forked from embeddings-benchmark/results
-
Notifications
You must be signed in to change notification settings - Fork 0
/
results.py
512 lines (483 loc) · 15.1 KB
/
results.py
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
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
"""MTEB Results"""
from __future__ import annotations
import json
import os
from pathlib import Path
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """@article{muennighoff2022mteb,
doi = {10.48550/ARXIV.2210.07316},
url = {https://arxiv.org/abs/2210.07316},
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
}
"""
_DESCRIPTION = """Results on MTEB"""
URL = "https://huggingface.co/datasets/mteb/results/resolve/main/paths.json"
VERSION = datasets.Version("1.0.1")
EVAL_LANGS = [
"af",
"afr-eng",
"am",
"amh",
"amh-eng",
"ang-eng",
"ar",
"ar-ar",
"ara-eng",
"arq-eng",
"arz-eng",
"ast-eng",
"awa-eng",
"az",
"aze-eng",
"bel-eng",
"ben-eng",
"ber-eng",
"bn",
"bos-eng",
"bre-eng",
"bul-eng",
"cat-eng",
"cbk-eng",
"ceb-eng",
"ces-eng",
"cha-eng",
"cmn-eng",
"cor-eng",
"csb-eng",
"cy",
"cym-eng",
"da",
"dan-eng",
"de",
"de-fr",
"de-pl",
"deu-eng",
"dsb-eng",
"dtp-eng",
"el",
"ell-eng",
"en",
"en-ar",
"en-de",
"en-en",
"en-tr",
"eng",
"epo-eng",
"es",
"es-en",
"es-es",
"es-it",
"est-eng",
"eus-eng",
"fa",
"fao-eng",
"fi",
"fin-eng",
"fr",
"fr-en",
"fr-pl",
"fra",
"fra-eng",
"fry-eng",
"gla-eng",
"gle-eng",
"glg-eng",
"gsw-eng",
"hau",
"he",
"heb-eng",
"hi",
"hin-eng",
"hrv-eng",
"hsb-eng",
"hu",
"hun-eng",
"hy",
"hye-eng",
"ibo",
"id",
"ido-eng",
"ile-eng",
"ina-eng",
"ind-eng",
"is",
"isl-eng",
"it",
"it-en",
"ita-eng",
"ja",
"jav-eng",
"jpn-eng",
"jv",
"ka",
"kab-eng",
"kat-eng",
"kaz-eng",
"khm-eng",
"km",
"kn",
"ko",
"ko-ko",
"kor-eng",
"kur-eng",
"kzj-eng",
"lat-eng",
"lfn-eng",
"lit-eng",
"lin",
"lug",
"lv",
"lvs-eng",
"mal-eng",
"mar-eng",
"max-eng",
"mhr-eng",
"mkd-eng",
"ml",
"mn",
"mon-eng",
"ms",
"my",
"nb",
"nds-eng",
"nl",
"nl-ende-en",
"nld-eng",
"nno-eng",
"nob-eng",
"nov-eng",
"oci-eng",
"orm",
"orv-eng",
"pam-eng",
"pcm",
"pes-eng",
"pl",
"pl-en",
"pms-eng",
"pol-eng",
"por-eng",
"pt",
"ro",
"ron-eng",
"ru",
"run",
"rus-eng",
"sl",
"slk-eng",
"slv-eng",
"spa-eng",
"sna",
"som",
"sq",
"sqi-eng",
"srp-eng",
"sv",
"sw",
"swa",
"swe-eng",
"swg-eng",
"swh-eng",
"ta",
"tam-eng",
"tat-eng",
"te",
"tel-eng",
"tgl-eng",
"th",
"tha-eng",
"tir",
"tl",
"tr",
"tuk-eng",
"tur-eng",
"tzl-eng",
"uig-eng",
"ukr-eng",
"ur",
"urd-eng",
"uzb-eng",
"vi",
"vie-eng",
"war-eng",
"wuu-eng",
"xho",
"xho-eng",
"yid-eng",
"yor",
"yue-eng",
"zh",
"zh-CN",
"zh-TW",
"zh-en",
"zsm-eng",
]
# v_measures key is somehow present in voyage-2-law results and is a list
SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures", "scores_per_experiment"]
# Use "train" split instead
TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"]
# Use "validation" split instead
VALIDATION_SPLIT = [
"AFQMC",
"Cmnli",
"IFlyTek",
"LEMBSummScreenFDRetrieval",
"MSMARCO",
"MSMARCO-PL",
"MultilingualSentiment",
"Ocnli",
"TNews",
]
# Use "dev" split instead
DEV_SPLIT = [
"CmedqaRetrieval",
"CovidRetrieval",
"DuRetrieval",
"EcomRetrieval",
"MedicalRetrieval",
"MMarcoReranking",
