Datasets:
mteb
/

Muennighoff commited on
Commit
1cc8a8e
1 Parent(s): 9b6b7ba
paths.json CHANGED
The diff for this file is too large to render. See raw diff
 
results.py CHANGED
@@ -26,7 +26,7 @@ VERSION = datasets.Version("1.0.1")
26
  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']
27
 
28
  # v_measures key is somehow present in voyage-2-law results and is a list
29
- SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures"]
30
 
31
  # Use "train" split instead
32
  TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"]
@@ -36,6 +36,8 @@ VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "LEMBSummScreenFDRetrieval", "M
36
  DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval"]
37
  # Use "test.full" split
38
  TESTFULL_SPLIT = ["OpusparcusPC"]
 
 
39
 
40
  TEST_AVG_SPLIT = {
41
  "LEMBNeedleRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"],
@@ -61,6 +63,7 @@ MODELS = [
61
  "LLM2Vec-Sheared-Llama-unsupervised",
62
  "LaBSE",
63
  "OpenSearch-text-hybrid",
 
64
  "all-MiniLM-L12-v2",
65
  "all-MiniLM-L6-v2",
66
  "all-mpnet-base-v2",
@@ -124,12 +127,14 @@ MODELS = [
124
  "google-gecko.text-embedding-preview-0409",
125
  "gottbert-base",
126
  "gte-Qwen1.5-7B-instruct",
 
127
  "gtr-t5-base",
128
  "gtr-t5-large",
129
  "gtr-t5-xl",
130
  "gtr-t5-xxl",
131
  "herbert-base-retrieval-v2",
132
  "instructor-base",
 
133
  "instructor-xl",
134
  "jina-embeddings-v2-base-en",
135
  "komninos",
@@ -228,8 +233,8 @@ def get_paths():
228
  if not os.path.isdir(os.path.join(results_model_dir, revision_folder)):
229
  continue
230
  for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)):
231
- if (res_file.endswith(".json")) and not(res_file.endswith("overall_results.json")):
232
- results_model_file = os.path.join(results_model_dir, res_file)
233
  files[model_dir].append(results_model_file)
234
  with open("paths.json", "w") as f:
235
  json.dump(files, f, indent=2)
@@ -265,9 +270,10 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
265
 
266
  def _split_generators(self, dl_manager):
267
  path_file = dl_manager.download_and_extract(URL)
 
 
268
  with open(path_file) as f:
269
  files = json.load(f)
270
-
271
  downloaded_files = dl_manager.download_and_extract(files[self.config.name])
272
  return [
273
  datasets.SplitGenerator(
@@ -298,6 +304,8 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
298
  split = "dev"
299
  elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
300
  split = "test.full"
 
 
301
  elif (ds_name in TEST_AVG_SPLIT):
302
  # Average splits
303
  res_dict["test_avg"] = {}
@@ -329,17 +337,40 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
329
  ### New MTEB format ###
330
  if isinstance(res_dict, list):
331
  for res in res_dict:
332
- lang = res.get("languages", [""])
333
- assert len(lang) == 1, "Only single-languages supported for now"
334
- lang = lang[0].replace("eng-Latn", "")
 
 
 
 
 
335
  for metric, score in res.items():
336
  if metric in SKIP_KEYS: continue
337
- out.append({
338
- "mteb_dataset_name": ds_name,
339
- "eval_language": lang,
340
- "metric": metric,
341
- "score": score * 100,
342
- })
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
343
 
344
  ### Old MTEB format ###
345
  else:
 
26
  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']
27
 
28
  # v_measures key is somehow present in voyage-2-law results and is a list
29
+ SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures", "scores_per_experiment"]
30
 
31
  # Use "train" split instead
32
  TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"]
 
36
  DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval"]
37
  # Use "test.full" split
38
  TESTFULL_SPLIT = ["OpusparcusPC"]
39
+ # Use "standard" split
40
+ STANDARD_SPLIT = ["BrightRetrieval"]
41
 
42
  TEST_AVG_SPLIT = {
43
  "LEMBNeedleRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"],
 
63
  "LLM2Vec-Sheared-Llama-unsupervised",
64
  "LaBSE",
65
  "OpenSearch-text-hybrid",
66
+ "SFR-Embedding-Mistral",
67
  "all-MiniLM-L12-v2",
68
  "all-MiniLM-L6-v2",
69
  "all-mpnet-base-v2",
 
