Add script for processing ultrafeedback prompt dataset

examples/datasets/ultrafeedback-prompt.py

@@ -0,0 +1,67 @@
+# 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.
+
+from dataclasses import dataclass
+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/ultrafeedback-prompt"`):
+            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/ultrafeedback-prompt"
+    dataset_num_proc: Optional[int] = None
+
+
+def to_unpaired_preference(example):
+    prompt = [{"role": "user", "content": example["instruction"]}]
+    return {"prompt": prompt}
+
+def drop_long_prompt(example):
+    if len(example["prompt"][0]["content"]) > 768:
+        return False
+    else:
+        return True
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser(ScriptArguments)
+    args = parser.parse_args_into_dataclasses()[0]
+
+    dataset = load_dataset("openbmb/UltraFeedback", split="train")
+
+    dataset = dataset.map(
+        to_unpaired_preference,
+        remove_columns=["source", "instruction", "models", "completions", "correct_answers", "incorrect_answers"],
+        num_proc=args.dataset_num_proc,
+    )
+    dataset = dataset.filter(drop_long_prompt)
+    dataset = dataset.train_test_split(test_size=0.05, seed=42)
+
+    if args.push_to_hub:
+        dataset.push_to_hub(args.repo_id)