"MMarcoRetrieval",
"MSMARCO",
"MSMARCO-PL",
"T2Reranking",
"T2Retrieval",
"VideoRetrieval",
"TERRa",
"MIRACLReranking",
"MIRACLRetrieval",
]
# Use "test.full" split
TESTFULL_SPLIT = ["OpusparcusPC"]
# Use "standard" split
STANDARD_SPLIT = ["BrightRetrieval"]
# Use "devtest" split
DEVTEST_SPLIT = ["FloresBitextMining"]
TEST_AVG_SPLIT = {
"LEMBNeedleRetrieval": [
"test_256",
"test_512",
"test_1024",
"test_2048",
"test_4096",
"test_8192",
"test_16384",
"test_32768",
],
"LEMBPasskeyRetrieval": [
"test_256",
"test_512",
"test_1024",
"test_2048",
"test_4096",
"test_8192",
"test_16384",
"test_32768",
],
}
MODELS = sorted(list(set([str(file).split('/')[-1] for file in (Path(__file__).parent / "results").glob("*") if file.is_dir()])))
# Needs to be run whenever new files are added
def get_paths():
import collections, json, os
files = collections.defaultdict(list)
for model_dir in MODELS:
results_model_dir = os.path.join("results", model_dir)
if not os.path.isdir(results_model_dir):
print(f"Skipping {results_model_dir}")
continue
for revision_folder in os.listdir(results_model_dir):
if not os.path.isdir(os.path.join(results_model_dir, revision_folder)):
continue
if revision_folder == "external":
continue
for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)):
if (res_file.endswith(".json")) and not (
res_file.endswith(("overall_results.json", "model_meta.json"))
):
results_model_file = os.path.join(results_model_dir, revision_folder, res_file)
files[model_dir].append(results_model_file)
with open("paths.json", "w") as f:
json.dump(files, f, indent=2)
return files
class MTEBResults(datasets.GeneratorBasedBuilder):
"""MTEBResults"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=model,
description=f"{model} MTEB results",
version=VERSION,
)
for model in MODELS
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"mteb_dataset_name": datasets.Value("string"),
"eval_language": datasets.Value("string"),
"metric": datasets.Value("string"),
"score": datasets.Value("float"),
"split": datasets.Value("string"),
"hf_subset": datasets.Value("string"),
}
),
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path_file = dl_manager.download_and_extract(URL)
# Local debugging help
# with open("/path/to/local/paths.json") as f:
with open(path_file) as f:
files = json.load(f)
downloaded_files = dl_manager.download_and_extract(files[self.config.name])
return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files})]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info(f"Generating examples from {filepath}")
out = []
for path in filepath:
with open(path, encoding="utf-8") as f:
res_dict = json.load(f)
# Naming changed from mteb_dataset_name to task_name
ds_name = res_dict.get("mteb_dataset_name", res_dict.get("task_name"))
# New MTEB format uses scores
res_dict = res_dict.get("scores", res_dict)
split = "test"
if (ds_name in TRAIN_SPLIT) and ("train" in res_dict):
split = "train"
elif (ds_name in VALIDATION_SPLIT) and ("validation" in res_dict):
split = "validation"
elif (ds_name in DEV_SPLIT) and ("dev" in res_dict):
split = "dev"
elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
split = "test.full"
elif ds_name in STANDARD_SPLIT:
