File size: 31,669 Bytes
4af3178
64dd40c
1380fc9
4af3178
64dd40c
2c63c2f
b4966ee
7aae94f
78db81b
bd1cf3d
46022eb
78db81b
4af3178
7aae94f
4af3178
7aae94f
 
cd84165
7aae94f
f1fa713
 
 
 
 
cd84165
7aae94f
003d24d
2c63c2f
 
 
a51beac
3ffdc42
a51beac
3ffdc42
 
7aae94f
 
 
 
 
 
f1fa713
 
7aae94f
 
f1fa713
 
4af3178
 
7aae94f
4af3178
 
 
7aae94f
4af3178
099d855
f1fa713
 
 
 
 
 
 
 
 
bcadbe0
7aae94f
 
 
 
4af3178
2c63c2f
 
 
 
 
 
 
cd84165
 
2c63c2f
 
7aae94f
 
 
 
 
 
 
 
556c58e
 
 
2c63c2f
 
cd84165
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7aae94f
cd84165
 
 
 
7aae94f
cd84165
 
 
 
 
 
 
 
 
2c63c2f
099d855
64dd40c
4af3178
 
 
 
 
 
 
 
 
 
bcadbe0
64dd40c
7d1d0b3
bcadbe0
 
 
4af3178
 
 
64dd40c
ac3fdf5
 
 
 
 
 
 
 
f61dd83
4af3178
f61dd83
 
 
4af3178
f61dd83
 
 
 
 
 
 
7aae94f
 
 
 
 
 
 
 
 
78db81b
2c63c2f
78db81b
2c63c2f
f1fa713
 
 
 
 
 
 
4d67578
 
 
2c63c2f
 
 
 
 
 
 
216d974
4af3178
 
216d974
d2198dc
2c63c2f
7aae94f
78db81b
234d367
7aae94f
 
 
 
 
 
 
 
0ef2874
 
003d24d
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4d67578
 
 
1bd4020
 
 
003d24d
78db81b
0d4db15
4d67578
 
 
909b95d
 
7aae94f
 
 
909b95d
7aae94f
4d67578
4af3178
 
7aae94f
 
 
 
 
78db81b
2458a90
 
 
0d4db15
 
7aae94f
 
 
 
 
 
 
 
 
0d4db15
f61dd83
 
6af949b
 
003d24d
 
7aae94f
 
 
 
3ffdc42
7aae94f
 
64dd40c
 
f61dd83
7aae94f
3ffdc42
bd1cf3d
 
3ffdc42
7aae94f
 
 
 
3ffdc42
 
003d24d
17e0108
003d24d
7aae94f
 
 
 
f61dd83
6af949b
 
 
7aae94f
 
 
0d4db15
7aae94f
 
817663f
7aae94f
817663f
7aae94f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f61dd83
 
 
 
556c58e
f61dd83
7aae94f
f61dd83
4af3178
f61dd83
 
 
 
 
556c58e
f61dd83
 
 
556c58e
3ffdc42
1e84aac
 
 
4af3178
1e84aac
 
 
 
 
 
c05e080
1e84aac
 
 
2c58564
1e84aac
4af3178
 
 
 
1e84aac
 
4af3178
 
 
 
 
 
 
 
 
 
7aae94f
 
 
 
 
 
4af3178
 
7aae94f
4af3178
7aae94f
 
 
 
f1fa713
 
 
 
7aae94f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4af3178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
01976f0
 
 
 
 
4af3178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f1fa713
 
4af3178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f1fa713
 
 
 
4af3178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7aae94f
4af3178
 
 
 
 
 
 
dbfa15a
6135b88
dbfa15a
4af3178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f1fa713
 
4af3178
 
01976f0
 
4af3178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7aae94f
4af3178
 
 
 
 
 
 
 
 
 
 
 
c05fcec
4af3178
d2198dc
 
 
 
 
 
4d67578
88c25a0
 
 
 
 
 
 
 
 
 
 
 
2a75cd8
4af3178
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b4966ee
eafd5c8
b4966ee
3ffdc42
 
 
17e0108
3ffdc42
 
 
 
