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Muennighoff commited on
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9355679
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1 Parent(s): 9a79f7e

Add BRIGHT Long results

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Files changed (17) hide show
  1. paths.json +0 -0
  2. results.py +73 -57
  3. results/Cohere-embed-english-v3.0/no_revision_available/BrightRetrieval.json +349 -91
  4. results/GritLM-7B/no_revision_available/BrightRetrieval.json +350 -92
  5. results/SFR-Embedding-Mistral/no_revision_available/BrightRetrieval.json +350 -92
  6. results/bge-large-en-v1.5/no_revision_available/BrightRetrieval.json +350 -92
  7. results/bm25/no_revision_available/BrightRetrieval.json +350 -92
  8. results/e5-mistral-7b-instruct/no_revision_available/BrightRetrieval.json +350 -92
  9. results/google-gecko.text-embedding-preview-0409/no_revision_available/BrightRetrieval.json +350 -92
  10. results/gte-Qwen1.5-7B-instruct/no_revision_available/BrightRetrieval.json +350 -92
  11. results/gte-Qwen2-7B-instruct/no_revision_available/BrightRetrieval.json +350 -92
  12. results/instructor-large/no_revision_available/BrightRetrieval.json +349 -91
  13. results/instructor-xl/no_revision_available/BrightRetrieval.json +350 -92
  14. results/sentence-transformers__all-mpnet-base-v2/no_revision_available/BrightRetrieval.json +651 -0
  15. results/text-embedding-3-large/no_revision_available/BrightRetrieval.json +350 -92
  16. results/voyage-large-2-instruct/no_revision_available/BrightRetrieval.json +350 -92
  17. scripts/convert_bright.py +25 -12
paths.json CHANGED
The diff for this file is too large to render. See raw diff
 
results.py CHANGED
@@ -68,7 +68,6 @@ MODELS = [
68
  "LaBSE",
69
  "OpenSearch-text-hybrid",
70
  "SFR-Embedding-Mistral",
71
- "all-MiniLM-L12-v2",
72
  "all-MiniLM-L6-v2",
73
  "all-MiniLM-L6-v2-instruct",
74
  "all-mpnet-base-v2",
@@ -194,6 +193,12 @@ MODELS = [
194
  "sentence-t5-large",
195
  "sentence-t5-xl",
196
  "sentence-t5-xxl",
 
 
 
 
 
 
197
  "sgpt-bloom-1b7-nli",
198
  "sgpt-bloom-7b1-msmarco",
199
  "silver-retriever-base-v1",
@@ -291,6 +296,7 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
291
  "eval_language": datasets.Value("string"),
292
  "metric": datasets.Value("string"),
293
  "score": datasets.Value("float"),
 
294
  }
295
  ),
296
  supervised_keys=None,
@@ -300,8 +306,8 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
300
  def _split_generators(self, dl_manager):
301
  path_file = dl_manager.download_and_extract(URL)
302
  # Local debugging:
303
- #with open("/Users/muennighoff/Desktop/results/paths.json") as f:
304
- with open(path_file) as f:
305
  files = json.load(f)
306
  downloaded_files = dl_manager.download_and_extract(files[self.config.name])
307
  return [
@@ -333,8 +339,12 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
333
  split = "dev"
334
  elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
335
  split = "test.full"
336
- elif (ds_name in STANDARD_SPLIT) and ("standard" in res_dict):
337
- split = "standard"
 
 
 
 
338
  elif (ds_name in DEVTEST_SPLIT) and ("devtest" in res_dict):
339
  split = "devtest"
340
  elif (ds_name in TEST_AVG_SPLIT):
@@ -363,65 +373,71 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
363
  elif "test" not in res_dict:
364
  print(f"Skipping {ds_name} as split {split} not present.")
365
  continue
366
- res_dict = res_dict.get(split)
367
 
368
- ### New MTEB format ###
369
- if isinstance(res_dict, list):
370
- for res in res_dict:
371
- lang = res.pop("languages", [""])
372
- subset = res.pop("hf_subset", "")
373
- if len(lang) == 1:
374
- lang = lang[0].replace("eng-Latn", "")
375
- else:
376
- lang = "_".join(lang)
377
- if not lang:
378
- lang = subset
379
- for metric, score in res.items():
380
- if metric in SKIP_KEYS: continue
381
- if isinstance(score, dict):
382
- # Legacy format with e.g. {cosine: {spearman: ...}}
383
- # Now it is {cosine_spearman: ...}
384
- for k, v in score.items():
385
- if not isinstance(v, float):
386
- print(f'WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
387
  continue
388
- if metric in SKIP_KEYS: continue
389
  out.append({
390
  "mteb_dataset_name": ds_name,
391
  "eval_language": lang,
392
- "metric": metric + "_" + k,
393
- "score": v * 100,
 
