Fix source attribution (Kaggle), clarify FOL translation as key preprocessing step, remove script reference

README.md

@@ -19,9 +19,14 @@ size_categories:
 
 # 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.
+A preprocessed and vocabulary-augmented dataset derived from the [ProofWriter (Kaggle)](https://www.kaggle.com/datasets/mathurinache/proofwriter) OWA splits, built for training a Natural Language → First-Order Logic translation model.
 
-## What's different from ProofWriter
+The source dataset contains natural-language premises and questions in English along with structured proof metadata. Our preprocessing adds two things that the original does not provide:
+
+1. **FOL translations** — each natural-language statement is converted to First-Order Logic via a rule-based translator (100% coverage). ProofWriter's NL maps deterministically to FOL (e.g. "Anne is kind" → `Kind(anne)`, "If someone is kind then they are furry" → `forall x (Kind(x) -> Furry(x))`). Proof chains and Unknown failure traces are also converted to FOL form.
+2. **Vocabulary substitution** — entity and predicate names are replaced per-question with random draws from large NLTK/WordNet pools, forcing models to learn structural FOL mapping rather than surface name memorisation.
+
+## Vocabulary substitution
 
 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:
 
@@ -29,7 +34,7 @@ The original ProofWriter uses a small fixed vocabulary (~75 entity names, ~80 pr
 - **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.
+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).
 
 ## Dataset Structure
 
@@ -101,8 +106,8 @@ Unknown
 
 ## 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).
+Built from ProofWriter OWA depth-2, depth-3, and depth-3ext splits, sourced from [Kaggle (mathurinache/proofwriter)](https://www.kaggle.com/datasets/mathurinache/proofwriter).
 
 ## License
 
-Apache 2.0. Original ProofWriter data © Allen Institute for AI (AI2), released under Apache 2.0.
+Apache 2.0. Original ProofWriter data by Allen Institute for AI (AI2), released under Apache 2.0.