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import json
import os
REPLACE_MAP = {
"NDCG": 'ndcg',
"MAP": 'map',
"MRR": 'mrr',
"RECALL": 'recall',
"P": 'precision',
}
MODEL_TO_MODEL = {
"bm25": "bm25",
"bge": "bge-large-en-v1.5",
"cohere": "Cohere-embed-english-v3.0",
"e5": "e5-mistral-7b-instruct",
"google": "google-gecko.text-embedding-preview-0409",
"grit": "GritLM-7B",
"inst-l": "instructor-large",
"inst-xl": "instructor-xl",
"openai": "text-embedding-3-large",
"qwen2": "gte-Qwen2-7B-instruct",
"qwen": "gte-Qwen1.5-7B-instruct",
"sbert": "all-mpnet-base-v2",
"sf": "SFR-Embedding-Mistral",
"voyage": "voyage-large-2-instruct",
}
folders = os.listdir('bright_scores')
print(folders)
models = set([x.split("_")[-3] for x in folders if os.path.isdir('bright_scores/' + x)])
for model in models:
print(f"Converting {model}")
result_template = {
"dataset_revision": "a75a0eb",
"mteb_version": "1.12.79",
"scores": {
"standard": []
},
"task_name": "BrightRetrieval",
}
for folder in [x for x in folders if (os.path.isdir('bright_scores/' + x)) and (x.split("_")[-3] == model)]:
results_path = 'bright_scores/' + folder + '/results.json'
if len(folder.split("_")) == 4:
split = folder.split("_")[0]
elif len(folder.split("_")) == 5:
split = folder.split("_")[0] + "_" + folder.split("_")[1]
with open(results_path) as f:
results = json.load(f)
result_template['scores']['standard'].append(
{
"hf_subset": split,
"languages": ["eng-Latn"],
"main_score": results["NDCG@10"],
**{"_at_".join([REPLACE_MAP.get(x, x) for x in k.split("@")]): v for k,v in results.items()}
}
)
model_folder = MODEL_TO_MODEL[model]
os.makedirs(f"results/{model_folder}/no_revision_available", exist_ok=True)
print(f"Writing to: results/{model_folder}/no_revision_available/BrightRetrieval.json")
with open(f"results/{model_folder}/no_revision_available/BrightRetrieval.json", "w") as f:
json.dump(result_template, f, indent=4)
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