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a2B: reproducible seed, public benchmarks, benchmark.py

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  1. README.md +60 -20
  2. benchmark.py +124 -0
  3. ties.json +1 -1
  4. tokenizer.json +124 -124
  5. tokenizer.model +2 -2
README.md CHANGED
@@ -59,23 +59,59 @@ See [Tied rows](#tied-rows) for what is shipped to help a model learn them as on
59
 
60
  ## Measured
61
 
62
- Scored on 20,000 documents withheld from the fit (1.42M weighted pairs),
63
- against morpheme segmentations derived from finite-state analyses of the same sources:
64
 
65
- | metric | value | what it means |
66
- |---|---|---|
67
- | **morph-f1** | **79.9%** | precision 90.0% / recall 71.7% against verified morpheme boundaries |
68
- | **stem seam** | **79.8%** | inflected words cut at the stem boundary |
69
- | **one-piece function** | **71.3%** | grammatical functions that are a single row (98 functions) |
70
- | **single-token lemma** | **94.9%** | dictionary headwords that survive as one piece |
71
- | **paradigm spread** | **1.23 rows** | distinct stem rows a paradigm spends; 1.0 is ideal |
72
- | chars/token | 3.662 | fertility, on Hungarian prose |
73
- | round-trip failures | 0 | on the full evaluation sample |
74
- | byte fallback | 0.084% | |
75
-
76
- Fertility is reported for reference only.
77
- It was **not** the objective, and this vocabulary deliberately trades a little of it for grammar:
78
- the fit that maximised chars/token scored 53.2% morph-f1, versus 79.9% here.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
79
 
80
  ### Special tokens
81
 
@@ -95,7 +131,7 @@ Note the ChatML split: `<|im_end|>` terminates a *turn*, `<|eos|>` terminates a
95
 
96
  ### Tied rows
97
 
98
- `ties.json` maps 1,365 allomorph rows onto the one row a model should learn first: `ban` -> `ben`, `höz` -> `hoz`, across 101 suffix families.
99
  Vowel harmony makes these variants of one morpheme,
100
  so initialising their embeddings from a shared base gives the model the paradigm for free rather than making it discover the relationship from data.
101
 
@@ -106,8 +142,8 @@ This is an initialisation aid, not part of tokenization, nothing reads it automa
106
  The vocabulary is overfitted well past its target size and then **selected down**,
107
  with a declared Hungarian lexicon deciding what survives:
108
 
109
- 1. **Seed** - 32,910 pieces required up front: 28,000 frequent stems, 1,000 named entities,
110
- 1,000 capitalised forms, 445 suffixes, 2,011 alternating stems and 448 programming keywords.
111
  2. **Overfit** - a large Unigram vocabulary is trained on the corpus.
112
  3. **Evict** - any piece that is a regular inflection of a declared headword is removed. `okát` is not a row; `ok` and `át` are.
113
  4. **Spare** - an eviction is reversed if the word would then segment worse than its morphology says it should.
@@ -123,7 +159,7 @@ Plus small code and mathematics slices, so that technical text does not fall bac
123
  - **Coverage, not rules, sets the floor.** A stem outside the 28,000-stem seed can still be mis-split,
124
  and an alternating stem can lose on score: `művek` cuts as `mű` + `vek` where morphology says `műv` + `ek`.
125
  - **Compound seams come from a declared lexicon**, so a compound absent from it gets no guaranteed seam.
126
- - **Cased entity handling is partial.** 4,000 capitalised forms are seeded; beyond those, capitalisation costs a row.
127
  - **Two losses are declared, not accidental.** Metaspace writes the word mark as
128
  U+2581, so a literal U+2581 in the input is indistinguishable from a space. The
129
  tokenizer declares a `Replace(U+2581, " ")` normalizer ahead of Metaspace, which
@@ -144,6 +180,10 @@ This tokenizer is a derivative work of the following resouces, see `NOTICE`.
144
  - **[hunmorph-foma](https://github.com/r0ller/hunmorph-foma)** - BSD-2-Clause,
145
  (c) 2016 r0ller. Finite-state transducers that analysed corpus words into lemma
146
  plus tags, bootstrapping the segmentation this vocabulary is scored against.
 
