Add dataset card

README.md

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+---
+license: apache-2.0
+language:
+- en
+task_categories:
+- text2text-generation
+- question-answering
+tags:
+- logical-reasoning
+- first-order-logic
+- proofwriter
+- symbolic-reasoning
+- natural-language-inference
+pretty_name: FOL Reasoning Dataset (Vocabulary-Augmented ProofWriter)
+size_categories:
+- 100K<n<1M
+---
+
+# FOL Reasoning Dataset
+
+A vocabulary-augmented version of the [ProofWriter](https://allenai.org/data/proofwriter) dataset (OWA splits), preprocessed for training a Natural Language → First-Order Logic translation model.
+
+## What's different from ProofWriter
+
+The original ProofWriter uses a small fixed vocabulary (~75 entity names, ~80 properties). This dataset replaces every entity and predicate name per-question with a random draw from:
+
+- **7,372 entity names** — NLTK `names` corpus (first names, filtered to 3–9 chars, alpha only)
+- **13,006 property words** — WordNet adjective synset lemmas (4–10 chars, alpha only)
+- **7,463 relation words** — WordNet verb synset lemmas (4–10 chars, alpha only)
+
+All original ProofWriter vocabulary is excluded from the replacement pools. The substitution is consistent within each question (same entity always maps to the same replacement). This forces the model to learn structural FOL mapping rather than memorising surface vocabulary.
+
+## Dataset Structure
+
+Each split is a JSONL file. One example per line:
+
+```json
+{
+  "premises": "Venkat is perseverant. If someone is perseverant they discover.",
+  "logic": "<extra_id_1>\nPerseverant(venkat)\nforall x (Perseverant(x) -> Discover(x))\n<extra_id_2>\nDiscover(venkat)\n<extra_id_3>\nPerseverant(venkat) and forall x (Perseverant(x) -> Discover(x)) -> therefore Discover(venkat)\n<extra_id_4>\nTrue",
+  "qdep": 1,
+  "answer": "True",
+  "source": "depth-2/meta-train-1234"
+}
+```
+
+### Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `premises` | string | Natural-language premises (facts + rules), vocabulary-substituted |
+| `logic` | string | Full decoder target: FOL premises → FOL question → proof chain → answer |
+| `qdep` | int | Question depth (0–5): minimum reasoning steps to answer |
+| `answer` | string | Ground truth: `"True"`, `"False"`, or `"Unknown"` |
+| `source` | string | Original ProofWriter example ID |
+
+### `logic` field sentinel structure
+
+```
+<extra_id_1>          ← start of FOL premises block
+Kind(anne)
+forall x (Kind(x) -> Furry(x))
+<extra_id_2>          ← start of FOL question
+Furry(anne)
+<extra_id_3>          ← start of proof chain
+Kind(anne) and forall x (Kind(x) -> Furry(x)) -> therefore Furry(anne)
+<extra_id_4>          ← answer token
+True
+```
+
+For `Unknown` examples, the proof is a failure chain:
+```
+<extra_id_3>
+forall x (Big(x) and Round(x) -> White(x)) <- Rough(fiona) -> Big(fiona) <- [no base fact]
+Cannot be determined from given premises.
+<extra_id_4>
+Unknown
+```
+
+## Splits
+
+| Split | Examples | File size |
+|-------|----------|-----------|
+| train | 229,832 | ~302 MB |
+| dev | 33,042 | ~45 MB |
+| test | 66,084 | ~88 MB |
+
+### Class distribution (train)
+
+| Class | Count | % |
+|-------|-------|---|
+| pos_True (non-negated → True) | 58,034 | 25.3% |
+| neg_False (negated → False) | 57,984 | 25.2% |
+| pos_Unknown | 51,808 | 22.5% |
+| neg_Unknown | 51,808 | 22.5% |
+| pos_False (non-negated → False) | 5,124 | 2.2% |
+| neg_True (negated → True) | 5,074 | 2.2% |
+
+`pos_False` and `neg_True` are rare (underrepresented ~11×) — training uses a weighted sampler to compensate.
+
+## Source
+
+Built from ProofWriter OWA depth-2, depth-3, and depth-3ext splits. Preprocessing script: `scripts/preprocess_proofwriter.py` in the [FOL SLM repository](https://huggingface.co/Venkatdatta/fol-slm).
+
+## License
+
+Apache 2.0. Original ProofWriter data © Allen Institute for AI (AI2), released under Apache 2.0.