127
  "google-gecko.text-embedding-preview-0409",
128
  "gottbert-base",
129
  "gte-Qwen1.5-7B-instruct",
130
+ "gte-Qwen2-7B-instruct",
131
  "gtr-t5-base",
132
  "gtr-t5-large",
133
  "gtr-t5-xl",
134
  "gtr-t5-xxl",
135
  "herbert-base-retrieval-v2",
136
  "instructor-base",
137
+ "instructor-large",
138
  "instructor-xl",
139
  "jina-embeddings-v2-base-en",
140
  "komninos",
 
233
  if not os.path.isdir(os.path.join(results_model_dir, revision_folder)):
234
  continue
235
  for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)):
236
+ if (res_file.endswith(".json")) and not(res_file.endswith(("overall_results.json", "model_meta.json"))):
237
+ results_model_file = os.path.join(results_model_dir, revision_folder, res_file)
238
  files[model_dir].append(results_model_file)
239
  with open("paths.json", "w") as f:
240
  json.dump(files, f, indent=2)
 
270
 
271
  def _split_generators(self, dl_manager):
272
  path_file = dl_manager.download_and_extract(URL)
273
+ # Local debugging:
274
+ #with open("/Users/muennighoff/Desktop/results/paths.json") as f:
275
  with open(path_file) as f:
276
  files = json.load(f)
 
277
  downloaded_files = dl_manager.download_and_extract(files[self.config.name])
278
  return [
279
  datasets.SplitGenerator(
 
304
  split = "dev"
305
  elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
306
  split = "test.full"
307
+ elif (ds_name in STANDARD_SPLIT) and ("standard" in res_dict):
308
+ split = "standard"
309
  elif (ds_name in TEST_AVG_SPLIT):
310
  # Average splits
311
  res_dict["test_avg"] = {}
 
337
  ### New MTEB format ###
338
  if isinstance(res_dict, list):
339
  for res in res_dict:
340
+ lang = res.pop("languages", [""])
341
+ subset = res.pop("hf_subset", "")
342
+ if len(lang) == 1:
343
+ lang = lang[0].replace("eng-Latn", "")
344
+ else:
345
+ lang = "_".join(lang)
346
+ if not lang:
347
+ lang = subset
348
  for metric, score in res.items():
349
  if metric in SKIP_KEYS: continue
350
+ if isinstance(score, dict):
351
+ # Legacy format with e.g. {cosine: {spearman: ...}}
352
+ # Now it is {cosine_spearman: ...}
353
+ for k, v in score.items():
354
+ if not isinstance(v, float):
355
+ print(f'WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}')
356
+ continue
357
+ if metric in SKIP_KEYS: continue
358
+ out.append({
359
+ "mteb_dataset_name": ds_name,
360
+ "eval_language": lang,
361
+ "metric": metric + "_" + k,
362
+ "score": v * 100,
363
+ })
364
+ else:
365
+ if not isinstance(score, float):
366
+ print(f'WARNING: Expected float, got {score} for {ds_name} {lang} {metric} {k}')
367
+ continue
368
+ out.append({
369
+ "mteb_dataset_name": ds_name,
370
+ "eval_language": lang,
371
+ "metric": metric,
372
+ "score": score * 100,
373
+ })
374
 
