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README.md

@@ -1,34 +1,91 @@
----
-dataset_info:
-  features:
-  - name: input
-    sequence: string
-  - name: output
-    dtype: string
-  splits:
-  - name: train_small
-    num_bytes: 17574
-    num_examples: 100
-  - name: test_small
-    num_bytes: 17799
-    num_examples: 100
-  - name: train_large
-    num_bytes: 172002
-    num_examples: 100
-  - name: test_large
-    num_bytes: 171585
-    num_examples: 100
-  download_size: 155156
-  dataset_size: 378960
-configs:
-- config_name: default
-  data_files:
-  - split: train_small
-    path: data/train_small-*
-  - split: test_small
-    path: data/test_small-*
-  - split: train_large
-    path: data/train_large-*
-  - split: test_large
-    path: data/test_large-*
----
+
+# Arithmetic Puzzles Dataset
+
+A collection of arithmetic puzzles with heavy use of variable assignment. Current LLMs struggle with variable indirection/multi-hop reasoning, this should be a tough test for them.
+
+Inputs are a list of strings representing variable assignments (`c=a+b`), and the output is the integer answer.
+
+Outputs are filtered to be between [-100, 100], and self-reference/looped dependencies are forbidden.
+
+Splits include:
+
+- `train_small/test_small` which includes 10k total examples of puzzles with up to 10 variables.
+- `train_large/test_large` which includes 10k total examples of puzzles with up to 100 variables.
+
+Conceptually the data looks like this:
+
+```python
+Input:
+
+  a=1
+  b=2
+  c=a+b
+  solve(c)=
+
+Output:
+  3
+```
+
+In actuality it looks like this:
+
+```python
+{
+    "input": ['var_0=1', 'var_1=2', 'var_2=a+b', 'solve(var_2)='],
+    "output": 3
+}
+```
+
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset
+dataset = load_dataset("neurallambda/arithmetic_puzzles")
+
+# Load specific splits
+train_small = load_dataset("neurallambda/arithmetic_puzzles", split="train_small")
+test_small = load_dataset("neurallambda/arithmetic_puzzles", split="test_small")
+train_large = load_dataset("neurallambda/arithmetic_puzzles", split="train_large")
+test_large = load_dataset("neurallambda/arithmetic_puzzles", split="test_large")
+```
+
+### Preparing Inputs
+
+To prepare the inputs as concatenated strings, you can do this:
+
+```python
+def prepare_input(example):
+    return {
+        "input_text": "
+".join(example["input"]),
+        "output": example["output"]
+    }
+
+# Apply the preparation to a specific split
+train_small_prepared = train_small.map(prepare_input)
+
+# Example of using the prepared dataset
+for example in train_small_prepared.select(range(5)):  # Show first 5 examples
+    print("Input:", example["input_text"])
+    print("Output:", example["output"])
+    print()
+```
+
+This will produce output similar to:
+
+```
+Input: var_0=5
+var_1=2
+var_2=-2 + -8
+var_3=3
+var_4=4
+var_5=var_2
+var_6=var_3 * 10
+var_7=var_2 - var_0
+var_8=var_1
+var_9=-2 - 9
+solve(var_3)=
+Output: 3
+```