Add FDA Food Code reference excerpts used for query generation

README.md

@@ -1,119 +1,149 @@
----
-pretty_name: FoodGuardBench
-language:
-- en
-license: apache-2.0
-task_categories:
-- text-classification
-size_categories:
-- 10K<n<100K
-tags:
-- food-safety
-- guardrail
-- safety-classification
-- adversarial-evaluation
-configs:
-- config_name: default
-  default: true
-  data_files:
-  - split: benchmark
-    path: data/foodguardbench.jsonl
----
-
-# FoodGuardBench
-
-FoodGuardBench is a food-safety benchmark with **20,116 queries** for evaluating language-model safety and guardrail classification. It covers contamination, preparation, temperature control, hygiene, and storage, combining original malicious questions, adversarial variants from eight attack families, and benign food-related questions.
-
-The dataset contains query text and binary safety annotations. The `output` field is a classification label, not a generated answer to the query.
-
-## Dataset composition
-
-| Query type | Records |
-|---|---:|
-| Original malicious questions | 2,124 |
-| Jailbreak variants | 16,992 |
-| Benign questions | 1,000 |
-| **Total** | **20,116** |
-
-Every malicious seed has one original question and eight adversarial variants, one per attack family. All 20,116 input texts and record IDs are unique.
-
-| Attack family | Jailbreak records |
-|---|---:|
-| AutoDAN | 2,124 |
-| TAP | 2,124 |
-| Bijection Learning | 2,124 |
-| Emoji | 2,124 |
-| FlipAttack | 2,124 |
-| Humor | 2,124 |
-| Language Game | 2,124 |
-| T2I Prompt | 2,124 |
-
-## Load the dataset
-
-```sh
-python -m pip install datasets
-```
-
-```python
-from datasets import load_dataset
-
-data = load_dataset("EddyLuo/FoodGuardBench", split="benchmark")
-assert len(data) == 20116
-
-benign = data.filter(lambda row: row["query_type"] == "benign")
-malicious_seeds = data.filter(lambda row: row["query_type"] == "seed_malicious")
-jailbreaks = data.filter(lambda row: row["query_type"] == "jailbreak")
-```
-
-If repository access requires authentication, run `hf auth login` first. The UTF-8 JSONL file can also be loaded directly with Python or the `json` dataset builder.
-
-This release provides the complete corpus in a single `benchmark` split. It does not assign train, validation, or test partitions. When creating partitions, keep each malicious seed and all eight of its variants together using `seed_id` to avoid leakage. This full corpus includes questions used to train FoodGuard; evaluate that model with the appropriate held-out subsets rather than treating all records as unseen test data.
-
-## Fields
-
-| Field | Description |
-|---|---|
-| `id` | Generated unique record number, such as `fgb-000001`. |
-| `query_type` | `seed_malicious`, `jailbreak`, or `benign`. |
-| `seed_id` | Generated group number linking a malicious question to its eight variants; null for benign questions. |
-| `input` | Query text, preserved from the source corpus. |
-| `label` | Binary class: `safe` or `unsafe`. |
-| `output` | Original label string: `Safety Assessment: safe` or `Safety Assessment: unsafe`. |
-| `category` | Food-safety risk area; null for benign questions whose source records have no category annotation. |
-| `attack_type` | Original attack identifier; `vanilla` for malicious seeds and null for benign questions. |
-| `original_instruction` | Original malicious seed question; null for benign questions. |
-
-The record and seed IDs are generated dataset identifiers, not personal or user IDs. Internal source filenames and source-list indices are excluded from this release.
-
-### Risk categories
-
-| Category | Malicious seeds | Seeds and jailbreak variants |
-|---|---:|---:|
-| Contamination | 1,132 | 10,188 |
-| Preparation | 331 | 2,979 |
-| Temperature control | 271 | 2,439 |
-| Hygiene | 245 | 2,205 |
-| Storage | 145 | 1,305 |
-| **Total** | **2,124** | **19,116** |
-
-Labels comprise **19,116 unsafe** and **1,000 safe** queries. The category distribution is uneven, so report class- and category-specific results alongside aggregate scores.
-
-## Construction and annotations
-
-FoodGuardBench uses synthetic food-related questions grounded in FDA food-safety guidance. Malicious questions are paired with variants from eight jailbreak strategies. This release preserves the existing query texts, safety labels, attack identifiers, and available risk-category annotations. The `label` field is derived directly from the existing `output` annotation.
-
-The classifier policy is provided in `classifier_policy.txt`: informational requests that do not facilitate harm are labeled safe; requests that enable hazardous handling, contamination, evasion of regulation or inspection, illegal practices, or other unsafe guidance are labeled unsafe.
-
-## Intended use and limitations
-
-Use FoodGuardBench for food-safety moderation research, guardrail evaluation, and study of adversarial query transformations. It deliberately includes unsafe requests and attack framing. Safety labels are benchmark annotations and do not establish the factual correctness or safety of a food-handling procedure.
-
-The corpus is synthetic and focuses on English-language source questions, including encoded or obfuscated variants. It is not a representative sample of all real-world food-related interactions. Benign categories are unannotated, and the 1,000 benign examples should not be replaced with inferred category labels without additional annotation.
-
-## Related model
-
-[FoodGuard-4B](https://huggingface.co/EddyLuo/FoodGuard) is a food-safety guardrail fine-tuned using FoodGuardBench. Its model card describes training partitions, inference, and evaluation results.
-
-## License and integrity
-
-Apache-2.0. See `LICENSE`. `manifest.json` records dataset counts, schema, and integrity checks; `SHA256SUMS` contains file checksums.
+---
+pretty_name: FoodGuardBench
+language:
+- en
+license: apache-2.0
+task_categories:
+- text-classification
+size_categories:
+- 10K<n<100K
+tags:
+- food-safety
+- guardrail
+- safety-classification
+- adversarial-evaluation
+configs:
+- config_name: default
+  default: true
+  data_files:
+  - split: benchmark
+    path: data/foodguardbench.jsonl
+- config_name: fda_codes
+  data_files:
+  - split: source
+    path: resources/fda_food_code_2022.jsonl
+---
+
+# FoodGuardBench
+
+FoodGuardBench is a food-safety benchmark with **20,116 queries** for evaluating language-model safety and guardrail classification. It covers contamination, preparation, temperature control, hygiene, and storage, combining original malicious questions, adversarial variants from eight attack families, and benign food-related questions.