 
 
 
147
 
148
  The tokenizer itself is Apache-2.0.
149
 
 
59
 
60
  ## Measured
61
 
62
+ Every number below is reproducible from public artifacts by `benchmark.py` in this repository:
 
63
 
64
+ ```
65
+ pip install transformers datasets
66
+ python benchmark.py
67
+ ```
68
+
69
+ It reads a pinned UniMorph commit, five Hugging Face datasets at a fixed document count, and five
70
+ published tokenizers. Nothing from the pipeline that produced this vocabulary is involved.
71
+
72
+ ### Paradigm sharing
73
+
74
+ The property this vocabulary is built for. On [UniMorph Hungarian](https://github.com/unimorph/hun)
75
+ (first 20,000 lemmas in file order: 19,424 lemmas, 1,014,882 lemma/form pairs):
76
+
77
+ | tokenizer | vocab | **one stem row + suffixes** | prefix kept | lemma is one token | tokens/form |
78
+ |---|---|---|---|---|---|
79
+ | **picur/picur-tokenizer** | 49,152 | **44.1%** | **72.4%** | **54.3%** | 3.04 |
80
+ | NYTK/PULI-GPT-3SX | 50,000 | 24.5% | 54.9% | 38.5% | 2.98 |
81
+ | SZTAKI-HLT/hubert-base-cc | 32,001 | 23.6% | 55.5% | 28.1% | 3.07 |
82
+ | FacebookAI/xlm-roberta-base | 250,002 | 10.8% | 60.4% | 12.8% | 3.66 |
83
+ | Qwen/Qwen3-8B | 151,643 | 2.0% | 54.9% | 3.1% | 5.29 |
84
+
85
+ **one stem row + suffixes** asks both halves of the question at once: the lemma is a SINGLE row,
86
+ *and* its inflected form begins with that row. So every mention of the word teaches one embedding.
87
+
88
+ Neither half can be won on its own. Cutting everything small satisfies `prefix kept` and destroys
89
+ `lemma is one token` -- that is the Qwen row, 54.9% and 2.0%. Swallowing whole inflected forms does
90
+ the reverse. Only the conjunction says a paradigm shares a row, and it does so at the same token
91
+ cost per form as the closest baseline.
92
+
93
+ ### Fertility
94
+
95
+ Characters a token buys, on the first 2,000 documents of each dataset:
96
+
97
+ | corpus | picur | PULI-GPT-3SX | huBERT | XLM-R | Qwen3 |
98
+ |---|---|---|---|---|---|
99
+ | `lazos/gutenberg-hu` | 3.761 | **4.050** | 3.915 | 3.381 | 2.417 |
100
+ | `lazos/gazette-hu` | 2.959 | **3.808** | 3.665 | 3.392 | 1.997 |
101
+ | `SZTAKI-HLT/HunSum-2-abstractive` | 3.988 | **4.544** | 4.487 | 3.803 | 2.367 |
102
+ | `open-web-math/open-web-math` | **2.923** | 2.394 | 2.385 | 3.412 | 3.669 |
103
+ | `bigcode/starcoderdata` (python) | **2.465** | 1.721 | 2.454 | 3.205 | 4.121 |
104
+
105
+ **On Hungarian prose the two Hungarian baselines beat this tokenizer, and that is the trade, not a
106
+ defect.** PULI buys more characters per token by keeping `házakban` as one row; this vocabulary
107
+ spends an extra token to cut it into `▁ház` `ak` `ban`, and gets the paradigm table above in
108
+ return. A model reading 4% fewer tokens that has to learn `ház` four times over has not come out
109
+ ahead.
110
+
111
+ Where the comparison is not about Hungarian morphology it inverts: on mathematics and code this
112
+ vocabulary beats both Hungarian baselines, because a slice of each was in the fit corpus so
113
+ technical text does not fall back to bytes. Qwen wins those outright, as an English-first
114
+ tokenizer should.
115
 
116
  ### Special tokens
117
 
 
131
 
132
  ### Tied rows
133
 
134
+ `ties.json` maps 1,353 allomorph rows onto the one row a model should learn first: `ban` -> `ben`, `höz` -> `hoz`, across 100 suffix families.
135
  Vowel harmony makes these variants of one morpheme,
136
  so initialising their embeddings from a shared base gives the model the paradigm for free rather than making it discover the relationship from data.
137
 
 
142
  The vocabulary is overfitted well past its target size and then **selected down**,
143
  with a declared Hungarian lexicon deciding what survives:
144
 
145
+ 1. **Seed** - 32,888 pieces required up front: 28,000 frequent stems, 1,000 named entities,
146
+ 1,000 capitalised forms, 432 suffixes, 2,005 alternating stems and 448 programming keywords.
147
  2. **Overfit** - a large Unigram vocabulary is trained on the corpus.
148
  3. **Evict** - any piece that is a regular inflection of a declared headword is removed. `okát` is not a row; `ok` and `át` are.
149
  4. **Spare** - an eviction is reversed if the word would then segment worse than its morphology says it should.
 
