Update README.md

README.md

@@ -14,19 +14,7 @@ size_categories:
 configs:
 - config_name: RottenReviews
   data_files:
-  - split: ICLR2024
-    path:
-    - raw/iclr2024_submissions.jsonl
-  - split: NIPS2023
-    path:
-    - raw/neurips2023_submissions.jsonl
-  - split: F1000 Journal
-    path:
-    - raw/f1000research_submissions.jsonl
-  - split: Semantic Web Journal
-    path:
-    - raw/semantic-web-journal_submissions.jsonl
-  - split: Human Annotation Data
+  - split: human_annotation_data
     path:
     - human_annotation_data.jsonl
 ---
@@ -39,6 +27,8 @@ Quick links: 📃 [Paper](https://reviewer.ly/wp-content/themes/reviewerly-vite-
 
 **RottenReviews** is a benchmark dataset designed to facilitate research on **peer review quality assessment** using multiple types of evaluation signals, including human expert annotations, structured metrics derived from textual features, and large language model (LLM)-based judgments.
 
+Note: This HF repo only contains the raw files and the human annotation data records. Some dataset components are available only in our Google Drive. Follow repository documentation for downloading the processed files.
+
 ## 🧠 Dataset Summary
 
 Peer review quality is central to the scientific publishing process, but systematic evaluation at scale is challenging. The **RottenReviews** dataset addresses this gap by providing a large corpus of academic peer reviews enriched with reviewer metadata and multiple quality indicators:
@@ -52,38 +42,6 @@ Peer review quality is central to the scientific publishing process, but systema
 The dataset was introduced to support research on benchmarking and modeling peer review quality at scale. It contains thousands of submissions and reviewer profiles, making it one of the most comprehensive resources for peer review quality analysis.
 
 
-## 📂 Dataset Structure
-
-The dataset is organized into multiple components reflecting different stages of processing and annotation:
-
-| **Folder / File**               | **Description**                                          | **Format**      |
-| ------------------------------- | -------------------------------------------------------- | --------------- |
-| `raw/`                          | Raw extracted submissions and reviews from source venues | JSON / PKL      |
-| `processed/`                    | Cleaned and structured review records                    | CSV / JSON      |
-| `human_annotation/`             | Subset of reviews annotated by human experts             | CSV / JSON      |
-| `feature_extraction/`           | Scripts and outputs for computing quantifiable metrics   | Notebooks / CSV |
-| `predict_review_quality_score/` | Inputs and outputs for quality prediction models         | CSV / JSON      |
-
-Due to size constraints, the full dataset is not hosted directly in the repository. Instructions for downloading the data are provided in the project README.
-
-
-## 📊 Data Fields
-
-### Review Record (example fields)
-
-* `id`: Unique identifier for the submission or review item
-* `date`: Submission or review date
-* `type`: Item type (e.g., Full Paper)
-* `title`: Paper title
-* `abstract`: Paper abstract
-* `reviews`: A list of review objects, each containing:
-
-  * `reviewer`: Anonymized reviewer identifier
-  * `date`: Review submission date
-  * `suggestion`: Reviewer recommendation (e.g., accept, reject)
-  * `comment`: Free-text review content
-
-
 ## 📌 Usage Example
 
 ```python
@@ -96,7 +54,7 @@ processed_reviews = dataset["processed"]
 print(processed_reviews[0])
 
 # Access human annotations
-human_data = dataset["human_annotation"]
+human_data = dataset["human_annotation_data"]
 print(human_data[0])
 ```