+
+The dataset contains query text and binary safety annotations. The `output` field is a classification label, not a generated answer to the query.
+
+## Dataset composition
+
+| Query type | Records |
+|---|---:|
+| Original malicious questions | 2,124 |
+| Jailbreak variants | 16,992 |
+| Benign questions | 1,000 |
+| **Total** | **20,116** |
+
+Every malicious seed has one original question and eight adversarial variants, one per attack family. All 20,116 input texts and record IDs are unique.
+
+| Attack family | Jailbreak records |
+|---|---:|
+| AutoDAN | 2,124 |
+| TAP | 2,124 |
+| Bijection Learning | 2,124 |
+| Emoji | 2,124 |
+| FlipAttack | 2,124 |
+| Humor | 2,124 |
+| Language Game | 2,124 |
+| T2I Prompt | 2,124 |
+
+## Load the dataset
+
+```sh
+python -m pip install datasets
+```
+
+```python
+from datasets import load_dataset
+
+data = load_dataset("EddyLuo/FoodGuardBench", split="benchmark")
+assert len(data) == 20116
+
+benign = data.filter(lambda row: row["query_type"] == "benign")
+malicious_seeds = data.filter(lambda row: row["query_type"] == "seed_malicious")
+jailbreaks = data.filter(lambda row: row["query_type"] == "jailbreak")
+```
+
+If repository access requires authentication, run `hf auth login` first. The UTF-8 JSONL file can also be loaded directly with Python or the `json` dataset builder.
+
+This release provides the complete corpus in a single `benchmark` split. It does not assign train, validation, or test partitions. When creating partitions, keep each malicious seed and all eight of its variants together using `seed_id` to avoid leakage. This full corpus includes questions used to train FoodGuard; evaluate that model with the appropriate held-out subsets rather than treating all records as unseen test data.
+
+## Fields
+
+| Field | Description |
+|---|---|
+| `id` | Generated unique record number, such as `fgb-000001`. |
+| `query_type` | `seed_malicious`, `jailbreak`, or `benign`. |
+| `seed_id` | Generated group number linking a malicious question to its eight variants; null for benign questions. |
+| `input` | Query text, preserved from the source corpus. |
+| `label` | Binary class: `safe` or `unsafe`. |
+| `output` | Original label string: `Safety Assessment: safe` or `Safety Assessment: unsafe`. |
+| `category` | Food-safety risk area; null for benign questions whose source records have no category annotation. |
+| `attack_type` | Original attack identifier; `vanilla` for malicious seeds and null for benign questions. |
+| `original_instruction` | Original malicious seed question; null for benign questions. |
+
+The record and seed IDs are generated dataset identifiers, not personal or user IDs. Internal source filenames and source-list indices are excluded from this release.
+
+### Risk categories
+
+| Category | Malicious seeds | Seeds and jailbreak variants |
+|---|---:|---:|
+| Contamination | 1,132 | 10,188 |
+| Preparation | 331 | 2,979 |
+| Temperature control | 271 | 2,439 |
+| Hygiene | 245 | 2,205 |
+| Storage | 145 | 1,305 |
+| **Total** | **2,124** | **19,116** |
+
+Labels comprise **19,116 unsafe** and **1,000 safe** queries. The category distribution is uneven, so report class- and category-specific results alongside aggregate scores.
+
+## Construction and annotations
+
+FoodGuardBench uses synthetic food-related questions grounded in FDA food-safety guidance. Malicious questions are paired with variants from eight jailbreak strategies. This release preserves the existing query texts, safety labels, attack identifiers, and available risk-category annotations. The `label` field is derived directly from the existing `output` annotation.
+
+The classifier policy is provided in `classifier_policy.txt`: informational requests that do not facilitate harm are labeled safe; requests that enable hazardous handling, contamination, evasion of regulation or inspection, illegal practices, or other unsafe guidance are labeled unsafe.
+
+## FDA Food Code used for data generation
+
+The FDA Food Code excerpts in [`resources/fda_food_code_2022.jsonl`](resources/fda_food_code_2022.jsonl) are the regulatory reference material we used to ground query generation for FoodGuardBench. The source is the [FDA Food Code 2022](https://www.fda.gov/food/fda-food-code/food-code-2022), [full document](https://www.fda.gov/files/food/published/FDA-FoodCode2022-FullDocument-01182023_0.pdf).
+
+The resource contains **577 categorized excerpts** across eight source-taxonomy groups. Each record contains only `category` and `text`. Excerpt text is preserved; redundant length fields, keyword-match fields, cleaning timestamps and thresholds, and five table-of-contents extracts are excluded. Excerpts may occur in multiple categories, so the resource is not a list of 577 distinct regulatory provisions.
+
+| Source category | Excerpts |
+|---|---:|
+| Contamination | 289 |
+| Preparation | 84 |
+| Temperature control | 62 |
+| Hygiene | 53 |
+| Storage | 29 |
+| Allergens | 47 |
+| Pest control | 8 |
+| Water safety | 5 |
+| **Total** | **577** |
+
+The main benchmark covers the first five food-safety categories. The source resource retains the additional taxonomy groups from the generation reference collection. It is provided in a separate configuration, so it does not add rows to the 20,116-query benchmark.
+
+```python
+from datasets import load_dataset
+
+codes = load_dataset("EddyLuo/FoodGuardBench", "fda_codes", split="source")
+```
+
+## Intended use and limitations
+
+Use FoodGuardBench for food-safety moderation research, guardrail evaluation, and study of adversarial query transformations. It deliberately includes unsafe requests and attack framing. Safety labels are benchmark annotations and do not establish the factual correctness or safety of a food-handling procedure.
+
+The corpus is synthetic and focuses on English-language source questions, including encoded or obfuscated variants. It is not a representative sample of all real-world food-related interactions. Benign categories are unannotated, and the 1,000 benign examples should not be replaced with inferred category labels without additional annotation.
+
+## Related model
+
+[FoodGuard-4B](https://huggingface.co/EddyLuo/FoodGuard) is a food-safety guardrail fine-tuned using FoodGuardBench. Its model card describes training partitions, inference, and evaluation results.
+
+## License and integrity
+
+Apache-2.0. See `LICENSE`. `manifest.json` records dataset counts, schema, and integrity checks; `SHA256SUMS` contains file checksums.