375
  ### Old MTEB format ###
376
  else:
results/Cohere-embed-english-v3.0/no_revision_available/BrightRetrieval.json ADDED
@@ -0,0 +1,393 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "dataset_revision": "a75a0eb",
3
+ "mteb_version": "1.12.79",
4
+ "scores": {
5
+ "standard": [
6
+ {
7
+ "hf_subset": "psychology",
8
+ "languages": [
9
+ "eng-Latn"
10
+ ],
11
+ "main_score": 0.21823,
12
+ "ndcg_at_1": 0.18812,
13
+ "ndcg_at_5": 0.20228,
14
+ "ndcg_at_10": 0.21823,
15
+ "ndcg_at_25": 0.24696,
16
+ "ndcg_at_50": 0.26808,
17
+ "ndcg_at_100": 0.29599,
18
+ "map_at_1": 0.05814,
19
+ "map_at_5": 0.12046,
20
+ "map_at_10": 0.14212,
21
+ "map_at_25": 0.16405,
22
+ "map_at_50": 0.16957,
23
+ "map_at_100": 0.17443,
24
+ "Recall_at_1": 0.05814,
25
+ "Recall_at_5": 0.16749,
26
+ "Recall_at_10": 0.25134,
27
+ "Recall_at_25": 0.3758,
28
+ "Recall_at_50": 0.4573,
29
+ "Recall_at_100": 0.57679,
30
+ "precision_at_1": 0.18812,
31
+ "precision_at_5": 0.13663,
32
+ "precision_at_10": 0.10198,
33
+ "precision_at_25": 0.06693,
34
+ "precision_at_50": 0.04436,
35
+ "precision_at_100": 0.02871,
36
+ "mrr": 0.27665
37
+ },
38
+ {
39
+ "hf_subset": "economics",
40
+ "languages": [
41
+ "eng-Latn"
42
+ ],
43
+ "main_score": 0.20185,
44
+ "ndcg_at_1": 0.15534,
45
+ "ndcg_at_5": 0.19145,
46
+ "ndcg_at_10": 0.20185,
47
+ "ndcg_at_25": 0.22909,
48
+ "ndcg_at_50": 0.24993,
49
+ "ndcg_at_100": 0.26924,
50
+ "map_at_1": 0.03475,
51
+ "map_at_5": 0.10182,
52
+ "map_at_10": 0.11747,
53
+ "map_at_25": 0.13623,
54
+ "map_at_50": 0.14624,
55
+ "map_at_100": 0.15216,
56
+ "Recall_at_1": 0.03475,
57
+ "Recall_at_5": 0.17231,
58
+ "Recall_at_10": 0.22402,
59
+ "Recall_at_25": 0.34823,
60
+ "Recall_at_50": 0.44208,
61
+ "Recall_at_100": 0.51574,
62
+ "precision_at_1": 0.15534,
63
+ "precision_at_5": 0.12816,
64
+ "precision_at_10": 0.10388,
65
+ "precision_at_25": 0.07534,
66
+ "precision_at_50": 0.0532,
67
+ "precision_at_100": 0.03466,
68
+ "mrr": 0.25056
69
+ },
70
+ {
71
+ "hf_subset": "robotics",
72
+ "languages": [
73
+ "eng-Latn"
74
+ ],
75
+ "main_score": 0.16211,
76
+ "ndcg_at_1": 0.18812,
77
+ "ndcg_at_5": 0.1599,
78
+ "ndcg_at_10": 0.16211,
79
+ "ndcg_at_25": 0.18741,
80
+ "ndcg_at_50": 0.21416,
81
+ "ndcg_at_100": 0.22839,
82
+ "map_at_1": 0.06417,
83
+ "map_at_5": 0.10481,
84
+ "map_at_10": 0.11268,
85
+ "map_at_25": 0.12302,
86
+ "map_at_50": 0.12867,
87
+ "map_at_100": 0.13067,
88
+ "Recall_at_1": 0.06417,
89
+ "Recall_at_5": 0.14288,
90
+ "Recall_at_10": 0.17651,
91
+ "Recall_at_25": 0.269,
92
+ "Recall_at_50": 0.3771,
93
+ "Recall_at_100": 0.43352,
94
+ "precision_at_1": 0.18812,
95
+ "precision_at_5": 0.09505,
96
+ "precision_at_10": 0.06535,
97
+ "precision_at_25": 0.04356,
98
+ "precision_at_50": 0.0295,
99
+ "precision_at_100": 0.01802,
100
+ "mrr": 0.24142
101
+ },
102
+ {
103
+ "hf_subset": "biology",
104
+ "languages": [
105
+ "eng-Latn"
106
+ ],
107
+ "main_score": 0.18978,
108
+ "ndcg_at_1": 0.16505,
109
+ "ndcg_at_5": 0.17454,
110
+ "ndcg_at_10": 0.18978,
111
+ "ndcg_at_25": 0.22773,
112
+ "ndcg_at_50": 0.25247,
113
+ "ndcg_at_100": 0.28335,
114
+ "map_at_1": 0.0434,
115
+ "map_at_5": 0.10616,
116
+ "map_at_10": 0.12105,
117
+ "map_at_25": 0.1367,
118
+ "map_at_50": 0.14193,
119
+ "map_at_100": 0.14624,
120
+ "Recall_at_1": 0.0434,
121
+ "Recall_at_5": 0.18077,
122
+ "Recall_at_10": 0.23136,
123