1bd4020
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
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
from functools import partial, reduce
import json
import os
import re

from datasets import load_dataset
import gradio as gr
from huggingface_hub import hf_hub_download
from huggingface_hub.repocard import metadata_load
import pandas as pd
from tqdm.autonotebook import tqdm

from utils.model_size import get_model_parameters_memory
from envs import LEADERBOARD_CONFIG, MODEL_META, REPO_ID, RESULTS_REPO, API

TASKS_CONFIG = LEADERBOARD_CONFIG["tasks"]
BOARDS_CONFIG = LEADERBOARD_CONFIG["boards"]

TASKS = list(TASKS_CONFIG.keys())
PRETTY_NAMES = {
    "InstructionRetrieval": "Retrieval w/Instructions",
    "PairClassification": "Pair Classification",
    "BitextMining": "Bitext Mining",
}

TASK_TO_METRIC = {k:v["metric"] for k,v in TASKS_CONFIG.items()}

def make_clickable_model(model_name, link=None):
    if link is None:
        link = "https://huggingface.co/" + model_name
    # Remove user from model name
    return (
        f'<a target="_blank" style="text-decoration: underline" href="{link}">{model_name.split("/")[-1]}</a>'
    )

EXTERNAL_MODELS = {k for k,v in MODEL_META["model_meta"].items() if v.get("is_external", False)}
EXTERNAL_MODEL_TO_LINK = {k: v["link"] for k,v in MODEL_META["model_meta"].items() if v.get("link", False)}
EXTERNAL_MODEL_TO_DIM = {k: v["dim"] for k,v in MODEL_META["model_meta"].items() if v.get("dim", False)}
EXTERNAL_MODEL_TO_SEQLEN = {k: v["seq_len"] for k,v in MODEL_META["model_meta"].items() if v.get("seq_len", False)}
EXTERNAL_MODEL_TO_SIZE = {k: v["size"] for k,v in MODEL_META["model_meta"].items() if v.get("size", False)}
PROPRIETARY_MODELS = {k for k,v in MODEL_META["model_meta"].items() if v.get("is_proprietary", False)}
TASK_DESCRIPTIONS = {k: v["task_description"] for k,v in TASKS_CONFIG.items()}
TASK_DESCRIPTIONS["Overall"] = "Overall performance across MTEB tasks."
SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {k for k,v in MODEL_META["model_meta"].items() if v.get("is_sentence_transformers_compatible", False)}
MODELS_TO_SKIP = MODEL_META["models_to_skip"]
CROSS_ENCODERS = MODEL_META["cross_encoders"]
BI_ENCODERS = [k for k, _ in MODEL_META["model_meta"].items() if k not in CROSS_ENCODERS + ["bm25"]]

PROPRIETARY_MODELS = {
    make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
    for model in PROPRIETARY_MODELS
}
SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS = {
    make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
    for model in SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS
}
CROSS_ENCODERS = {
    make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
    for model in CROSS_ENCODERS
}
BI_ENCODERS = {
    make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))
    for model in BI_ENCODERS
}


TASK_TO_TASK_TYPE = {task_category: [] for task_category in TASKS}
for board_config in BOARDS_CONFIG.values():
    for task_category, task_list in board_config["tasks"].items():
        TASK_TO_TASK_TYPE[task_category].extend(task_list)

def add_lang(examples):
    if not(examples["eval_language"]):
        examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"]
    else:
        examples["mteb_dataset_name_with_lang"] = examples["mteb_dataset_name"] + f' ({examples["eval_language"]})'
    return examples

def norm(names): return set([name.split(" ")[0] for name in names])

def add_task(examples):
    # Could be added to the dataset loading script instead
    task_name = examples["mteb_dataset_name"]
    task_type = None
    for task_category, task_list in TASK_TO_TASK_TYPE.items():
        if task_name in norm(task_list):
            task_type = task_category
            break
    if task_type is not None:
        examples["mteb_task"] = task_type
    else:
        print("WARNING: Task not found for dataset", examples["mteb_dataset_name"])
        examples["mteb_task"] = "Unknown"
    return examples

if os.path.exists("EXTERNAL_MODEL_RESULTS.json"):
    with open("EXTERNAL_MODEL_RESULTS.json") as f:
        EXTERNAL_MODEL_RESULTS = json.load(f)
    # Update with models not contained
    models_to_run = []
    for model in EXTERNAL_MODELS:
        if model not in EXTERNAL_MODEL_RESULTS:
            models_to_run.append(model)
            EXTERNAL_MODEL_RESULTS[model] = {k: {v: []} for k, v in TASK_TO_METRIC.items()}
else:
    EXTERNAL_MODEL_RESULTS = {model: {k: {v: []} for k, v in TASK_TO_METRIC.items()} for model in EXTERNAL_MODELS}
    models_to_run = EXTERNAL_MODELS

