Update README.md

README.md

@@ -1,266 +1,41 @@
 ---
 license: mit
-language:
-- en
-pretty_name: Metamath Proof Graphs
-size_categories:
-- 10K<n<100K
+pretty_name: Metamath Proof Graphs (10k)
 task_categories:
 - graph-ml
 tags:
 - graphs
 - gnn
-- theorem-proving
-- reasoning
 - metamath
 - pytorch-geometric
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
-- config_name: raw
-  data_files:
-  - split: train
-    path: raw/train-*
-  - split: validation
-    path: raw/validation-*
-  - split: test
-    path: raw/test-*
-dataset_info:
-- config_name: default
-  features:
-  - name: theorem_index
-    dtype: int64
-  - name: theorem_label
-    dtype: string
-  - name: graph_type
-    dtype: string
-  - name: nodes
-    list:
-    - name: label
-      dtype: string
-    - name: node_index
-      dtype: int64
-    - name: num
-      dtype: int64
-    - name: statement
-      dtype: string
-  - name: edge_index
-    list:
-      list: int64
-  - name: edge_attr
-    list:
-      list: int64
-  splits:
-  - name: train
-    num_bytes: 496594530
-    num_examples: 76489
-  - name: validation
-    num_bytes: 27586060
-    num_examples: 4249
-  - name: test
-    num_bytes: 27592552
-    num_examples: 4250
-  download_size: 92724290
-  dataset_size: 551773142
-- config_name: raw
-  features:
-  - name: theorem_index
-    dtype: int64
-  - name: theorem_label
-    dtype: string
-  - name: graph_type
-    dtype: string
-  - name: nodes
-    list:
-    - name: label
-      dtype: string
-    - name: node_index
-      dtype: int64
-    - name: num
-      dtype: int64
-    - name: statement
-      dtype: string
-  - name: edge_index
-    list:
-      list: int64
-  - name: edge_attr
-    list:
-      list: int64
-  splits:
-  - name: train
-    num_bytes: 496594530
-    num_examples: 76489
-  - name: validation
-    num_bytes: 27586060
-    num_examples: 4249
-  - name: test
-    num_bytes: 27592552
-    num_examples: 4250
-  download_size: 92724290
-  dataset_size: 551773142
+- topobench
+size_categories:
+- 10K<n<100K
 ---
 
-# Metamath Proof Graphs
-
-Graph-structured representations of Metamath theorem proofs designed for reasoning-focused Graph Neural Networks.  
-Each example contains node features, directed edges, and theorem labels suitable for proof-step prediction and symbolic reasoning research.
-
-## Dataset Summary
-
-This dataset converts Metamath’s human-verified formal proofs into directed graphs:
-
-- **Nodes** represent logical statements (proof steps or hypotheses)
-- **Edges** represent inference dependencies
-- **Node features** are 512-dimensional Universal Sentence Encoder embeddings
-- **Labels** represent the proven theorem, normalized into a compact integer space
-
-Ideal for research on GNN-based symbolic reasoning, proof-step prediction, and theorem classification.
-
-## Dataset Structure
-
-Each proof instance includes:
-
-- **Proof Graph** — logical inference steps represented as a directed graph  
-- **Statement Graph** — hypotheses and conclusion as a compact graph  
-- **Node Features** — 512-dimensional USE embeddings  
-- **Label** — integer id for the target theorem (with UNK handling below)
-
-## Label Normalization (UNK)
-
-To reduce extreme class imbalance:
-
-- Labels appearing **five times or fewer** are mapped to a single `UNK` class  
-- Remaining labels are reindexed into a **dense, contiguous integer space**  
-- A reverse mapping is provided for converting predictions back to theorem names
-
-## Label Mapping
-
-Each theorem label is mapped to an integer via a fixed index derived from the Metamath label list.  
-A reverse index is included for interpretability.
-
-## Node Embeddings
-
-Node representations use **Universal Sentence Encoder (USE v4)**:
-
-- 512-dimensional embeddings  
-- Computed once during preprocessing  
-- Applied to both proof-step nodes and statement-graph nodes  
-
-## Data Splits
-
-A fixed-seed 80/10/10 split is used in reference experiments:
-
-| Split | Examples |
-|-------|----------|
-| Train | ~8,000 |
-| Validation | ~1,000 |
-| Test | ~1,000 |
-
-## Dataset Size (approx.)
+# Metamath Proof Graphs (10k)
 
