Fix corrupted .gitattributes + resolve README merge (keep local)

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README.md

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-version https://git-lfs.github.com/spec/v1
-oid sha256:6accd353a10dd590442641dacad27c7bcc8fca3f51b078c1c0d4c23f5441987a
-size 3009
+---
+license: cc-by-nc-nd-4.0
+tags:
+- medical-imaging
+- echocardiography
+- embeddings
+- mimic-iv-echo
+- v-jepa2
+- video-embeddings
+- self-supervised-learning
+---
+
+# MIMIC-IV-Echo V-JEPA2 Embeddings
+
+Pre-computed video embeddings for [MIMIC-IV-Echo](https://physionet.org/content/mimic-iv-echo/) echocardiography videos, extracted using [V-JEPA2](https://github.com/facebookresearch/jepa) (Meta's self-supervised video encoder).
+
+## Dataset
+
+| | |
+|---|---|
+| **Videos** | 525,328 echocardiography clips |
+| **Subjects** | ~4,800 patients |
+| **Studies** | ~7,200 echo studies |
+| **Embedding model** | V-JEPA2 ViT-L (300M params) |
+| **Embedding dim** | 1024 (float32) |
+| **Format** | Sharded Parquet (10 shards, ~2.1 GB total) |
+
+## Structure
+
+```
+mimic-iv-echo-jepa-embeddings/
+└── jepa-l-embeddings/
+    ├── train-00000-of-00010.parquet   (p10, 51K rows, 202 MB)
+    ├── train-00001-of-00010.parquet   (p11, 52K rows, 205 MB)
+    ├── ...
+    └── train-00009-of-00010.parquet   (p19, 53K rows, 208 MB)
+```
+
+Each shard corresponds to one MIMIC-IV patient folder (p10-p19).
+
+## Columns
+
+| Column | Type | Source | Description |
+|--------|------|--------|-------------|
+| `subject_id` | int64 | echo-record-list.csv | MIMIC patient ID |
+| `study_id` | int64 | echo-record-list.csv | Echo study ID |
+| `dicom_id` | str | filename | Original DICOM identifier (e.g. `94106955_0001`) |
+| `file_path` | str | embedding key | Relative path to source MP4 |
+| `acquisition_datetime` | str | echo-record-list.csv | Per-video acquisition timestamp |
+| `study_datetime` | str | echo-study-list.csv | Per-study timestamp |
+| `note_id` | str | echo-study-list.csv | Clinical note reference (nullable) |
+| `note_seq` | str | echo-study-list.csv | Note sequence number (nullable) |
+| `note_charttime` | str | echo-study-list.csv | Note chart time (nullable) |
+| `embedding` | list[float32] | V-JEPA2 ViT-L | 1024-dim video embedding |
+
+Metadata is joined from two MIMIC-IV-Echo CSV files so each row is self-contained:
+- **echo-record-list.csv** (525K rows) — per-video: subject, study, acquisition time
+- **echo-study-list.csv** (7K rows) — per-study: study time, clinical notes
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("parquet", data_dir="jepa-l-embeddings/")
+print(ds["train"][0])  # {'subject_id': 10002221, 'embedding': [...], ...}
+```
+
+```python
+import pyarrow.parquet as pq
+
+table = pq.read_table("jepa-l-embeddings/")
+print(table.num_rows)   # 525328
+print(table.schema)
+```
+
+## Extraction
+
+Embeddings were extracted using the pipeline in [EchoJEPA-VE/scripts/extract-embeddings/](https://github.com/your-org/EchoJEPA-VE/tree/main/scripts/extract-embeddings).
+
+| Step | Command |
+|------|---------|
+| Extract (SLURM) | `sbatch extract_slurm.sh vitl` |
+| Merge .pt files | `python merge_embeddings.py --model vitl` |
+| Convert to Parquet | `python to_parquet.py --model vitl` |
+
+Config: L40S GPU, batch=256, 8 DataLoader workers, ~30 min per folder.