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data/s5_data/README.md CHANGED
@@ -4,93 +4,37 @@ size_categories:
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  - small
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  - medium
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  - large
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- - xlarge
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- - xxlarge
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  tags:
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  - mathematics
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  - group-theory
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  - permutations
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  - sequence-to-sequence
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  - benchmark
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- - generated
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  task_categories:
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  - text-generation
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  - sequence-modeling
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- annotations_creators:
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- - no-annotations
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- language_creators:
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- - other
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- language:
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- - en
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- licenses:
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- - mit
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- ---
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  # Permutation Composition Dataset for S5
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- This dataset contains sequences of permutation IDs and their compositions, designed for benchmarking sequence-to-sequence models on group theory tasks.
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-
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- ## Dataset Structure
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-
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- The dataset is split into `train` and `test` sets. Each sample in the dataset has the following features:
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-
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- - `input_sequence`: A space-separated string of integer IDs representing the sequence of permutations to be composed.
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- - `target`: An integer ID representing the composition of the `input_sequence` permutations.
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-
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- ## Group Details
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-
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- - **Group Name**: S5
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- - **Group Type**: Symmetric Group
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- - **Degree**: 5 (permutations act on 5 elements)
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- - **Order**: 120 (total number of elements in the group)
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- ## Data Generation
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-
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- This dataset was generated using the `s5-data-gen` script. The generation process involves:
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-
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- 1. Generating all unique permutations for the specified group.
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- 2. Mapping each unique permutation to a unique integer ID.
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- 3. Randomly sampling sequences of these permutation IDs.
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- 4. Composing the permutations in the sequence (from right to left: `p_n o ... o p_2 o p_1`).
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- 5. Mapping the resulting composed permutation to its integer ID as the target.
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-
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- ### Generation Parameters:
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-
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- - **Total Samples**: 100000
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- - **Minimum Sequence Length**: 3
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- - **Maximum Sequence Length**: 512
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- - **Test Split Size**: 0.2
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  ## Dataset Statistics
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-
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- - **Train Samples**: 80000
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- - **Test Samples**: 20000
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-
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- ## Permutation Mapping
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-
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- 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.
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-
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- Example of `metadata.json` content:
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-
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- ```json
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- {
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- "0": "[0, 1, 2, 3, 4]",
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- "1": "[0, 1, 3, 2, 4]",
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- "2": "[0, 1, 4, 3, 2]",
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- "3": "[0, 2, 1, 3, 4]",
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- "4": "[0, 2, 3, 1, 4]"
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- // ... and so on for all 120 permutations
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- }
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- ```
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  ## Usage
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- You can load this dataset using the Hugging Face `datasets` library:
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-
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  ```python
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  from datasets import load_dataset
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- import json
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- from huggingface_hub import hf_hub_download
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  # Load the dataset
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  dataset = load_dataset("BeeGass/permutation-groups")
@@ -99,22 +43,6 @@ dataset = load_dataset("BeeGass/permutation-groups")
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  metadata_path = hf_hub_download(repo_id="BeeGass/permutation-groups", filename="metadata.json")
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  with open(metadata_path, "r") as f:
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  id_to_perm_map = json.load(f)
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-
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- # Example: Decode a sample
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- first_train_sample = dataset["train"][0]
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- input_ids = [int(x) for x in first_train_sample["input_sequence"].split(" ")]
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- target_id = int(first_train_sample["target"])
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-
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- print(f"Input sequence IDs: {input_ids}")
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- print(f"Target ID: {target_id}")
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-
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- # Convert IDs back to permutations (example for the first input permutation)
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- # Note: SymPy Permutation expects a list of integers, not a string representation
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- # You would need to parse the string representation from id_to_perm_map
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- # For example: eval(id_to_perm_map[str(input_ids[0])])
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-
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- print(f"First input permutation (array form): {id_to_perm_map[str(input_ids[0])]}")
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- print(f"Target permutation (array form): {id_to_perm_map[str(target_id)]}")
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  ```
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  ## License
 
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  - small
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  - medium
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  - large
 
 
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  tags:
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  - mathematics
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  - group-theory
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  - permutations
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  - sequence-to-sequence
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  - benchmark
 
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  task_categories:
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  - text-generation
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  - sequence-modeling
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+ ---
 
 
 
 
 
 
 
 
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  # Permutation Composition Dataset for S5
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+ This dataset contains 50000 samples of permutation composition problems for the group S5.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Group Information
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+ - **Group**: S5
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+ - **Order**: 120
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+ - **Degree**: 5
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Dataset Statistics
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+ - **Total samples**: 50000
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+ - **Train samples**: 40000
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+ - **Test samples**: 10000
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+ - **Min sequence length**: 3
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+ - **Max sequence length**: 512
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Usage
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36
  ```python
37
  from datasets import load_dataset
 
 
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  # Load the dataset
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  dataset = load_dataset("BeeGass/permutation-groups")
 
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  metadata_path = hf_hub_download(repo_id="BeeGass/permutation-groups", filename="metadata.json")
44
  with open(metadata_path, "r") as f:
45
  id_to_perm_map = json.load(f)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  ## License
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  "input_sequence": {
6
  "dtype": "string",
 
11
  "_type": "Value"
12
  }
13
  },
14
+ "homepage": "",
15
+ "license": ""
16
  }
data/s5_data/train/state.json CHANGED
@@ -4,7 +4,7 @@
4
  "filename": "data-00000-of-00001.arrow"
5
  }
6
  ],
7
- "_fingerprint": "f3666f82c68c3e9f",
8
  "_format_columns": null,
9
  "_format_kwargs": {},
10
  "_format_type": null,
 
4
  "filename": "data-00000-of-00001.arrow"
5
  }
6
  ],
7
+ "_fingerprint": "e37c1309f51188c7",
8
  "_format_columns": null,
9
  "_format_kwargs": {},
10
  "_format_type": null,