pbar = tqdm(models_to_run, desc="Fetching external model results")
for model in pbar:
    pbar.set_description(f"Fetching external model results for {model!r}")
    ds = load_dataset(RESULTS_REPO, model, trust_remote_code=True)
    # For local debugging:
    #, download_mode='force_redownload', verification_mode="no_checks")
    ds = ds.map(add_lang)
    ds = ds.map(add_task)
    base_dict = {"Model": make_clickable_model(model, link=EXTERNAL_MODEL_TO_LINK.get(model, f"https://huggingface.co/spaces/{REPO_ID}"))}
    # For now only one metric per task - Could add more metrics lateron
    for task, metric in TASK_TO_METRIC.items():
        ds_dict = ds.filter(lambda x: (x["mteb_task"] == task) and (x["metric"] == metric))["test"].to_dict()
        ds_dict = {k: round(v, 2) for k, v in zip(ds_dict["mteb_dataset_name_with_lang"], ds_dict["score"])}
        EXTERNAL_MODEL_RESULTS[model][task][metric].append({**base_dict, **ds_dict})

# Save & cache EXTERNAL_MODEL_RESULTS
with open("EXTERNAL_MODEL_RESULTS.json", "w") as f:
    json.dump(EXTERNAL_MODEL_RESULTS, f)

def get_dim_seq_size(model):
    filenames = [sib.rfilename for sib in model.siblings]
    dim, seq = "", ""
    for filename in filenames:
        if re.match("\d+_Pooling/config.json", filename):
            st_config_path = hf_hub_download(model.modelId, filename=filename)
            dim = json.load(open(st_config_path)).get("word_embedding_dimension", "")
            break
    for filename in filenames:
        if re.match("\d+_Dense/config.json", filename):
            st_config_path = hf_hub_download(model.modelId, filename=filename)
            dim = json.load(open(st_config_path)).get("out_features", dim)
    if "config.json" in filenames:
        config_path = hf_hub_download(model.modelId, filename="config.json")
        config = json.load(open(config_path))
        if not dim:
            dim = config.get("hidden_dim", config.get("hidden_size", config.get("d_model", "")))
        seq = config.get("n_positions", config.get("max_position_embeddings", config.get("n_ctx", config.get("seq_length", ""))))
    # Get model file size without downloading. Parameters in million parameters and memory in GB
    parameters, memory = get_model_parameters_memory(model)
    return dim, seq, parameters, memory

def make_datasets_clickable(df):
    """Does not work"""
    if "BornholmBitextMining" in df.columns:
        link = "https://huggingface.co/datasets/strombergnlp/bornholmsk_parallel"
        df = df.rename(
            columns={f'BornholmBitextMining': '<a target="_blank" style="text-decoration: underline" href="{link}">BornholmBitextMining</a>',})
    return df

def add_rank(df):
    cols_to_rank = [col for col in df.columns if col not in ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"]]
    if len(cols_to_rank) == 1:
        df.sort_values(cols_to_rank[0], ascending=False, inplace=True)
    else:
        df.insert(len(df.columns) - len(cols_to_rank), "Average", df[cols_to_rank].mean(axis=1, skipna=False))
        df.sort_values("Average", ascending=False, inplace=True)
    df.insert(0, "Rank", list(range(1, len(df) + 1)))
    df = df.round(2)
    # Fill NaN after averaging
    df.fillna("", inplace=True)
    return df

model_infos_path = "model_infos.json"
MODEL_INFOS = {}
if os.path.exists(model_infos_path):
    with open(model_infos_path) as f:
        MODEL_INFOS = json.load(f)

