README.md

---
pretty_name: ImmunoGraph
tags:
- biology
- knowledge-graph
- question-answering
configs:
- config_name: qa_easy
  default: true
  data_files:
  - split: data
    path: 1-QA_DATASETS/Easy/easy_final_50000.parquet
- config_name: qa_medium
  data_files:
  - split: data
    path: 1-QA_DATASETS/Medium/medium_final20k_corrected.parquet
- config_name: qa_hard_context_swap
  data_files:
  - split: data
    path: 1-QA_DATASETS/Hard/hard_context_swap_19985.parquet
- config_name: qa_hard_gold_context_no_advantage
  data_files:
  - split: data
    path: 1-QA_DATASETS/Hard/hard_gold_context_no_advantage_3000.parquet
- config_name: qa_hard_structural_loss
  data_files:
  - split: data
    path: 1-QA_DATASETS/Hard/hard_structural_loss_10000.parquet
- config_name: immunokg
  data_files:
  - split: data
    path: ImmunoKG/*.parquet
---

# ImmunoGraph

This repository contains the ImmunoGraph QA datasets, ImmunoKG knowledge graph, answer-validation resources, and evaluation dictionaries. The original directory structure is preserved. Data files use the Parquet format with ZSTD compression.

| Collection | Parquet files | Records |
|---|---:|---:|
| QA: Easy | 1 | 50,000 |
| QA: Medium | 1 | 20,000 |
| QA: Hard, context swap | 1 | 19,985 |
| QA: Hard, gold context no advantage | 1 | 3,000 |
| QA: Hard, structural loss | 1 | 10,000 |
| **All QA datasets** | **5** | **102,985** |
| **ImmunoKG** | **48** | **100,294,641** |
| **Evaluation dictionaries** | **10** | **554,100** |

The four directories contain 79 files, including five provenance/audit reports in `ImmunoKG/_reports/`, totaling 1,780,151,970 bytes (approximately 1.78 GB). Together with the three root-level conversion documents, the uploaded inventory contains 82 files and 1,780,223,937 bytes; the dataset card is additional.

## Download / 下载

Install `huggingface_hub` to use the `hf` command. For a private repository, authenticate with a token that has read access:

```bash
HF_ENDPOINT=https://huggingface.co hf auth login
```

Download the four complete directories and conversion documents while preserving their paths:

```bash
HF_ENDPOINT=https://huggingface.co hf download 1ricky1/ImmunoGraph \
  --repo-type dataset \
  --local-dir ./ImmunoGraph \
  --include "1-QA_DATASETS/**" "ImmunoKG/**" \
    "3-ANSWER_VALIDATION/**" "3-EVALUATION_DICTIONARIES/**" \
    "README-PARQUET.md" "PARQUET-CONVERSION-MANIFEST.json" "CONVERSION-SUMMARY.json"
```

Alternatively, use Python:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="1ricky1/ImmunoGraph",
    repo_type="dataset",
    endpoint="https://huggingface.co",
    local_dir="./ImmunoGraph",
    allow_patterns=[
        "1-QA_DATASETS/**", "ImmunoKG/**",
        "3-ANSWER_VALIDATION/**", "3-EVALUATION_DICTIONARIES/**",
        "README-PARQUET.md", "PARQUET-CONVERSION-MANIFEST.json", "CONVERSION-SUMMARY.json",
    ],
    token=True,
)
```

## Read the data / 读取数据

QA fields contain JSON-encoded text, including string, numeric, list and object values. Decode them with `json.loads`. For ImmunoKG, entity IDs, names, types and relations are plain text; `context` contains JSON text. The auxiliary `_source_keys` column records which fields were present in the source record, preserving the distinction between a missing field and a null value.

