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1bf7747 39da3c9 1bf7747 39da3c9 1bf7747 39da3c9 1bf7747 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | #!/usr/bin/env python3
"""Run a pinned five-task Turkish retrieval suite with official MTEB evaluators."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import mteb
import torch
from mteb.models import SentenceTransformerEncoderWrapper
from sentence_transformers import SentenceTransformer
HARIER_TASK = "Given a Turkish web search query, retrieve relevant passages that answer the query"
TASK_NAMES = [
"TurHistQuadRetrieval",
"XQuADRetrieval",
"WebFAQRetrieval",
"MKQARetrieval",
"BelebeleRetrieval",
]
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--model", required=True)
parser.add_argument("--revision")
parser.add_argument("--label")
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--batch-size", type=int, default=16)
parser.add_argument("--max-seq-length", type=int, default=512)
parser.add_argument(
"--prompt-style",
choices=["auto", "plain", "harrier", "e5"],
default="auto",
help="Select the query/document format explicitly for fair local-model evaluation.",
)
parser.add_argument("--tasks", nargs="+", choices=TASK_NAMES, default=TASK_NAMES)
parser.add_argument("--overwrite", action="store_true")
return parser.parse_args()
def prompts_for(model_id: str, style: str = "auto") -> dict[str, str] | None:
if style == "plain":
return None
if style == "harrier":
return {
"Retrieval-query": f"Instruct: {HARIER_TASK}\nQuery: ",
"Retrieval-document": "",
}
if style == "e5":
return {"Retrieval-query": "query: ", "Retrieval-document": "passage: "}
normalized = model_id.casefold()
if "harrier" in normalized or "dusunen-rota" in normalized:
return {
"Retrieval-query": f"Instruct: {HARIER_TASK}\nQuery: ",
"Retrieval-document": "",
}
if "e5" in normalized:
return {"Retrieval-query": "query: ", "Retrieval-document": "passage: "}
return None
def build_tasks(task_names: list[str] | None = None) -> list:
selected = task_names or TASK_NAMES
return [
mteb.get_task(
task_name=name,
languages=["tur"],
exclusive_language_filter=True,
)
for name in selected
]
def main() -> None:
args = parse_args()
args.output.parent.mkdir(parents=True, exist_ok=True)
tasks = build_tasks(args.tasks)
model = SentenceTransformer(
args.model,
revision=args.revision,
model_kwargs={"dtype": torch.bfloat16},
)
model.max_seq_length = args.max_seq_length
parameter_count = sum(parameter.numel() for parameter in model.parameters())
wrapper = SentenceTransformerEncoderWrapper(
model=model,
model_prompts=prompts_for(args.model, args.prompt_style),
)
result = mteb.evaluate(
wrapper,
tasks,
cache=None,
overwrite_strategy="always" if args.overwrite else "only-missing",
encode_kwargs={
"batch_size": args.batch_size,
"normalize_embeddings": True,
},
show_progress_bar=True,
co2_tracker=False,
public_only=True,
)
task_scores = {}
for task_result in result.task_results:
task_scores[task_result.task_name] = float(task_result.get_score())
task_metadata = []
for task in tasks:
metadata = task.metadata.model_dump(mode="json")
task_metadata.append(
{
"name": metadata["name"],
"dataset": metadata["dataset"],
"license": metadata["license"],
"domains": metadata["domains"],
"eval_splits": metadata["eval_splits"],
"subsets": list(task.hf_subsets),
}
)
payload = {
"suite": "dusunen-turkish-retrieval-benchmark-v1",
"language_filter": "turkish_only_exclusive",
"model": args.label or args.model,
"model_source": args.model,
"model_revision": args.revision,
"parameters": parameter_count,
"embedding_dimension": model.get_sentence_embedding_dimension(),
"inference_dtype": "bfloat16",
"normalized_embeddings": True,
"prompt_style": args.prompt_style,
"device": torch.cuda.get_device_name(0) if torch.cuda.is_available() else "cpu",
"mteb_version": mteb.__version__,
"task_main_scores": task_scores,
"macro_average": sum(task_scores.values()) / len(task_scores),
"tasks": task_metadata,
"raw_mteb_result": result.model_dump(mode="json"),
}
args.output.write_text(
json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8"
)
print(
json.dumps(
{k: v for k, v in payload.items() if k != "raw_mteb_result"},
ensure_ascii=False,
indent=2,
)
)
if __name__ == "__main__":
main()
|