+ "Recall_at_25": 0.33892,
124
+ "Recall_at_50": 0.42433,
125
+ "Recall_at_100": 0.55949,
126
+ "precision_at_1": 0.16505,
127
+ "precision_at_5": 0.12621,
128
+ "precision_at_10": 0.08641,
129
+ "precision_at_25": 0.05165,
130
+ "precision_at_50": 0.03282,
131
+ "precision_at_100": 0.02107,
132
+ "mrr": 0.27194
133
+ },
134
+ {
135
+ "hf_subset": "stackoverflow",
136
+ "languages": [
137
+ "eng-Latn"
138
+ ],
139
+ "main_score": 0.16465,
140
+ "ndcg_at_1": 0.15385,
141
+ "ndcg_at_5": 0.14298,
142
+ "ndcg_at_10": 0.16465,
143
+ "ndcg_at_25": 0.1897,
144
+ "ndcg_at_50": 0.21659,
145
+ "ndcg_at_100": 0.24688,
146
+ "map_at_1": 0.03844,
147
+ "map_at_5": 0.08066,
148
+ "map_at_10": 0.09662,
149
+ "map_at_25": 0.10725,
150
+ "map_at_50": 0.11405,
151
+ "map_at_100": 0.1191,
152
+ "Recall_at_1": 0.03844,
153
+ "Recall_at_5": 0.14358,
154
+ "Recall_at_10": 0.21203,
155
+ "Recall_at_25": 0.28273,
156
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+ ],
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+ "main_score": 0.01481,
268
+ "ndcg_at_1": 0.02679,
269
+ "ndcg_at_5": 0.02011,
270
+ "ndcg_at_10": 0.01481,
271
+ "ndcg_at_25": 0.01324,
272
+ "ndcg_at_50": 0.01723,
273
+ "ndcg_at_100": 0.02295,
274
+ "map_at_1": 0.00135,
275
+ "map_at_5": 0.0032,
276
+ "map_at_10": 0.00331,
277
+ "map_at_25": 0.00362,
278
+ "map_at_50": 0.00398,
279
+ "map_at_100": 0.00432,
280
+ "Recall_at_1": 0.00135,
281
+ "Recall_at_5": 0.00507,
282
+ "Recall_at_10": 0.0058,
283
+ "Recall_at_25": 0.01043,
284
+ "Recall_at_50": 0.01873,
285
+ "Recall_at_100": 0.03085,
286
+ "precision_at_1": 0.02679,
287
+ "precision_at_5": 0.01786,
288
+ "precision_at_10": 0.01071,
289
+ "precision_at_25": 0.00857,
290
+ "precision_at_50": 0.00768,
291
+ "precision_at_100": 0.00652,
292
+ "mrr": 0.05551
293
+ },
294
+ {
295
+ "hf_subset": "theoremqa_theorems",
296
+ "languages": [
297
+ "eng-Latn"
298
+ ],
299
+ "main_score": 0.10128,
300
+ "ndcg_at_1": 0.09231,
301
+ "ndcg_at_5": 0.09778,
302
+ "ndcg_at_10": 0.10128,
303
+ "ndcg_at_25": 0.13144,
304
+ "ndcg_at_50": 0.15753,
305
+ "ndcg_at_100": 0.18175,
306
+ "map_at_1": 0.03385,
307
+ "map_at_5": 0.0741,
308
+ "map_at_10": 0.07716,
309
+ "map_at_25": 0.08486,
310
+ "map_at_50": 0.08921,
311
+ "map_at_100": 0.09167,
312
+ "Recall_at_1": 0.03385,
313
+ "Recall_at_5": 0.12103,
314
+ "Recall_at_10": 0.12872,
315
+ "Recall_at_25": 0.24154,
316
+ "Recall_at_50": 0.36077,
317
+ "Recall_at_100": 0.48872,
318
+ "precision_at_1": 0.09231,
319
+ "precision_at_5": 0.04923,
320
+ "precision_at_10": 0.02769,
321
+ "precision_at_25": 0.01785,
322
+ "precision_at_50": 0.01262,
323
+ "precision_at_100": 0.00862,
324
+ "mrr": 0.13921
325
+ },
326
+ {
327
+ "hf_subset": "biology",
328
+ "languages": [
329
+ "eng-Latn"
330
+ ],
331
+ "main_score": 0.23552,
332
+ "ndcg_at_1": 0.18447,
333
+ "ndcg_at_5": 0.20546,
334
+ "ndcg_at_10": 0.23552,
335
+ "ndcg_at_25": 0.27602,
336
+ "ndcg_at_50": 0.29962,
337
+ "ndcg_at_100": 0.32645,
338
+ "map_at_1": 0.05303,
339
+ "map_at_5": 0.13537,
340
+ "map_at_10": 0.16143,
341
+ "map_at_25": 0.17948,
342
+ "map_at_50": 0.18628,
343
+ "map_at_100": 0.19024,
344
+ "Recall_at_1": 0.05303,
345
+ "Recall_at_5": 0.20556,
346
+ "Recall_at_10": 0.29298,
347
+ "Recall_at_25": 0.41167,
348
+ "Recall_at_50": 0.49,
349
+ "Recall_at_100": 0.60618,
350
+ "precision_at_1": 0.18447,
351