394
  })
395
- else:
396
- if not isinstance(score, float):
397
- print(f'WARNING: Expected float, got {score} for {ds_name} {lang} {metric}')
398
- continue
399
- out.append({
400
- "mteb_dataset_name": ds_name,
401
- "eval_language": lang,
402
- "metric": metric,
403
- "score": score * 100,
404
- })
405
 
406
- ### Old MTEB format ###
407
- else:
408
- is_multilingual = any(x in res_dict for x in EVAL_LANGS)
409
- langs = res_dict.keys() if is_multilingual else ["en"]
410
- for lang in langs:
411
- if lang in SKIP_KEYS: continue
412
- test_result_lang = res_dict.get(lang) if is_multilingual else res_dict
413
- for metric, score in test_result_lang.items():
414
- if not isinstance(score, dict):
415
- score = {metric: score}
416
- for sub_metric, sub_score in score.items():
417
- if any(x in sub_metric for x in SKIP_KEYS): continue
418
- if isinstance(sub_score, dict): continue
419
- out.append({
420
- "mteb_dataset_name": ds_name,
421
- "eval_language": lang if is_multilingual else "",
422
- "metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric,
423
- "score": sub_score * 100,
424
- })
 
425
  for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])):
426
  yield idx, row
427
 
 
68
  "LaBSE",
69
  "OpenSearch-text-hybrid",
70
  "SFR-Embedding-Mistral",
 
71
  "all-MiniLM-L6-v2",
72
  "all-MiniLM-L6-v2-instruct",
73
  "all-mpnet-base-v2",
 
193
  "sentence-t5-large",
194
  "sentence-t5-xl",
195
  "sentence-t5-xxl",
196
+ "sentence-transformers__LaBSE",
197
+ "sentence-transformers__all-MiniLM-L12-v2",
198
+ "sentence-transformers__all-MiniLM-L6-v2",
199
+ "sentence-transformers__all-mpnet-base-v2",
200
+ "sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2",
201
+ "sentence-transformers__paraphrase-multilingual-mpnet-base-v2",
202
  "sgpt-bloom-1b7-nli",
203
  "sgpt-bloom-7b1-msmarco",
204
  "silver-retriever-base-v1",
 
296
  "eval_language": datasets.Value("string"),
297
  "metric": datasets.Value("string"),
298
  "score": datasets.Value("float"),
299
+ "split": datasets.Value("string"),
300
  }
301
  ),
302
  supervised_keys=None,
 
306
  def _split_generators(self, dl_manager):
307
  path_file = dl_manager.download_and_extract(URL)
308
  # Local debugging:
309
+ with open("/Users/muennighoff/Desktop/leaderboard/results/paths.json") as f:
310
+ #with open(path_file) as f:
311
  files = json.load(f)
312
  downloaded_files = dl_manager.download_and_extract(files[self.config.name])
313
  return [
 
339
  split = "dev"
340
  elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
341
  split = "test.full"
342
+ elif (ds_name in STANDARD_SPLIT):
343
+ split = []
344
+ if "standard" in res_dict:
345
+ split += ["standard"]
346
+ if "long" in res_dict:
347
+ split += ["long"]
348
  elif (ds_name in DEVTEST_SPLIT) and ("devtest" in res_dict):
349
  split = "devtest"
350
  elif (ds_name in TEST_AVG_SPLIT):
 
373
  elif "test" not in res_dict:
374
  print(f"Skipping {ds_name} as split {split} not present.")
375
  continue
 
