggingface.co/datasets/Bai-YT/RAGDOLL/blob/main/webpage_links.csv). We additionally share the downloaded webpages associated with the data subset used in our paper at [`webpage_contents`](https://huggingface.co/datasets/Bai-YT/RAGDOLL/tree/main/webpage_contents) for reproducibility.
 
 
 ### Description
@@ -48,12 +53,12 @@ When downloading webpages, it is highly recommended to download *dynamic* pages
 - Use the `selenium` package to invoke web browsers (faster, more up-to-date).
 - Download from CommonCraw (slower, more reproducible).
 
-The downloading method is controlled with [`cc_fetch`](https://github.com/spfrommer/ragdoll-data-pipeline/blob/11c54ca77029a743ada72a2548df2b3a86262bc7/utils/query_utils.py#L39).
+The downloading method is controlled with [`cc_fetch`](https://huggingface.co/datasets/Bai-YT/RAGDOLL/blob/a19ce2a29f7317aefdbfae4e469f28d4cfa25d21/collection_pipeline/utils/query_utils.py#L39).
 
 
 ### Collecting Your Own Dataset
 
-You can use this data collection pipeline to collect additional websites or additional product categories. To do so, modify [`dataset/categories`](https://github.com/spfrommer/cse-ranking-manipulation) accordingly and run the code with the following these instructions.
+You can use this data collection pipeline to collect additional websites or additional product categories. To do so, modify [`categories.md`](https://huggingface.co/datasets/Bai-YT/RAGDOLL/blob/main/README.md) accordingly and run the code with the following these instructions.
 
 Required packages:
 ```
@@ -63,7 +68,7 @@ click pandas torch requests bs4 lxml unidecode selenium openai cdx_toolkit
 To query GPT-4-Turbo to collect a set of brands and products, run
 ```
 python find_sites.py --model "gpt-4-turbo"
-# feel free to replace with gpt-4o
+# feel free to replace with gpt-4o or other OpenAI models without code modification
 ```
 
 To clean the dataset (with Google Search API and GPT-3.5-Turbo), run