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data/s3_data/README.md

@@ -4,93 +4,37 @@ size_categories:
   - small
   - medium
   - large
-  - xlarge
-  - xxlarge
 tags:
   - mathematics
   - group-theory
   - permutations
   - sequence-to-sequence
   - benchmark
-  - generated
 task_categories:
   - text-generation
   - sequence-modeling
-annotations_creators:
-  - no-annotations
-language_creators:
-  - other
-language:
-  - en
-licenses:
-  - mit
---- 
+---
 
 # Permutation Composition Dataset for S3
 
-This dataset contains sequences of permutation IDs and their compositions, designed for benchmarking sequence-to-sequence models on group theory tasks.
-
-## Dataset Structure
-
-The dataset is split into `train` and `test` sets. Each sample in the dataset has the following features:
-
-- `input_sequence`: A space-separated string of integer IDs representing the sequence of permutations to be composed.
-- `target`: An integer ID representing the composition of the `input_sequence` permutations.
-
-## Group Details
-
-- **Group Name**: S3
-- **Group Type**: Symmetric Group
-- **Degree**: 3 (permutations act on 3 elements)
-- **Order**: 6 (total number of elements in the group)
+This dataset contains 15000 samples of permutation composition problems for the group S3.
 
-## Data Generation
-
-This dataset was generated using the `s5-data-gen` script. The generation process involves:
-
-1.  Generating all unique permutations for the specified group.
-2.  Mapping each unique permutation to a unique integer ID.
-3.  Randomly sampling sequences of these permutation IDs.
-4.  Composing the permutations in the sequence (from right to left: `p_n o ... o p_2 o p_1`).
-5.  Mapping the resulting composed permutation to its integer ID as the target.
-
-### Generation Parameters:
-
-- **Total Samples**: 10000
-- **Minimum Sequence Length**: 3
-- **Maximum Sequence Length**: 512
-- **Test Split Size**: 0.2
+## Group Information
+- **Group**: S3
+- **Order**: 6
+- **Degree**: 3
 
 ## Dataset Statistics
-
-- **Train Samples**: 8000
-- **Test Samples**: 2000
-
-## Permutation Mapping
-
-The mapping from integer IDs to their corresponding permutation array forms is provided in the `metadata.json` file alongside the dataset. This file is crucial for interpreting the `input_sequence` and `target` IDs.
-
-Example of `metadata.json` content:
-
-```json
-{
-  "0": "[0, 1, 2, 3, 4]",
-  "1": "[0, 1, 3, 2, 4]",
-  "2": "[0, 1, 4, 3, 2]",
-  "3": "[0, 2, 1, 3, 4]",
-  "4": "[0, 2, 3, 1, 4]"
-  // ... and so on for all 6 permutations
-}
-```
+- **Total samples**: 15000
+- **Train samples**: 12000
+- **Test samples**: 3000
+- **Min sequence length**: 3
+- **Max sequence length**: 10
 
 ## Usage
 
-You can load this dataset using the Hugging Face `datasets` library:
-
 ```python
 from datasets import load_dataset
-import json
-from huggingface_hub import hf_hub_download
 
 # Load the dataset
 dataset = load_dataset("BeeGass/permutation-groups")
@@ -99,22 +43,6 @@ dataset = load_dataset("BeeGass/permutation-groups")
 metadata_path = hf_hub_download(repo_id="BeeGass/permutation-groups", filename="metadata.json")
 with open(metadata_path, "r") as f:
     id_to_perm_map = json.load(f)
-
-# Example: Decode a sample
-first_train_sample = dataset["train"][0]
-input_ids = [int(x) for x in first_train_sample["input_sequence"].split(" ")]
-target_id = int(first_train_sample["target"])
-
-print(f"Input sequence IDs: {input_ids}")
-print(f"Target ID: {target_id}")
-
-# Convert IDs back to permutations (example for the first input permutation)
-# Note: SymPy Permutation expects a list of integers, not a string representation
-# You would need to parse the string representation from id_to_perm_map
-# For example: eval(id_to_perm_map[str(input_ids[0])])
-
-print(f"First input permutation (array form): {id_to_perm_map[str(input_ids[0])]}")
-print(f"Target permutation (array form): {id_to_perm_map[str(target_id)]}")
 ```
 
 ## License

data/s3_data/metadata.json

@@ -1,8 +1,8 @@
 {
-    "0": "[0, 1, 2]",
-    "1": "[1, 2, 0]",
-    "2": "[2, 0, 1]",
-    "3": "[0, 2, 1]",
-    "4": "[1, 0, 2]",
-    "5": "[2, 1, 0]"
+  "0": "[0, 1, 2]",
+  "1": "[1, 2, 0]",
+  "2": "[2, 0, 1]",
+  "3": "[0, 2, 1]",
+  "4": "[1, 0, 2]",
+  "5": "[2, 1, 0]"
 }

data/s3_data/test/data-00000-of-00001.arrow

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data/s3_data/test/dataset_info.json

@@ -1,6 +1,6 @@
 {
   "citation": "",
-  "description": "Permutation composition benchmark for the S3 group.",
+  "description": "",
   "features": {
     "input_sequence": {
       "dtype": "string",
@@ -11,6 +11,6 @@
       "_type": "Value"
     }
   },
-  "homepage": "https://github.com/your-repo",
-  "license": "mit"
+  "homepage": "",
+  "license": ""
 }

data/s3_data/test/state.json

@@ -4,7 +4,7 @@
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     }
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data/s3_data/train/data-00000-of-00001.arrow

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data/s3_data/train/dataset_info.json

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 {
   "citation": "",
-  "description": "Permutation composition benchmark for the S3 group.",
+  "description": "",
   "features": {
     "input_sequence": {
       "dtype": "string",
@@ -11,6 +11,6 @@
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     }
   },
-  "homepage": "https://github.com/your-repo",
-  "license": "mit"
+  "homepage": "",
+  "license": ""
 }

data/s3_data/train/state.json

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