def get_mteb_data(tasks=["Clustering"], langs=[], datasets=[], fillna=True, add_emb_dim=True, task_to_metric=TASK_TO_METRIC, rank=True, refresh=True):
    global MODEL_INFOS
    api = API
    models = api.list_models(filter="mteb")
    # Initialize list to models that we cannot fetch metadata from
    df_list = []
    for model in EXTERNAL_MODEL_RESULTS:
        results_list = []
        for task in tasks:
            # Not all models have InstructionRetrieval, other new tasks
            if task not in EXTERNAL_MODEL_RESULTS[model]:
                continue
            results_list += EXTERNAL_MODEL_RESULTS[model][task][task_to_metric[task]]
        
        if len(datasets) > 0:
            res = {k: v for d in results_list for k, v in d.items() if (k == "Model") or any([x in k for x in datasets])}
        elif langs:
            # Would be cleaner to rely on an extra language column instead
            langs_format = [f"({lang})" for lang in langs]
            res = {k: v for d in results_list for k, v in d.items() if any([k.split(" ")[-1] in (k, x) for x in langs_format])}
        else:
            res = {k: v for d in results_list for k, v in d.items()}
        # Model & at least one result
        if len(res) > 1:
            if add_emb_dim:
                res["Model Size (Million Parameters)"] = EXTERNAL_MODEL_TO_SIZE.get(model, "")
                res["Memory Usage (GB, fp32)"] = round(res["Model Size (Million Parameters)"] * 1e6 * 4 / 1024**3, 2) if res["Model Size (Million Parameters)"] != "" else ""
                res["Embedding Dimensions"] = EXTERNAL_MODEL_TO_DIM.get(model, "")
                res["Max Tokens"] = EXTERNAL_MODEL_TO_SEQLEN.get(model, "")
            df_list.append(res)

    for model in models:
        if model.modelId in MODELS_TO_SKIP: continue
        print("MODEL", model.modelId)
        if model.modelId not in MODEL_INFOS or refresh:
            readme_path = hf_hub_download(model.modelId, filename="README.md")
            meta = metadata_load(readme_path)
            MODEL_INFOS[model.modelId] = {
                "metadata": meta
            }
        meta = MODEL_INFOS[model.modelId]["metadata"]
        if "model-index" not in meta:
            continue
        # meta['model-index'][0]["results"] is list of elements like:
        # {
        #    "task": {"type": "Classification"},
        #    "dataset": {
        #        "type": "mteb/amazon_massive_intent",
        #        "name": "MTEB MassiveIntentClassification (nb)",
        #        "config": "nb",
        #        "split": "test",
        #    },
        #    "metrics": [
        #        {"type": "accuracy", "value": 39.81506388702084},
        #        {"type": "f1", "value": 38.809586587791664},
        #    ],
        # },
        # Use "get" instead of dict indexing to skip incompat metadata instead of erroring out
        if len(datasets) > 0:
            task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and any([x in sub_res.get("dataset", {}).get("name", "") for x in datasets])]
        elif langs:
            task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks) and (sub_res.get("dataset", {}).get("config", "default") in ("default", *langs))]
        else:
            task_results = [sub_res for sub_res in meta["model-index"][0]["results"] if (sub_res.get("task", {}).get("type", "") in tasks)]
        out = [{res["dataset"]["name"].replace("MTEB ", ""): [round(score["value"], 2) for score in res["metrics"] if score["type"] == task_to_metric.get(res["task"]["type"])][0]} for res in task_results]
        out = {k: v for d in out for k, v in d.items()}
        out["Model"] = make_clickable_model(model.modelId)
        # Model & at least one result
        if len(out) > 1:
            if add_emb_dim:
                try:
                    # Fails on gated repos, so we only include scores for them
                    if "dim_seq_size" not in MODEL_INFOS[model.modelId] or refresh:
                        MODEL_INFOS[model.modelId]["dim_seq_size"] = list(get_dim_seq_size(model))
                    out["Embedding Dimensions"], out["Max Tokens"], out["Model Size (Million Parameters)"], out["Memory Usage (GB, fp32)"] = tuple(MODEL_INFOS[model.modelId]["dim_seq_size"])
                except:
                    MODEL_INFOS[model.modelId]["dim_seq_size"] = "", "", "", ""
            df_list.append(out)
        if model.library_name == "sentence-transformers" or "sentence-transformers" in model.tags or "modules.json" in {file.rfilename for file in model.siblings}:
            SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS.add(out["Model"])
            