376
 
377
+ splits = [split] if not isinstance(split, list) else split
378
+ full_res_dict = res_dict
379
+ for split in splits:
380
+ res_dict = full_res_dict.get(split)
381
+
382
+ ### New MTEB format ###
383
+ if isinstance(res_dict, list):
384
+ for res in res_dict:
385
+ lang = res.pop("languages", [""])
386
+ subset = res.pop("hf_subset", "")
387
+ if len(lang) == 1:
388
+ lang = lang[0].replace("eng-Latn", "")
389
+ else:
390
+ lang = "_".join(lang)
391
+ if not lang:
392
+ lang = subset
393
+ for metric, score in res.items():
394
+ if metric in SKIP_KEYS: continue
395
+ if isinstance(score, dict):
396
+ # Legacy format with e.g. {cosine: {spearman: ...}}
397
+ # Now it is {cosine_spearman: ...}
398
+ for k, v in score.items():
399
+ if not isinstance(v, float):
400
+ print(f'WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}')
401
+ continue
402
+ if metric in SKIP_KEYS: continue
403
+ out.append({
404
+ "mteb_dataset_name": ds_name,
405
+ "eval_language": lang,
406
+ "metric": metric + "_" + k,
407
+ "score": v * 100,
408
+ })
409
+ else:
410
+ if not isinstance(score, float):
411
+ print(f'WARNING: Expected float, got {score} for {ds_name} {lang} {metric}')
412
  continue
 
413
  out.append({
414
  "mteb_dataset_name": ds_name,
415
  "eval_language": lang,
416
+ "metric": metric,
417
+ "score": score * 100,
418
+ "split": split,
419
  })
 
 
 
 
 
 
 
 
 
 
420
 
421
+ ### Old MTEB format ###
422
+ else:
423
+ is_multilingual = any(x in res_dict for x in EVAL_LANGS)
424
+ langs = res_dict.keys() if is_multilingual else ["en"]
425
+ for lang in langs:
426
+ if lang in SKIP_KEYS: continue
427
+ test_result_lang = res_dict.get(lang) if is_multilingual else res_dict
428
+ for metric, score in test_result_lang.items():
429
+ if not isinstance(score, dict):
430
+ score = {metric: score}
431
+ for sub_metric, sub_score in score.items():
432
+ if any(x in sub_metric for x in SKIP_KEYS): continue
433
+ if isinstance(sub_score, dict): continue
434
+ out.append({
435
+ "mteb_dataset_name": ds_name,
436
+ "eval_language": lang if is_multilingual else "",
437
+ "metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric,
438
+ "score": sub_score * 100,
439
+ "split": split,
440
+ })
441
  for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])):
442
  yield idx, row
443
 