-- **10,000** proof graphs  
-- **50–200** nodes per graph  
-- **512-dimensional** node features  
-- **~829** normalized labels (including `UNK`)  
+This repository hosts a single PyTorch Geometric dataset file used for the TAG-DS TopoBench challenge.
 
-## Example Data Structure
+## Contents
 
-```json
-{
-  "x": [...],           // Node features [num_nodes, 512]
-  "edge_index": [...],  // Directed edges (COO format)
-  "graph_features": [...],
-  "label": 42
-}
-```
+- `data.pt`  
+  A serialized object with the following keys:
+  - `data` (PyG `Data` object with collated node features, edges and labels)
+  - `slices` (index information for reconstructing individual graphs)
+  - `train_idx` (tensor of graph indices for training)
+  - `val_idx` (tensor of graph indices for validation)
+  - `test_idx` (tensor of graph indices for testing)
 
-## Intended Use
-
-Designed for:
-
-- Proof-step prediction  
-- Symbolic reasoning research  
-- Theorem classification tasks  
-- Benchmarking GNN architectures (GIN, GAT, Graph Transformers)
-
-Not intended for:
-
-- End-to-end automated theorem proving  
-- Non-graph ML tasks without graph conversion  
-
-## Usage
-
-### Hugging Face Datasets
+## Basic usage
 
 ```python
-from datasets import load_dataset
-ds = load_dataset("jableable/metamath-proof-graphs")
-sample = ds["train"][0]
-```
-
-### PyTorch Geometric (loader included)
-
-```python
-from proofgraphs import ProofDataset
-dataset = ProofDataset(root="data/")
-```
-
-## Limitations
-
-- USE embeddings are fixed; domain-specific encoders may yield better performance  
-- Some theorem classes remain imbalanced even after `UNK` grouping  
-- The dataset targets step-level reasoning, not full theorem proving  
-- Logical structure is encoded via graph topology + text embeddings, not explicit grammar
-
-## Baseline Performance
-
-**Reference GNN (GIN, 3 layers):**
-- **Top-5 Accuracy:** 70 percent  
-- (Link to training code to be added)
-
-## Dataset Creation
-
-### Source
-
-Derived from the public-domain **Metamath** proof database.  
-Graph extraction, embedding generation, normalization, and preprocessing were performed by the dataset author.
-
-### Preprocessing (brief)
-
-1. Convert proofs to directed graphs  
-2. Generate USE embeddings for each node  
-3. Normalize labels and collapse rare ones into `UNK`  
-4. Reindex labels contiguously  
-5. Generate fixed-seed train/val/test splits  
-
-Scripts are provided in the repository.
-
-### Acknowledgments
-
-This dataset builds on earlier code and extraction work developed in collaboration with Hongyi Shen and Evgeniya Lagoda.  
-Their contributions to the initial graph-processing pipeline informed the final dataset.  
-All packaging, normalization, and Hugging Face dataset preparation were performed by the dataset author.
-
-## Citation
-
-If you use this dataset, please cite:
-
-```bibtex
-@misc{able2025proofgraphs,
-  author       = {Able, Jared},
-  title        = {Metamath Proof Graphs Dataset},
-  year         = {2025},
-  publisher    = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/datasets/jableable/metamath-proof-graphs}}
-}
-```
+import torch
 
-## Contact
+obj = torch.load("data.pt", weights_only=False)
 
-For questions or issues, please open an issue on the GitHub repo.
+data      = obj["data"]
+slices    = obj["slices"]
+train_idx = obj["train_idx"]
+val_idx   = obj["val_idx"]
+test_idx  = obj["test_idx"]