159
  - **Coverage, not rules, sets the floor.** A stem outside the 28,000-stem seed can still be mis-split,
160
  and an alternating stem can lose on score: `művek` cuts as `mű` + `vek` where morphology says `műv` + `ek`.
161
  - **Compound seams come from a declared lexicon**, so a compound absent from it gets no guaranteed seam.
162
+ - **Cased entity handling is partial.** 1,000 capitalised forms are seeded; beyond those, capitalisation costs a row.
163
  - **Two losses are declared, not accidental.** Metaspace writes the word mark as
164
  U+2581, so a literal U+2581 in the input is indistinguishable from a space. The
165
  tokenizer declares a `Replace(U+2581, " ")` normalizer ahead of Metaspace, which
 
180
  - **[hunmorph-foma](https://github.com/r0ller/hunmorph-foma)** - BSD-2-Clause,
181
  (c) 2016 r0ller. Finite-state transducers that analysed corpus words into lemma
182
  plus tags, bootstrapping the segmentation this vocabulary is scored against.
183
+ - **[UniMorph Hungarian](https://github.com/unimorph/hun)** - CC BY-SA 3.0, derived
184
+ from English Wiktionary. Used by `benchmark.py` only: it is scored against and
185
+ never built from, which is what makes the paradigm table above independent of
186
+ everything that produced the vocabulary.
187
 
