Update README.md

README.md

@@ -1,52 +1,128 @@
----
-license: mit
-dataset_info:
-  features:
-  - name: edge_index
-    list:
-      list: int64
-  - name: edge_attr
-    list:
-      list: int64
-  - name: x
-    list:
-      list: int64
-  - name: ba_edge_index
-    list:
-      list: int64
-  - name: ba_edge_attr
-    list:
-      list: float64
-  - name: fra_edge_index
-    list:
-      list: int64
-  - name: fra_edge_attr
-    list:
-      list: int64
-  - name: cluster_idx
-    list: int64
-  - name: bafra_edge_index
-    list:
-      list: int64
-  - name: bafra_edge_attr
-    list:
-      list: float64
-  - name: smiles
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 17772414767
-    num_examples: 1551232
-  - name: validation
-    num_bytes: 454862268
-    num_examples: 39775
-  download_size: 1889271320
-  dataset_size: 18227277035
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
----
+---
+license: mit
+dataset_info:
+  features:
+  - name: edge_index
+    list:
+      list: int64
+  - name: edge_attr
+    list:
+      list: int64
+  - name: x
+    list:
+      list: int64
+  - name: ba_edge_index
+    list:
+      list: int64
+  - name: ba_edge_attr
+    list:
+      list: float64
+  - name: fra_edge_index
+    list:
+      list: int64
+  - name: fra_edge_attr
+    list:
+      list: int64
+  - name: cluster_idx
+    list: int64
+  - name: bafra_edge_index
+    list:
+      list: int64
+  - name: bafra_edge_attr
+    list:
+      list: float64
+  - name: smiles
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 17772414767
+    num_examples: 1551232
+  - name: validation
+    num_bytes: 454862268
+    num_examples: 39775
+  download_size: 1889271320
+  dataset_size: 18227277035
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+---
+
+# MuMo Pretraining Dataset
+
+- 📄 Paper: [NeurIPS 2025 Poster](https://neurips.cc/virtual/2025/poster/119127)
+- 📬 Contact:
+  - Zihao Jing: zjing29@uwo.ca
+  - Pingzhao Hu: phu49@uwo.ca
+
+## Abstract
+
+Multimodal molecular models often suffer from 3D conformer unreliability and modality collapse, limiting robustness and generalization. MuMo addresses these challenges with a Structured Fusion Pipeline (SFP) that combines 2D topology and 3D geometry into a stable structural prior, and a Progressive Injection (PI) mechanism that asymmetrically integrates this prior into the sequence stream. Built on a state space backbone, MuMo supports long-range dependency modeling. Across 21+ benchmarks, MuMo achieves strong improvements and robustness to 3D conformer noise. See paper for details: [NeurIPS 2025](https://neurips.cc/virtual/2025/poster/119127).
+
+## Dataset Overview
+
+- Source: filtered ChEMBL (~1.6M molecules)
+- Purpose: language-style pretraining over SMILES with graph/geometry supervision
+- Processing: generated using `preprocess/mol3d_processor.py`
+- Splits: `train` (≈1.55M), `validation` (≈39.8K)
+
+You can load this dataset directly via the Hugging Face Datasets API or via our training scripts with `--dataset_name`.
+
+## Data Schema (per example)
+
+- `smiles` (string): canonical SMILES string
+- Graph keys (2D topology and basic chemistry):
+  - `x`: node feature matrix (list of lists)
+  - `edge_index`: 2×E edge indices (list of two lists of int)
+  - `edge_attr`: edge feature matrix (list of lists)
+- Fragment-level keys (BRICS-based):
+  - `fra_edge_index`: fragment connectivity indices (list of lists of int)
+  - `fra_edge_attr`: fragment edge features (list of lists)
+- Geometry-level keys:
+  - `ba_edge_index`: geometry-based connections (list of lists of int)
+  - `ba_edge_attr`: features for geometry connections (list of lists)
+- Geometry–fragment keys:
+  - `bafra_edge_index`: geometry fragment connectivity (list of lists of int)
+  - `bafra_edge_attr`: features for geometry fragments (list of lists)
+- `cluster_idx` (list of int): fragment membership index per atom (which fragment each atom belongs to)
+
+Notes:
+- Shapes and dtypes may be adapted by downstream collators; values are stored as lists for portability.
+- All lists are serialized for JSONL storage and converted to tensors during training.
+
+## Usage
+
+Python (Datasets):
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("zihaojing/MuMo-Pretraining")
+print(ds)
+example = ds["train"][0]
+print(example.keys())
+```
+
+Training script (Transformers):
+
+```bash
+deepspeed train/pretrain.py \
+  --dataset_name zihaojing/MuMo-Pretraining \
+  --do_train --do_eval \
+  ...
+```
+
+## Processing Pipeline
+
+We use `preprocess/mol3d_processor.py` to derive graph and geometry features from SMILES:
+
+- Atom features, bonds, and 2D topology populate `x`, `edge_index`, `edge_attr`.
+- BRICS-based fragmentation provides `fra_edge_index`, `fra_edge_attr`, and `cluster_idx`.
+- Geometry connections and fragment geometry provide `ba_edge_index`, `ba_edge_attr`, `bafra_edge_index`, `bafra_edge_attr`.
+
+## License
+
+MIT