Add math_shepherd.py script for dataset processing

examples/datasets/math_shepherd.py

@@ -0,0 +1,130 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+from dataclasses import dataclass
+from itertools import chain
+from typing import Optional
+
+from datasets import load_dataset
+from transformers import HfArgumentParser
+
+
+@dataclass
+class ScriptArguments:
+    r"""
+    Arguments for the script.
+
+    Args:
+        push_to_hub (`bool`, *optional*, defaults to `False`):
+            Whether to push the dataset to the Hugging Face Hub.
+        repo_id (`str`, *optional*, defaults to `"trl-lib/math_shepherd"`):
+            Hugging Face repository ID to push the dataset to.
+        dataset_num_proc (`Optional[int]`, *optional*, defaults to `None`):
+            Number of workers to use for dataset processing.
+    """
+
+    push_to_hub: bool = False
+    repo_id: str = "trl-lib/math_shepherd"
+    dataset_num_proc: Optional[int] = None
+
+
+def process_example(example):
+    # Replace "ки" with "ⶻ" so that the size of the "input" matches the size of the "label"
+    inputs = example["input"].replace("ки", "ⶻ")
+
+    # Find the indices of the "ⶻ" characters (that should match with the indexes of the "+" or "-" in the label)
+    indexes = [m.start() for m in re.finditer("ⶻ", inputs)]
+
+    # Sanity that all indexes are either "+" or "-"
+    assert all(example["label"][idx] in ["+", "-"] for idx in indexes)
+
+    # Get the labels
+    labels = [example["label"][idx] == "+" for idx in indexes]
+
+    # Split the inputs into steps (caution, the first step is missing here, it is the prompt)
+    steps = [inputs[i:j] for i, j in zip(chain([0], indexes), chain(indexes, [None]))]
+
+    # Remove the last step (single ⶻ)
+    steps = steps[:-1]
+
+    # Get the prompt (first part) and completions (rest)
+    prompt = steps[0]
+    completions = steps[1:]
+
+    # Remove the heading "ⶻ" and the final whitespace from the completions
+    assert all(completion.startswith("ⶻ") for completion in completions)
+    completions = [completion[1:].strip() for completion in completions]
+
+    # At this point, we need to retrieve the first step from the prompt.
+    # First, we handle particular cases (annotation error) where we have a first label before the end of the prompt.
+    if prompt.startswith(
+        (
+            "Mr. Rocky",
+            "Parker",
+            "What is the smallest positive",
+            " The Myth",
+            "Let $\\mathbf{a}$",
+            "Find the arithmetic",
+            "Determine an ordered pair",
+            "Determine the ordered pair",
+            "At the Quill and Scroll stationery",
+            "Round to the nearest",
+            r"Calculate $\sqrt{10p}",
+            r"Simplify $\sqrt{28x}",
+        )
+    ):
+        # Some spotted datasets errors where there is an annotation in the prompt: we remove it
+        labels = labels[1:]
+
+    # Then we handle the general case: we get the first step from the prompt by looking for "Step 1:" or "step 1:" or
+    # (less common) "?".
+    elif "Step 1:" in prompt:
+        prompt, first_step = prompt.split("Step 1:")
+        first_step = "Step 1:" + first_step
+        completions = [first_step.strip()] + completions
+    elif "step 1:" in prompt:
+        prompt, first_step = prompt.split("step 1:")
+        first_step = "step 1:" + first_step
+        completions = [first_step.strip()] + completions
+    elif "?" in prompt:
+        prompt, first_step = prompt.split("?")
+        prompt = prompt + "?"
+        completions = [first_step.strip()] + completions
+    else:
+        raise ValueError(f"Prompt can't be processed: {prompt}")
+
+    # Strip the prompt
+    prompt = prompt.strip()
+
+    # Sanity check that the length of the completions is the same as the length of the labels
+    assert len(completions) == len(labels)
+
+    return {"prompt": prompt, "completions": completions, "labels": labels}
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser(ScriptArguments)
+    script_args = parser.parse_args_into_dataclasses()[0]
+
+    dataset = load_dataset("peiyi9979/Math-Shepherd")
+
+    dataset = dataset.map(
+        process_example,
+        remove_columns=["input", "label", "task"],
+        num_proc=script_args.dataset_num_proc,
+    )
+
+    if script_args.push_to_hub:
+        dataset.push_to_hub(script_args.repo_id)