```python
import json
import pyarrow.parquet as pq

qa = pq.read_table(
    "ImmunoGraph/1-QA_DATASETS/Easy/easy_final_50000.parquet",
    columns=["question", "answer"],
)
question = json.loads(qa["question"][0].as_py())
answer = json.loads(qa["answer"][0].as_py())
```

The six Hub configurations keep the five QA schemas and the ImmunoKG schema separate. Each configuration exposes its complete collection as a single `data` split. If `HF_ENDPOINT` points to a mirror, set it to `https://huggingface.co` before starting Python:

```python
from datasets import load_dataset

kg = load_dataset(
    "1ricky1/ImmunoGraph",
    "immunokg",
    split="data",
    streaming=True,
    token=True,
)
first_record = next(iter(kg))
```

The preserved reports in `ImmunoKG/_reports/` describe provenance and earlier processing. Some refer to source `.json` filenames from before the Parquet conversion.

## Validation resources / 验证资源

- [3-ANSWER_VALIDATION/](https://huggingface.co/datasets/1ricky1/ImmunoGraph/tree/main/3-ANSWER_VALIDATION) contains the preserved answer-validation scripts, documentation, tests, and smoke-test report.
- [3-EVALUATION_DICTIONARIES/](https://huggingface.co/datasets/1ricky1/ImmunoGraph/tree/main/3-EVALUATION_DICTIONARIES) contains ten Parquet dictionaries and the preserved source manifests. Dictionary rows use JSON-encoded `key` and `value` columns, with `_source_keys` recording source fields.

The preserved validation scripts and their manifests describe the original JSON-based workflow. Their readers and paths need to be adapted for the numbered directories and Parquet files. The dictionary manifests record original JSON filenames and hashes; [PARQUET-CONVERSION-MANIFEST.json](https://huggingface.co/datasets/1ricky1/ImmunoGraph/blob/main/PARQUET-CONVERSION-MANIFEST.json) maps them to the converted files, and [CONVERSION-SUMMARY.json](https://huggingface.co/datasets/1ricky1/ImmunoGraph/blob/main/CONVERSION-SUMMARY.json) records conversion checks.

## Parquet conversion notes / Parquet 转换说明

以下完整保留 [README-PARQUET.md](https://huggingface.co/datasets/1ricky1/ImmunoGraph/blob/main/README-PARQUET.md) 的内容。这里的 122 个 Parquet 文件指完整的本地转换产物，包含模型回答等内容；当前 Hugging Face 仓库包含 63 个 Parquet 文件（5 个问答、48 个知识图谱、10 个字典），以及验证代码、报告和说明文档。转换清单也覆盖完整的本地转换产物。

# ImmonoGraph

本目录由 `/data/KG/FFFFINAL` 转换生成，保留原来的题目、模型回答、字典、代码与验证目录结构。

- 共 122 个 Parquet 文件，使用 ZSTD 压缩。
- ImmunoKG 为 48 个 Parquet 文件，共 100,294,641 条记录。
- 问答、模型回答和实体字典均已转换。代码、配置、来源记录和说明保持原格式。
- `gene_dictionary.json` 中 10 处非法 `NaN` 已按要求修正为 `null`，对应字典已转换。
- 原 FFFFINAL 除上述授权的字典修正外，没有改动。

## 读取格式

KG 的头尾 ID、名称、类型、关系等为文本列，`context` 以完整 JSON 文本保存。问答/模型回答各字段采用 JSON 文本编码，可用 Python `json.loads` 或 DuckDB JSON 函数解码；这包含字符串、数值、列表和对象。字典对象采用 `key` / `value` 两列。

`_source_keys` 是辅助列，记录原始记录中存在的字段，区分“字段缺失”和“字段值为 null”，便于完整还原原 JSON。

例如，在 Python 中读取 KG 上下文：

```python
import duckdb, json
row = duckdb.sql("SELECT head_id, context FROM read_parquet('ImmunoKG/48-gene_clinicalconcept.parquet') LIMIT 1").fetchone()
head_id, context = row[0], json.loads(row[1])
```

原有测试代码和来源清单按原格式复制，其中指向 JSON 的读取路径需要调整后才能直接读取本目录的 Parquet；转换文件对应关系以根目录的 `PARQUET-CONVERSION-MANIFEST.json` 为准。

## 校验

每个转换分块都从 Parquet 读回，并逐条计算规范化内容 SHA256，与原记录比较；最终合并文件再次核对记录数和所有字段的行校验值。原格式文件与源文件 SHA256 一致。详见 `CONVERSION-SUMMARY.json` 和转换清单。