    # Save & cache MODEL_INFOS
    with open("model_infos.json", "w") as f:
        json.dump(MODEL_INFOS, f)

    df = pd.DataFrame(df_list)
    # If there are any models that are the same, merge them
    # E.g. if out["Model"] has the same value in two places, merge & take whichever one is not NaN else just take the first one
    df = df.groupby("Model", as_index=False).first()
    # Put 'Model' column first
    cols = sorted(list(df.columns))
    base_columns = ["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens"]
    if len(datasets) > 0:
        #filter invalid columns
        cols = [col for col in cols if col in base_columns + datasets]
    i = 0
    for column in base_columns:
        if column in cols:
            cols.insert(i, cols.pop(cols.index(column)))
            i += 1
    df = df[cols]
    if rank:
        df = add_rank(df)       
    if fillna:
        df.fillna("", inplace=True)
    return df

# Get dict with a task list for each task category
# E.g. {"Classification": ["AmazonMassiveIntentClassification (en)", ...], "PairClassification": ["SprintDuplicateQuestions", ...]}
def get_mteb_average(task_dict: dict, refresh=True):
    all_tasks = reduce(lambda x, y: x + y, task_dict.values())
    DATA_OVERALL = get_mteb_data(
        tasks=list(task_dict.keys()),
        datasets=all_tasks,
        fillna=False,
        add_emb_dim=True,
        rank=False,
        refresh=refresh
    )
    # Debugging:
    # DATA_OVERALL.to_csv("overall.csv")
    
    DATA_OVERALL.insert(1, f"Average ({len(all_tasks)} datasets)", DATA_OVERALL[all_tasks].mean(axis=1, skipna=False))
    for i, (task_category, task_category_list) in enumerate(task_dict.items()):
        DATA_OVERALL.insert(i+2, f"{task_category} Average ({len(task_category_list)} datasets)", DATA_OVERALL[task_category_list].mean(axis=1, skipna=False))
    DATA_OVERALL.sort_values(f"Average ({len(all_tasks)} datasets)", ascending=False, inplace=True)
    # Start ranking from 1
    DATA_OVERALL.insert(0, "Rank", list(range(1, len(DATA_OVERALL) + 1)))

    DATA_OVERALL = DATA_OVERALL.round(2)

    DATA_TASKS = {}
    for task_category, task_category_list in task_dict.items():
        DATA_TASKS[task_category] = add_rank(DATA_OVERALL[["Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)"] + task_category_list])
        DATA_TASKS[task_category] = DATA_TASKS[task_category][DATA_TASKS[task_category].iloc[:, 4:].ne("").any(axis=1)]

    # Fill NaN after averaging
    DATA_OVERALL.fillna("", inplace=True)

    data_overall_rows = ["Rank", "Model", "Model Size (Million Parameters)", "Memory Usage (GB, fp32)", "Embedding Dimensions", "Max Tokens", f"Average ({len(all_tasks)} datasets)"]
    for task_category, task_category_list in task_dict.items():
        data_overall_rows.append(f"{task_category} Average ({len(task_category_list)} datasets)")

    DATA_OVERALL = DATA_OVERALL[data_overall_rows]
    DATA_OVERALL = DATA_OVERALL[DATA_OVERALL.iloc[:, 5:].ne("").any(axis=1)]

    return DATA_OVERALL, DATA_TASKS

boards_data = {}
all_data_tasks = []
for board, board_config in BOARDS_CONFIG.items():
    boards_data[board] = {
        "data_overall": None,
        "data_tasks": {}
    }
    if board_config["has_overall"]:
        data_overall, data_tasks = get_mteb_average(board_config["tasks"], refresh=False)
        boards_data[board]["data_overall"] = data_overall
        boards_data[board]["data_tasks"] = data_tasks
        all_data_tasks.extend(data_tasks.values())
    else:
        for task_category, task_category_list in board_config["tasks"].items():
            data_task_category = get_mteb_data(tasks=[task_category], datasets=task_category_list, refresh=False)
            data_task_category.drop(columns=["Embedding Dimensions", "Max Tokens"], inplace=True)
            boards_data[board]["data_tasks"][task_category] = data_task_category
            all_data_tasks.append(data_task_category)

