Initial commit

scripts/data_process/nq_search.py

@@ -0,0 +1,101 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# 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.
+"""
+Preprocess the nq dataset to parquet format
+"""
+
+import re
+import os
+import datasets
+
+from verl.utils.hdfs_io import copy, makedirs
+import argparse
+
+
+def make_prefix(dp, template_type):
+    question = dp['question']
+
+    # NOTE: also need to change reward_score/countdown.py
+    if template_type == 'base':
+        """This works for any base model"""
+        prefix = f"""Answer the given question. \
+You must conduct reasoning inside <think> and </think> first every time you get new information. \
+After reasoning, if you find you lack some knowledge, you can call a search engine by <search> query </search> and it will return the top searched results between <information> and </information>. \
+You can search as many times as your want. \
+If you find no further external knowledge needed, you can directly provide the answer inside <answer> and </answer>, without detailed illustrations. For example, <answer> Beijing </answer>. Question: {question}\n"""
+    else:
+        raise NotImplementedError
+    return prefix
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--local_dir', default='./data/nq_search')
+    parser.add_argument('--hdfs_dir', default=None)
+    parser.add_argument('--template_type', type=str, default='base')
+
+    args = parser.parse_args()
+
+    data_source = 'nq'
+
+    dataset = datasets.load_dataset('RUC-NLPIR/FlashRAG_datasets', 'nq')
+
+    train_dataset = dataset['train']
+    test_dataset = dataset['test']
+
+    # add a row to each data item that represents a unique id
+    def make_map_fn(split):
+
+        def process_fn(example, idx):
+            example['question'] = example['question'].strip()
+            if example['question'][-1] != '?':
+                example['question'] += '?'
+            question = make_prefix(example, template_type=args.template_type)
+            solution = {
+                "target": example['golden_answers'],
+            }
+
+            data = {
+                "data_source": data_source,
+                "prompt": [{
+                    "role": "user",
+                    "content": question,
+                }],
+                "ability": "fact-reasoning",
+                "reward_model": {
+                    "style": "rule",
+                    "ground_truth": solution
+                },
+                "extra_info": {
+                    'split': split,
+                    'index': idx,
+                }
+            }
+            return data
+
+        return process_fn
+
+    train_dataset = train_dataset.map(function=make_map_fn('train'), with_indices=True)
+    test_dataset = test_dataset.map(function=make_map_fn('test'), with_indices=True)
+
+    local_dir = args.local_dir
+    hdfs_dir = args.hdfs_dir
+
+    train_dataset.to_parquet(os.path.join(local_dir, 'train.parquet'))
+    test_dataset.to_parquet(os.path.join(local_dir, 'test.parquet'))
+
+    if hdfs_dir is not None:
+        makedirs(hdfs_dir)
+
+        copy(src=local_dir, dst=hdfs_dir)