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

from datasets import load_dataset

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

Load only the common columns:

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:

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:

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.