# Exact, add all non-nan integer values for every dataset
NUM_SCORES = 0
DATASETS = []
MODELS = []
# LANGUAGES = []
for d in all_data_tasks:
    # NUM_SCORES += d.iloc[:, 1:].apply(lambda x: sum([1 for y in x if isinstance(y, float) and not np.isnan(y)]), axis=1).sum()
    cols_to_ignore = 4 if "Average" in d.columns else 3
    # Count number of scores including only non-nan floats & excluding the rank column
    NUM_SCORES += d.iloc[:, cols_to_ignore:].notna().sum().sum()
    # Exclude rank & model name column (first two); Do not count different language versions as different datasets
    DATASETS += [i.split(" ")[0] for i in d.columns[cols_to_ignore:]]
    # LANGUAGES += [i.split(" ")[-1] for i in d.columns[cols_to_ignore:]]
    MODELS += d["Model"].tolist()

NUM_DATASETS = len(set(DATASETS))
# NUM_LANGUAGES = len(set(LANGUAGES))
NUM_MODELS = len(set(MODELS))

# 1. Force headers to wrap
# 2. Force model column (maximum) width
# 3. Prevent model column from overflowing, scroll instead
# 4. Prevent checkbox groups from taking up too much space
css = """
table > thead {
    white-space: normal
}

table {
    --cell-width-1: 250px
}

table > tbody > tr > td:nth-child(2) > div {
    overflow-x: auto
}

.filter-checkbox-group {
    max-width: max-content;
}
"""

"""
Each inner tab can have the following keys:
- language: The language of the leaderboard
- language_long: [optional] The long form of the language
- description: The description of the leaderboard
- credits: [optional] The credits for the leaderboard
- data: The data for the leaderboard
- refresh: The function to refresh the leaderboard
"""

def get_refresh_function(task_category, task_list):
    def _refresh():
        data_task_category = get_mteb_data(tasks=[task_category], datasets=task_list)
        data_task_category.drop(columns=["Embedding Dimensions", "Max Tokens"], inplace=True)
        return data_task_category
    return _refresh

data = {
    "Overall": {"metric": "Various, refer to task tabs", "data": []}
}
for task in TASKS:
    data[task] = {"metric": TASKS_CONFIG[task]["metric_description"], "data": []}

for board, board_config in BOARDS_CONFIG.items():
    init_name = board_config["title"]
    if init_name in PRETTY_NAMES:
        init_name = PRETTY_NAMES[init_name]
    board_pretty_name = f"{init_name} leaderboard"
    acronym = board_config.get("acronym", None)
    board_icon = board_config.get("icon", None)
    if board_icon is None:
        board_icon = ""
    credits = board_config.get("credits", None)

    if board_config["has_overall"]:
        overall_pretty_name = board_pretty_name
        if acronym is not None:
            overall_pretty_name += f" ({board_config['acronym']})"
        data["Overall"]["data"].append({
            "language": board_config["title"],
            "language_long": board_config["language_long"],
            "description": f"**Overall MTEB {overall_pretty_name}** 🔮{board_icon}",
            "data": boards_data[board]["data_overall"],
            "refresh": lambda: get_mteb_average(board_config["tasks"])[0],#partial(get_mteb_average, board_config["tasks"]),
            "credits": credits,
        })
    for task_category, task_category_list in board_config["tasks"].items():
        task_icon = TASKS_CONFIG[task_category]['icon']
        if "special_icons" in board_config and isinstance(board_config["special_icons"], dict):
            task_icon = board_config["special_icons"].get(task_category, task_icon)
        data[task_category]["data"].append({
            "language": board_config["title"],
            "language_long": board_config["language_long"],
            "description": f"**{task_category} {board_pretty_name}** {task_icon}{board_icon}",
            "data": boards_data[board]["data_tasks"][task_category],
            "refresh": get_refresh_function(task_category, task_category_list),
            "credits": credits,
        })

dataframes = []
full_dataframes = []
tabs = []

