File size: 1,698 Bytes
545c4d5 | 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 | import json
from datetime import datetime
from cde_benchmark.embedders.sentence_transformer_embedder import (
SentenceTransformerEmbedder,
)
from sentence_transformers import SentenceTransformer
from cde_benchmark.embedders.jina_late_chunking_embedder import LateChunkingEmbedder
from cde_benchmark.formatters.data_formatter import DataFormatter
from cde_benchmark.evaluators.nanobeir import NanoBEIR
# Values
MODEL_NAME = "./models/modernbert-embed-base"
# MODEL_NAME = "./models/jina-embeddings-v2-small-en"
# MODEL_NAME = "./models/e5-base-v2"
model = SentenceTransformer(MODEL_NAME)
embedder = SentenceTransformerEmbedder(model) if False else LateChunkingEmbedder(model)
output_dic = {
"model": MODEL_NAME.split("/")[-1],
"date": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
"is_contextual": embedder.is_contextual_model,
}
print(output_dic)
output_dic["metrics"] = {}
output_dic["metrics"].update({"covid-qa": embedder.compute_metrics_e2e(DataFormatter(
"./data_dir/covid-qa", "train", query_key="queries"
))})
output_dic["metrics"].update({"chunked-mldr": embedder.compute_metrics_e2e( DataFormatter(
"./data_dir/chunked-mldr", "test", query_key="queries"
))})
output_dic["metrics"].update({"tech-qa": embedder.compute_metrics_e2e(DataFormatter(
"./data_dir/tech-qa", "train", query_key="queries"
))})
print(output_dic)
# nanobeir
nanobeir = NanoBEIR("./data_dir/nanobeir", embedder, is_contextual_model=False)
output_dic["metrics"].update(nanobeir.run_all_tasks())
# save as json
with open(
f"results/metrics_{MODEL_NAME.split('/')[-1]}{'_contextual' if embedder.is_contextual_model else ''}.json",
"w",
) as f:
json.dump(output_dic, f)
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