chore: import upstream snapshot with attribution

research/llm_embedder/evaluation/eval_tool.py

@@ -0,0 +1,54 @@
+import os
+import logging
+from typing import List
+from dataclasses import dataclass, field
+from transformers import HfArgumentParser
+from src.retrieval import (
+    RetrievalArgs, 
+)
+from .eval_retrieval import main
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ToolArgs(RetrievalArgs):
+    output_dir: str = field(
+        default="data/results/tool",
+    )
+    eval_data: str = field(
+        default="llm-embedder:tool/toolbench/test.json",
+        metadata={'help': 'Query jsonl.'}
+    )
+    corpus: str = field(
+        default="llm-embedder:tool/toolbench/corpus.json",
+        metadata={'help': 'Corpus path for retrieval.'}
+    )
+    key_template: str = field(
+        default="{text}",
+        metadata={'help': 'How to concatenate columns in the corpus to form one key?'}
+    )
+
+    cutoffs: List[int] = field(
+        default_factory=lambda: [1,3,5],
+        metadata={'help': 'Cutoffs to evaluate retrieval metrics.'}
+    )
+    max_neg_num: int = field(
+        default=32,
+        metadata={'help': 'Maximum negative number to mine.'}
+    )
+    log_path: str = field(
+        default="data/results/tool/toolbench.log",
+        metadata={'help': 'Path to the file for logging.'}
+    )
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser([ToolArgs])
+    args, = parser.parse_args_into_dataclasses()
+    if args.retrieval_method == "dense":
+        output_dir = os.path.join(args.output_dir, args.query_encoder.strip(os.sep).replace(os.sep, "--"))
+        args.output_dir = output_dir
+    else:
+        output_dir = os.path.join(args.output_dir, args.retrieval_method)
+    main(args)