# The following JavaScript function updates the URL parameters based on the selected task and language
# Additionally, `update_url_task` and `update_url_language` are used to update the current task and language
# The current task and language are stored in the `current_task_language` and `language_per_task` JSON objects
# This is all a bit hacky, but it might be the only way to pass options to a JavaScript function via Gradio
set_window_url_params = """
function(goalUrlObject) {
    const params = new URLSearchParams(window.location.search);
    for (const [key, value] of Object.entries(goalUrlObject)) {
        params.set(key, value);
    };
    const queryString = '?' + params.toString();
    console.log(queryString);
    window.history.replaceState({}, '', queryString);
    return [];
}
"""

def update_url_task(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
    current_task_language["task"] = event.target.id
    # Either use the cached language for this task or the 1st language
    try:
        current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[1].children[0].id)
    except Exception as e: # is Overall tab, no description
        current_task_language["language"] = language_per_task.get(event.target.id, event.target.children[0].children[0].id)

    return current_task_language, language_per_task

def update_url_language(event: gr.SelectData, current_task_language: dict, language_per_task: dict):
    current_task_language["language"] = event.target.id
    if "task" not in current_task_language:
        current_task_language["task"] = "overall"
    language_per_task[current_task_language["task"]] = event.target.id
    return current_task_language, language_per_task

NUMERIC_INTERVALS = {
    "<100M": pd.Interval(0, 100, closed="right"),
    "100M to 250M": pd.Interval(100, 250, closed="right"),
    "250M to 500M": pd.Interval(250, 500, closed="right"),
    "500M to 1B": pd.Interval(500, 1000, closed="right"),
    ">1B": pd.Interval(1000, 1_000_000, closed="right"),
}

MODEL_TYPES = [
    "Open",
    "Proprietary",
    "Sentence Transformers",
    "Cross-Encoders",
    "Bi-Encoders"
]

def filter_data(search_query, model_types, model_sizes, *full_dataframes):
    output_dataframes = []
    for df in full_dataframes:
        # Apply the search query
        if search_query:
            names = df["Model"].map(lambda x: re.match("<a .+?>(.+)</a>", x).group(1))
            masks = []
            for query in search_query.split(";"):
                masks.append(names.str.contains(query))
            df = df[reduce(lambda a, b: a | b, masks)]

        # Apply the model type filtering
        if set(model_types) != set(MODEL_TYPES):
            masks = []
            for model_type in model_types:
                if model_type == "Open":
                    masks.append(~df["Model"].isin(PROPRIETARY_MODELS))
                elif model_type == "Proprietary":
                    masks.append(df["Model"].isin(PROPRIETARY_MODELS))
                elif model_type == "Sentence Transformers":
                    masks.append(df["Model"].isin(SENTENCE_TRANSFORMERS_COMPATIBLE_MODELS))
                elif model_type == "Cross-Encoders":
                    masks.append(df["Model"].isin(CROSS_ENCODERS))
                elif model_type == "Bi-Encoders":
                    masks.append(df["Model"].isin(BI_ENCODERS))
            if masks:
                df = df[reduce(lambda a, b: a | b, masks)]
            else:
                df = pd.DataFrame(columns=df.columns)

        # Apply the model size filtering
        if set(model_sizes) != set(NUMERIC_INTERVALS.keys()):
            numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[model_size] for model_size in model_sizes]))
            sizes = df["Model Size (Million Parameters)"].replace('', 0)
            mask = sizes.apply(lambda size: any(numeric_interval.contains(size)))
            df = df[mask]

        output_dataframes.append(df)
    return output_dataframes


with gr.Blocks(css=css) as block:

    # Store the current task and language for updating the URL. This is a bit hacky, but it works
    # for passing the current task and language to the JavaScript function via Gradio
    current_task_language = gr.JSON(value=dict(), visible=False)
    language_per_task = gr.JSON(value=dict(), visible=False)

    gr.Markdown(f"""
    Massive Text Embedding Benchmark (MTEB) Leaderboard. To submit, refer to the <a href="https://github.com/embeddings-benchmark/mteb/blob/main/docs/adding_a_model.md" target="_blank" style="text-decoration: underline">MTEB GitHub repository</a> 🤗 Refer to the [MTEB paper](https://arxiv.org/abs/2210.07316) for details on metrics, tasks and models.
    """)

    with gr.Row():
        search_bar = gr.Textbox(
            label="Search Bar (separate multiple queries with `;`)",
            placeholder=" 🔍 Search for a model and press enter...",
        )
        filter_model_type = gr.CheckboxGroup(
            label="Model types",
            choices=MODEL_TYPES,
            value=MODEL_TYPES,
            interactive=True,
            elem_classes=["filter-checkbox-group"]
        )
        filter_model_sizes = gr.CheckboxGroup(
            label="Model sizes (in number of parameters)",
            choices=list(NUMERIC_INTERVALS.keys()),
            value=list(NUMERIC_INTERVALS.keys()),
            interactive=True,
            elem_classes=["filter-checkbox-group"],
            scale=2,
        )

    with gr.Tabs() as outer_tabs:
        # Store the tabs for updating them on load based on URL parameters
        tabs.append(outer_tabs)
        for task, task_values in data.items():
            metric = task_values["metric"]
            task_tab_id = task.lower().replace(" ", "-")