results/Cohere-embed-english-v3.0/no_revision_available/BrightRetrieval.json CHANGED
@@ -21,12 +21,12 @@
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,
@@ -53,12 +53,12 @@
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,
@@ -85,12 +85,12 @@
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,
@@ -117,12 +117,12 @@
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,
@@ -149,12 +149,12 @@
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
- "Recall_at_50": 0.36804,
157
- "Recall_at_100": 0.48952,
158
  "precision_at_1": 0.15385,
159
  "precision_at_5": 0.09744,
160
  "precision_at_10": 0.07778,
@@ -168,32 +168,32 @@
168
  "languages": [
169
  "eng-Latn"
170
  ],
171
- "main_score": 0.06038,
172
- "ndcg_at_1": 0.03077,
173
- "ndcg_at_5": 0.04492,
174
- "ndcg_at_10": 0.06038,
175
- "ndcg_at_25": 0.07583,
176
- "ndcg_at_50": 0.09984,
177
- "ndcg_at_100": 0.11176,
178
- "map_at_1": 0.01282,
179
- "map_at_5": 0.03846,
180
- "map_at_10": 0.04515,
181
- "map_at_25": 0.04835,
182
- "map_at_50": 0.0522,
183
- "map_at_100": 0.05341,
184
- "Recall_at_1": 0.01282,
185
- "Recall_at_5": 0.05385,
186
- "Recall_at_10": 0.1,
187
- "Recall_at_25": 0.15897,
188
- "Recall_at_50": 0.27051,
189
- "Recall_at_100": 0.33333,
190
- "precision_at_1": 0.03077,
191
- "precision_at_5": 0.02462,
192
- "precision_at_10": 0.01692,
193
- "precision_at_25": 0.01046,
194
- "precision_at_50": 0.00862,
195
  "precision_at_100": 0.00538,
196
- "mrr": 0.06393
197
  },
198
  {
199
  "hf_subset": "pony",
@@ -213,12 +213,12 @@
213
  "map_at_25": 0.00438,
214
  "map_at_50": 0.00531,
215
  "map_at_100": 0.00614,
216
- "Recall_at_1": 0.0018,
217
- "Recall_at_5": 0.00494,
218
- "Recall_at_10": 0.00891,
219
- "Recall_at_25": 0.01887,
220
- "Recall_at_50": 0.03612,
221
- "Recall_at_100": 0.06218,
222
  "precision_at_1": 0.01786,
223
  "precision_at_5": 0.01607,
224
  "precision_at_10": 0.01786,
@@ -245,12 +245,12 @@
245
  "map_at_25": 0.13095,
246
  "map_at_50": 0.14093,
247
  "map_at_100": 0.14616,
248
- "Recall_at_1": 0.04498,
249
- "Recall_at_5": 0.15885,
250
- "Recall_at_10": 0.23436,
251
- "Recall_at_25": 0.36151,
252
- "Recall_at_50": 0.45659,
253
- "Recall_at_100": 0.5814,
254
  "precision_at_1": 0.12963,
255
  "precision_at_5": 0.11111,
256
  "precision_at_10": 0.08519,
@@ -277,12 +277,12 @@
277
  "map_at_25": 0.04331,
278
  "map_at_50": 0.0456,
279
  "map_at_100": 0.04742,
280
- "Recall_at_1": 0.01341,
281
- "Recall_at_5": 0.0488,
282
- "Recall_at_10": 0.07132,
283
- "Recall_at_25": 0.10913,
284
- "Recall_at_50": 0.13728,
285
- "Recall_at_100": 0.20174,
286
  "precision_at_1": 0.07207,
287
  "precision_at_5": 0.04865,
288
  "precision_at_10": 0.03423,
@@ -309,12 +309,12 @@
309
  "map_at_25": 0.13465,
310
  "map_at_50": 0.13615,
311
  "map_at_100": 0.13741,
312
- "Recall_at_1": 0.10569,
313
- "Recall_at_5": 0.14228,
314
- "Recall_at_10": 0.15447,
315
- "Recall_at_25": 0.18769,
316
- "Recall_at_50": 0.23606,
317
- "Recall_at_100": 0.29354,
318
  "precision_at_1": 0.16585,
319
  "precision_at_5": 0.05171,
320
  "precision_at_10": 0.03024,
@@ -341,12 +341,12 @@
341
  "map_at_25": 0.22438,
342
  "map_at_50": 0.22678,
343
  "map_at_100": 0.22818,
344
- "Recall_at_1": 0.15293,
345
- "Recall_at_5": 0.24871,
346
- "Recall_at_10": 0.31056,
347
- "Recall_at_25": 0.39507,
348
- "Recall_at_50": 0.46315,
349
- "Recall_at_100": 0.53697,
350
  "precision_at_1": 0.28169,
351
  "precision_at_5": 0.10704,
352
  "precision_at_10": 0.06479,
@@ -373,12 +373,12 @@
373
  "map_at_25": 0.22454,
374
  "map_at_50": 0.22961,
375
  "map_at_100": 0.23404,
376
- "Recall_at_1": 0.12962,
377
- "Recall_at_5": 0.24012,
378
- "Recall_at_10": 0.29579,
379
- "Recall_at_25": 0.37788,
380
- "Recall_at_50": 0.45206,
381
- "Recall_at_100": 0.54211,
382
  "precision_at_1": 0.28448,
383
  "precision_at_5": 0.1431,
384
  "precision_at_10": 0.10345,
@@ -387,6 +387,264 @@
387
  "precision_at_100": 0.02379,
388
  "mrr": 0.37019
389
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
390
  ]
391
  },
392
  "task_name": "BrightRetrieval"
 