+ "precision_at_5": 0.14951,
352
+ "precision_at_10": 0.10971,
353
+ "precision_at_25": 0.06214,
354
+ "precision_at_50": 0.03786,
355
+ "precision_at_100": 0.02272,
356
+ "mrr": 0.30076
357
+ },
358
+ {
359
+ "hf_subset": "stackoverflow",
360
+ "languages": [
361
+ "eng-Latn"
362
+ ],
363
+ "main_score": 0.15034,
364
+ "ndcg_at_1": 0.12821,
365
+ "ndcg_at_5": 0.13332,
366
+ "ndcg_at_10": 0.15034,
367
+ "ndcg_at_25": 0.19195,
368
+ "ndcg_at_50": 0.2278,
369
+ "ndcg_at_100": 0.24506,
370
+ "map_at_1": 0.0434,
371
+ "map_at_5": 0.08455,
372
+ "map_at_10": 0.09794,
373
+ "map_at_25": 0.11497,
374
+ "map_at_50": 0.12426,
375
+ "map_at_100": 0.12738,
376
+ "Recall_at_1": 0.0434,
377
+ "Recall_at_5": 0.11759,
378
+ "Recall_at_10": 0.17524,
379
+ "Recall_at_25": 0.29522,
380
+ "Recall_at_50": 0.41908,
381
+ "Recall_at_100": 0.47921,
382
+ "precision_at_1": 0.12821,
383
+ "precision_at_5": 0.09744,
384
+ "precision_at_10": 0.0735,
385
+ "precision_at_25": 0.05538,
386
+ "precision_at_50": 0.04137,
387
+ "precision_at_100": 0.02538,
388
+ "mrr": 0.2073
389
+ }
390
+ ]
391
+ },
392
+ "task_name": "BrightRetrieval"
393
+ }
scripts/convert_bright.py ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import os
3
+
4
+ REPLACE_MAP = {
5
+ "NDCG": 'ndcg',
6
+ "MAP": 'map',
7
+ "MRR": 'mrr',
8
+ "RECALL": 'recall',
9
+ "P": 'precision',
10
+ }
11
+
12
+ MODEL_TO_MODEL = {
13
+ "bm25": "bm25",
14
+ "bge": "bge-large-en-v1.5",
15
+ "cohere": "Cohere-embed-english-v3.0",
16
+ "e5": "e5-mistral-7b-instruct",
17
+ "google": "google-gecko.text-embedding-preview-0409",
18
+ "grit": "GritLM-7B",
19
+ "inst-l": "instructor-large",
20
+ "inst-xl": "instructor-xl",
21
+ "openai": "text-embedding-3-large",
22
+ "qwen2": "gte-Qwen2-7B-instruct",
23
+ "qwen": "gte-Qwen1.5-7B-instruct",
24
+ "sbert": "all-mpnet-base-v2",
25
+ "sf": "SFR-Embedding-Mistral",
26
+ "voyage": "voyage-large-2-instruct",
27
+ }
28
+ folders = os.listdir('bright_scores')
29
+ print(folders)
30
+ models = set([x.split("_")[-3] for x in folders if os.path.isdir('bright_scores/' + x)])
31
+ for model in models:
32
+ print(f"Converting {model}")
33
+ result_template = {
34
+ "dataset_revision": "a75a0eb",
35
+ "mteb_version": "1.12.79",
36
+ "scores": {
37
+ "standard": []
38
+ },
39
+ "task_name": "BrightRetrieval",
40
+ }
41
+ for folder in [x for x in folders if (os.path.isdir('bright_scores/' + x)) and (x.split("_")[-3] == model)]:
42
+ results_path = 'bright_scores/' + folder + '/results.json'
43
+ if len(folder.split("_")) == 4:
44
+ split = folder.split("_")[0]
45
+ elif len(folder.split("_")) == 5:
46
+ split = folder.split("_")[0] + "_" + folder.split("_")[1]
47
+ with open(results_path) as f:
48
+ results = json.load(f)
49
+ result_template['scores']['standard'].append(
50
+ {
51
+ "hf_subset": split,
52
+ "languages": ["eng-Latn"],
53
+ "main_score": results["NDCG@10"],
54
+ **{"_at_".join([REPLACE_MAP.get(x, x) for x in k.split("@")]): v for k,v in results.items()}
55
+ }
56
+ )
57
+
58
+ model_folder = MODEL_TO_MODEL[model]
59
+ os.makedirs(f"results/{model_folder}/no_revision_available", exist_ok=True)
60
+ print(f"Writing to: results/{model_folder}/no_revision_available/BrightRetrieval.json")
61
+ with open(f"results/{model_folder}/no_revision_available/BrightRetrieval.json", "w") as f:
62
+ json.dump(result_template, f, indent=4)
63
+
64
+