File size: 2,475 Bytes
7476daa | 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 | import os, json, time, warnings
os.environ['HF_HUB_DISABLE_XET'] = '1'
os.environ['TOKENIZERS_PARALLELISM'] = 'false'
warnings.filterwarnings('ignore')
import mteb
import datasets
import pandas as pd
from pathlib import Path
from huggingface_hub import snapshot_download
MODEL = 'mixedbread-ai/deepset-mxbai-embed-de-large-v1'
benchmark = None
for b in mteb.get_benchmarks():
if getattr(b, 'name', None) == 'MTEB(eng, v2)':
benchmark = b
break
mind_task = [t for t in benchmark.tasks if t.metadata.name == 'MindSmallReranking'][0]
print(f'Task: {mind_task.metadata.name}', flush=True)
# Download dataset to HF cache
print('Downloading dataset...', flush=True)
t0 = time.time()
snapshot_download(
repo_id='mteb/MindSmallReranking',
repo_type='dataset',
revision='227478e3235572039f4f7661840e059f31ef6eb1',
)
print(f'Downloaded: {time.time()-t0:.1f}s', flush=True)
# Load data in offline mode
print('Loading data...', flush=True)
os.environ['HF_DATASETS_OFFLINE'] = '1'
t0 = time.time()
mind_task.load_data()
print(f'Data loaded: {time.time()-t0:.1f}s', flush=True)
# Load model
print('Loading model...', flush=True)
t0 = time.time()
model = mteb.get_model(MODEL)
print(f'Model loaded: {time.time()-t0:.1f}s', flush=True)
# Evaluate
print('Running evaluation...', flush=True)
outdir = Path('/tmp/output')
outdir.mkdir(parents=True, exist_ok=True)
t0 = time.time()
results = mteb.evaluate(
model, tasks=[mind_task],
prediction_folder=str(outdir),
overwrite_strategy='always', raise_error=True,
)
print(f'Completed: {time.time()-t0:.0f}s', flush=True)
for tr in results.task_results:
for split, sv in tr.scores.items():
if isinstance(sv, list):
for s in sv:
ms = s.get('main_score')
if ms is not None:
print(f'SCORE: {tr.task_name} [{split}]: {ms:.4f}', flush=True)
elif isinstance(sv, dict):
ms = sv.get('main_score')
if ms is not None:
print(f'SCORE: {tr.task_name} [{split}]: {ms:.4f}', flush=True)
from mteb.results.task_result import TaskResult
tr_data = results.task_results[0]
task_result = TaskResult.model_validate(tr_data.model_dump())
json_text = task_result.model_dump_json(indent=2)
# Save to file
with open('/data/mindsmall_result.json', 'w') as f:
f.write(json_text)
print('Saved to /data/mindsmall_result.json', flush=True)
print('JSON_START')
print(json_text)
print('JSON_END')
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