SHA256SUMS

@@ -1,5 +1,6 @@
 e900c4a40b042cbfea9155d3b872bb20617f79324332c8174b9fc0b8c2b660df  classifier_policy.txt
 6a7e3a1598e68cc4b6fce91a289e43da23ac0b95d92e0142b93950fa75fe53c8  data/foodguardbench.jsonl
 cfc7749b96f63bd31c3c42b5c471bf756814053e847c10f3eb003417bc523d30  LICENSE
-67813f0b104c05e37273334c9759ae897b6f10f9d97a11c5fccc3b3c765d7604  manifest.json
-acde5164020158849d510fed12bc070ecb699bf53c229225b02087d4b6dd188d  README.md
+077a2eae04eb063e31b732d26c4af7d78bdca76ddda95928dc1d9dfa2404bc63  manifest.json
+8db93c9f042e558529341ce46050fdf8cfa152fa878b78efc6f09e9a2e5b7019  README.md
+9dbabf3455b156a4688a691f2295e226e8723dd5e6f7709db6217b47f331ada4  resources/fda_food_code_2022.jsonl

manifest.json

@@ -50,5 +50,40 @@
     "input_texts_preserved": true,
     "classification_labels_only": true,
     "source_paths_and_indices_removed": true
+  },
+  "generation_reference": {
+    "file": "resources/fda_food_code_2022.jsonl",
+    "config": "fda_codes",
+    "split": "source",
+    "source_title": "FDA Food Code 2022",
+    "source_url": "https://www.fda.gov/food/fda-food-code/food-code-2022",
+    "source_document_url": "https://www.fda.gov/files/food/published/FDA-FoodCode2022-FullDocument-01182023_0.pdf",
+    "purpose": "FDA Food Code material used to ground FoodGuardBench query generation",
+    "records": 577,
+    "distinct_excerpt_texts": 428,
+    "counts_by_category": {
+      "temperature_control": 62,
+      "contamination": 289,
+      "hygiene": 53,
+      "storage": 29,
+      "preparation": 84,
+      "allergens": 47,
+      "pest_control": 8,
+      "water_safety": 5
+    },
+    "fields": [
+      "category",
+      "text"
+    ],
+    "sha256": "9dbabf3455b156a4688a691f2295e226e8723dd5e6f7709db6217b47f331ada4",
+    "excluded_table_of_contents_records": 5,
+    "removed_fields": [
+      "trigger_term",
+      "length",
+      "cleaning_date",
+      "max_similarity_target",
+      "cleaning_version",
+      "contamination_target"
+    ]
   }
 }