split = []
if "standard" in res_dict:
split += ["standard"]
if "long" in res_dict:
split += ["long"]
elif (ds_name in DEVTEST_SPLIT) and ("devtest" in res_dict):
split = "devtest"
elif ds_name in TEST_AVG_SPLIT:
# Average splits
res_dict = {}
for split in TEST_AVG_SPLIT[ds_name]:
# Old MTEB format
if isinstance(res_dict.get(split), dict):
for k, v in res_dict.get(split, {}).items():
if k in ["hf_subset", "languages"]:
res_dict[k] = v
v /= len(TEST_AVG_SPLIT[ds_name])
if k not in res_dict:
res_dict[k] = v
else:
res_dict[k] += v
# New MTEB format
elif isinstance(res_dict.get(split), list):
assert len(res_dict[split]) == 1, "Only single-lists supported for now"
for k, v in res_dict[split][0].items():
if k in ["hf_subset", "languages"]:
res_dict[k] = v
if not isinstance(v, float):
continue
v /= len(TEST_AVG_SPLIT[ds_name])
if k not in res_dict:
res_dict[k] = v
else:
res_dict[k] += v
split = "test_avg"
res_dict = {split: [res_dict]}
elif "test" not in res_dict:
print(f"Skipping {ds_name} as split {split} not present.")
continue
splits = [split] if not isinstance(split, list) else split
full_res_dict = res_dict
for split in splits:
res_dict = full_res_dict.get(split)
### New MTEB format ###
if isinstance(res_dict, list):
for res in res_dict:
lang = res.pop("languages", [""])
subset = res.pop("hf_subset", "")
if len(lang) == 1:
lang = lang[0].replace("eng-Latn", "")
else:
lang = "_".join(lang)
if not lang:
lang = subset
for metric, score in res.items():
if metric in SKIP_KEYS:
continue
if isinstance(score, dict):
# Legacy format with e.g. {cosine: {spearman: ...}}
# Now it is {cosine_spearman: ...}
for k, v in score.items():
if not isinstance(v, float):
print(f"WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}")
continue
if metric in SKIP_KEYS:
continue
out.append(
{
"mteb_dataset_name": ds_name,
"eval_language": lang,
"metric": metric + "_" + k,
"score": v * 100,
"hf_subset": subset,
}
)
else:
if not isinstance(score, float):
print(f"WARNING: Expected float, got {score} for {ds_name} {lang} {metric}")
continue
out.append(
{
"mteb_dataset_name": ds_name,
"eval_language": lang,
"metric": metric,
"score": score * 100,
"split": split,
"hf_subset": subset,
}
)
### Old MTEB format ###
else:
is_multilingual = any(x in res_dict for x in EVAL_LANGS)
langs = res_dict.keys() if is_multilingual else ["en"]
for lang in langs:
if lang in SKIP_KEYS:
continue
test_result_lang = res_dict.get(lang) if is_multilingual else res_dict
subset = test_result_lang.pop("hf_subset", "")
if subset == "" and is_multilingual:
subset = lang
for metric, score in test_result_lang.items():
if not isinstance(score, dict):
score = {metric: score}
for sub_metric, sub_score in score.items():
if any(x in sub_metric for x in SKIP_KEYS):
continue
if isinstance(sub_score, dict):
continue
out.append(
{
"mteb_dataset_name": ds_name,
"eval_language": lang if is_multilingual else "",
"metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric,
"score": sub_score * 100,
"split": split,
"hf_subset": subset,
}
)
for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])):
yield idx, row
# NOTE: for generating the new paths
if __name__ == "__main__":
get_paths()