---
pretty_name: FLIP2 Protein Benchmark Tasks
license: other
tags:
- biology
- protein
- benchmark
- protein-engineering
- flip
- flip2
- parquet
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*.parquet
  - split: test
    path: data/test-*.parquet
---

# LiteFold/FLIP2

This repository now includes a Dataset Viewer-friendly Parquet version of the LiteFold FLIP2 tables. The default `load_dataset()` configuration reads the normalized Parquet files in `data/`.

The normalized table contains 890,356 rows from 16 source tables. The original wrapped JSONL source tables remain available in the repository under `tables/`.

## Splits

- `train`: 801,710 rows
- `test`: 88,646 rows

Rows are assigned with a deterministic hash split: `sha256(record_id) % 10`, where bucket `0` is test and buckets `1-9` are train.

## Columns

The table includes common browsing and modeling columns:

- `record_id`: stable SHA-256 row identifier
- `dataset_id`, `source_file`, `source_table`, `source_row_index`: source provenance
- `task_name`, `subtask_name`, `assay_name`: FLIP2 task metadata
- `sequence`, `sequence_length`, `target`, `score_value`, `label`: normalized convenience fields
- `split_bucket`: deterministic split bucket

The original FLIP2 fields are preserved as snake_case string columns: `raw_sequence`, `set`, `raw_target`, and `validation`. See `metadata/column_mapping.parquet` for the original field-name mapping and `metadata/source_tables.parquet` for per-table row counts.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("LiteFold/FLIP2")
print(ds)
print(ds["train"][0])
```

Load only the common columns:

```python
from datasets import load_dataset

cols = ["record_id", "task_name", "subtask_name", "sequence", "score_value", "label"]
train = load_dataset("LiteFold/FLIP2", split="train", columns=cols)
```

Filter to a task:

```python
from datasets import load_dataset

train = load_dataset("LiteFold/FLIP2", split="train")
trpb = train.filter(lambda row: row["task_name"] == "trpb")
```

Metadata tables can be loaded directly:

```python
from datasets import load_dataset

source_tables = load_dataset(
    "parquet",
    data_files="hf://datasets/LiteFold/FLIP2/metadata/source_tables.parquet",
    split="train",
)
column_mapping = load_dataset(
    "parquet",
    data_files="hf://datasets/LiteFold/FLIP2/metadata/column_mapping.parquet",
    split="train",
)
```

## Rebuild

The normalization script used for this upload is included at `scripts/prepare_wrapped_jsonl_dataset.py`.