            # Overall, Bitext Mining, Classification, etc.
            pretty_task_name = task if task not in PRETTY_NAMES.keys() else PRETTY_NAMES[task]
            with gr.Tab(pretty_task_name, id=task_tab_id) as task_tab:
                # For updating the 'task' in the URL
                task_tab.select(update_url_task, [current_task_language, language_per_task], [current_task_language, language_per_task]).then(None, [current_task_language], [], js=set_window_url_params)
                if "Overall" != task:
                    gr.Markdown(TASK_DESCRIPTIONS[task])
                with gr.Tabs() as task_tabs:
                    # Store the task tabs for updating them on load based on URL parameters
                    tabs.append(task_tabs)

                    for item in task_values["data"]:
                        item_tab_id = item["language"].lower().replace(" ", "-")

                        # English, Chinese, French, etc.
                        with gr.Tab(item["language"], id=item_tab_id) as item_tab:
                            # For updating the 'language' in the URL
                            item_tab.select(update_url_language, [current_task_language, language_per_task], [current_task_language, language_per_task], trigger_mode="always_last").then(None, [current_task_language], [], js=set_window_url_params)

                            with gr.Row():
                                gr.Markdown(f"""
                                {item['description']}

                                - **Metric:** {metric}
                                - **Languages:** {item['language_long'] if 'language_long' in item else item['language']}
                                {"- **Credits:** " + item['credits'] if ("credits" in item and item["credits"] is not None) else ''}
                                """)

                            with gr.Row():
                                datatype = ["number", "markdown"] + ["number"] * len(item["data"])
                                dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", height=500)
                                dataframes.append(dataframe)

                                full_dataframe = gr.Dataframe(item["data"], datatype=datatype, type="pandas", visible=False)
                                full_dataframes.append(full_dataframe)

                            with gr.Row():
                                refresh_button = gr.Button("Refresh")
                                refresh_button.click(item["refresh"], inputs=None, outputs=dataframe, concurrency_limit=20)

    gr.Markdown(f"""
    - **Total Datasets**: {NUM_DATASETS}
    - **Total Languages**: 113
    - **Total Scores**: {NUM_SCORES}
    - **Total Models**: {NUM_MODELS}
    """ + r"""
    Made with ❤️ for NLP. If this work is useful to you, please consider citing:

    ```bibtex
    @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}
    }
    ```
    """)

    def set_tabs_on_load(request: gr.Request):
        """Set the selected tab based on the URL parameters on load."""
        global tabs
        valid_task_keys = [child.id for child in tabs[0].children]
        return_tabs = [gr.Tabs()] * len(tabs)

        query_params = request.request.query_params
        task_key = query_params.get("task", "overall")
        if task_key not in valid_task_keys:
            task_key = "overall"
        return_tabs[0] = gr.Tabs(selected=task_key)

        tabs_idx = valid_task_keys.index(task_key) + 1
        language_key = query_params.get("language", "english")
        return_tabs[tabs_idx] = gr.Tabs(selected=language_key)
        current_task_language = {"task": task_key, "language": language_key}
        language_per_task = {task_key: language_key}
        return return_tabs + [current_task_language, language_per_task]

    block.load(set_tabs_on_load, inputs=[], outputs=tabs + [current_task_language, language_per_task])

    search_bar.submit(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
    filter_model_type.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)
    filter_model_sizes.change(filter_data, inputs=[search_bar, filter_model_type, filter_model_sizes] + full_dataframes, outputs=dataframes)

block.queue(max_size=10)
block.launch()

# Possible changes:
# Could add graphs / other visual content
# Could add verification marks

# Sources:
# https://huggingface.co/spaces/gradio/leaderboard
# https://huggingface.co/spaces/huggingface-projects/Deep-Reinforcement-Learning-Leaderboard
# https://getemoji.com/