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,
 
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,
 
85
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@@ -213,12 +213,12 @@
213
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@@ -245,12 +245,12 @@
245
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@@ -277,12 +277,12 @@
277
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279
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@@ -309,12 +309,12 @@
309
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@@ -328,32 +328,32 @@
328
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329
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330
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@@ -373,12 +373,12 @@
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@@ -387,6 +387,264 @@
387
  "precision_at_100": 0.01342,
388
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389
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
390
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391
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392
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@@ -232,32 +232,32 @@
232
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233
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234
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235
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263
  "hf_subset": "psychology",
@@ -277,12 +277,12 @@
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@@ -309,12 +309,12 @@
309
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@@ -341,12 +341,12 @@
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@@ -387,6 +387,264 @@
387
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389
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390
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391
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392
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results/e5-mistral-7b-instruct/no_revision_available/BrightRetrieval.json CHANGED
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607
+ "recall_at_100": 0.99074,
608
+ "precision_at_1": 0.34259,
609
+ "precision_at_5": 0.15185,
610
+ "precision_at_10": 0.09907,
611
+ "precision_at_25": 0.04444,
612
+ "precision_at_50": 0.02352,
613
+ "precision_at_100": 0.01185,
614
+ "mrr": 0.48794
615
+ },
616
+ {
617
+ "hf_subset": "earth_science",
618
+ "languages": [
619
+ "eng-Latn"
620
+ ],
621
+ "main_score": 0.61544,
622
+ "ndcg_at_1": 0.48276,
623
+ "ndcg_at_5": 0.5734,
624
+ "ndcg_at_10": 0.61544,
625
+ "ndcg_at_25": 0.65196,
626
+ "ndcg_at_50": 0.66175,
627
+ "ndcg_at_100": 0.66449,
628
+ "map_at_1": 0.35345,
629
+ "map_at_5": 0.50069,
630
+ "map_at_10": 0.524,
631
+ "map_at_25": 0.53824,
632
+ "map_at_50": 0.54038,
633
+ "map_at_100": 0.54071,
634
+ "recall_at_1": 0.35345,
635
+ "recall_at_5": 0.67744,
636
+ "recall_at_10": 0.78376,
637
+ "recall_at_25": 0.90661,
638
+ "recall_at_50": 0.94971,
639
+ "recall_at_100": 0.96408,
640
+ "precision_at_1": 0.48276,
641
+ "precision_at_5": 0.2,
642
+ "precision_at_10": 0.12155,
643
+ "precision_at_25": 0.05759,
644
+ "precision_at_50": 0.03034,
645
+ "precision_at_100": 0.01543,
646
+ "mrr": 0.62409
647
+ }
648
  ]
649
  },
650
  "task_name": "BrightRetrieval"
scripts/convert_bright.py CHANGED
@@ -6,6 +6,7 @@ REPLACE_MAP = {
6
  "MAP": 'map',
7
  "MRR": 'mrr',
8
  "RECALL": 'recall',
 
9
  "P": 'precision',
10
  }
11
 
@@ -25,30 +26,42 @@ MODEL_TO_MODEL = {
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()}
 
6
  "MAP": 'map',
7
  "MRR": 'mrr',
8
  "RECALL": 'recall',
9
+ "Recall": 'recall',
10
  "P": 'precision',
11
  }
12
 
 
26
  "sf": "SFR-Embedding-Mistral",
27
  "voyage": "voyage-large-2-instruct",
28
  }
29
+ folders = os.listdir('bright_scores/main') + os.listdir('bright_scores/long_context')
30
+ models = set([x.split("_")[-3] for x in folders if (os.path.isdir('bright_scores/main/' + x) or os.path.isdir('bright_scores/long_context/' + x))])
31
+ print(models)
32
  for model in models:
33
  print(f"Converting {model}")
34
  result_template = {
35
  "dataset_revision": "a75a0eb",
36
  "mteb_version": "1.12.79",
37
  "scores": {
38
+ "standard": [],
39
+ "long": []
40
  },
41
  "task_name": "BrightRetrieval",
42
  }
43
+ for folder in [
44
+ x for x in folders if (os.path.isdir('bright_scores/main/' + x) or os.path.isdir('bright_scores/long_context/' + x)) and (x.split("_")[-3] == model)
45
+ ]:
46
+ if os.path.isdir('bright_scores/main/' + folder):
47
+ results_path = os.path.join('bright_scores/main', folder, 'results.json')
48
+ split = "standard"
49
+ else:
50
+ results_path = os.path.join('bright_scores/long_context', folder, 'results.json')
51
+ assert "long_True" in folder, folder
52
+ split = "long"
53
+
54
  with open(results_path) as f:
55
  results = json.load(f)
56
+
57
+ if len(folder.split("_")) == 4:
58
+ subset = folder.split("_")[0]
59
+ elif len(folder.split("_")) == 5:
60
+ subset = folder.split("_")[0] + "_" + folder.split("_")[1]
61
+
62
+ result_template['scores'][split].append(
63
  {
64
+ "hf_subset": subset,
65
  "languages": ["eng-Latn"],
66
  "main_score": results["NDCG@10"],
67
  **{"_at_".join([REPLACE_MAP.get(x, x) for x in k.split("@")]): v for k,v in results.items()}