188
  The tokenizer itself is Apache-2.0.
189
 
benchmark.py ADDED
@@ -0,0 +1,124 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Reproduce the tables in this model card.
2
+
3
+ pip install transformers datasets
4
+ python benchmark.py # both tables
5
+ python benchmark.py paradigms # UniMorph only
6
+ python benchmark.py fertility # corpora only
7
+
8
+ Everything it reads is public and pinned: a UniMorph commit, five Hugging Face datasets at a fixed
9
+ document count, and five published tokenizers. No part of the pipeline that produced this
10
+ vocabulary is involved, which is the point -- the numbers are meant to be checkable by someone who
11
+ has never seen it.
12
+ """
13
+ import re
14
+ import sys
15
+ import unicodedata
16
+ import urllib.request
17
+ from collections import defaultdict
18
+
19
+ from datasets import load_dataset
20
+ from transformers import AutoTokenizer
21
+
22
+ TOKENIZERS = (
23
+ "picur/picur-tokenizer",
24
+ "NYTK/PULI-GPT-3SX",
25
+ "SZTAKI-HLT/hubert-base-cc",
26
+ "Qwen/Qwen3-8B",
27
+ "FacebookAI/xlm-roberta-base",
28
+ )
29
+
30
+ UNIMORPH = ("https://raw.githubusercontent.com/unimorph/hun/"
31
+ "20d5e04211bbab3dd689c99637356a8eab7c5fd7/hun")
32
+ LEMMAS = 20_000
33
+
34
+ CORPORA = (
35
+ ("lazos/gutenberg-hu", None, "data", "text"),
36
+ ("lazos/gazette-hu", None, "data", "text"),
37
+ ("SZTAKI-HLT/HunSum-2-abstractive", None, "data", "article"),
38
+ ("open-web-math/open-web-math", None, "data", "text"),
39
+ ("bigcode/starcoderdata", "python", None, "content"),
40
+ )
41
+ DOCUMENTS = 2_000
42
+ MIN_CHARS = 200
43
+ WORD = re.compile(r"\w+", re.UNICODE)
44
+
45
+
46
+ def load():
47
+ found = {}
48
+ for name in TOKENIZERS:
49
+ try:
50
+ found[name] = AutoTokenizer.from_pretrained(name)
51
+ except Exception as error: # a gated model is skipped, not fatal
52
+ print(f"skip {name}: {type(error).__name__}", file=sys.stderr)
53
+ return found
54
+
55
+
56
+ def paradigms():
57
+ """Does an inflected form begin with its lemma, and is that lemma a single row?"""
58
+ with urllib.request.urlopen(UNIMORPH, timeout=120) as handle:
59
+ raw = handle.read().decode("utf-8")
60
+ table = defaultdict(list)
61
+ for line in raw.splitlines():
62
+ parts = line.split("\t")
63
+ if len(parts) < 3 or not parts[0] or not parts[1]:
64
+ continue
65
+ if len(table) >= LEMMAS and parts[0] not in table:
66
+ continue
67
+ if parts[1] != parts[0]:
68
+ table[parts[0]].append(parts[1])
69
+ pairs = sum(len(v) for v in table.values())
70
+ print(f"\nUniMorph hun: {len(table):,} lemmas, {pairs:,} lemma/form pairs")
71
+ print(f"{'tokenizer':32s} {'stem row':>9s} {'prefix':>8s} {'lemma=1':>8s} {'tok/form':>9s}")
72
+ for name, tok in load().items():
73
+ cache = {}
74
+
75
+ def cut(word, tok=tok, cache=cache):
76
+ if word not in cache:
77
+ cache[word] = tuple(tok.tokenize(" " + word))
78
+ return cache[word]
79
+
80
+ rows = shared = whole = cost = 0
81
+ for lemma, forms in table.items():
82
+ head = cut(lemma)
83
+ entire = len(head) == 1
84
+ whole += entire
85
+ for form in forms:
86
+ piece = cut(form)
87
+ keeps = len(piece) >= len(head) and piece[:len(head)] == head
88
+ shared += keeps
89
+ rows += keeps and entire
90
+ cost += len(piece)
91
+ print(f"{name:32s} {rows/pairs:8.1%} {shared/pairs:8.1%} "
92
+ f"{whole/len(table):8.1%} {cost/pairs:9.2f}")
93
+
94
+
95
+ def fertility():
96
+ """Characters a token buys, on a fixed prefix of each dataset."""
97
+ loaded = load()
98
+ for repo, config, folder, column in CORPORA:
99
+ stream = load_dataset(repo, config, data_dir=folder, split="train", streaming=True)
100
+ texts = []
101
+ for row in stream:
102
+ text = (row.get(column) or "").strip()
103
+ if len(text) >= MIN_CHARS:
104
+ texts.append(unicodedata.normalize("NFC", text))
105
+ if len(texts) >= DOCUMENTS:
106
+ break
107
+ chars = sum(map(len, texts))
108
+ words = [w for t in texts for w in WORD.findall(t)]
109
+ print(f"\n{repo} {len(texts):,} documents, {chars/1e6:.2f} Mchar")
110
+ for name, tok in loaded.items():
111
+ total = sum(len(tok.encode(t, add_special_tokens=False)) for t in texts)
112
+ cost = {w: len(tok.encode(" " + w, add_special_tokens=False)) for w in set(words)}
113
+ counts = [cost[w] for w in words]
114
+ print(f" {name:32s} {chars/total:6.3f} chars/token "
115
+ f"{sum(counts)/len(counts):4.2f} pieces/word "
116
+ f"{sum(n >= 4 for n in counts)/len(counts):5.1%} need 4+")
117
+
118
+
119
+ if __name__ == "__main__":
120
+ wanted = sys.argv[1] if len(sys.argv) > 1 else "all"
121
+ if wanted in ("all", "paradigms"):
122
+ paradigms()
123
+ if wanted in ("all", "fertility"):
124
+ fertility()
ties.json CHANGED
@@ -1 +1 @@
1
- {"832": 289, "881": 1507, "1036": 363, "1430": 33281, "1464": 1264, "1836": 1427, "2002": 1427, "2104": 1610, "2127": 555, "2927": 393, "3073": 868, "3300": 2543, "3573": 2520, "3776": 555, "3946": 555, "4080": 2826, "4085": 1264, "4229": 6547, "4580": 2257, "4651": 1023, "4834": 3774, "4882": 3546, "5204": 5108, "5573": 44976, "5643": 4760, "5992": 4828, "6110": 2826, "6128": 621, "6391": 1722, "6717": 1264, "6760": 4760, "7534": 345, "7739": 2210, "8006": 3268, "8129": 4590, "8315": 5640, "8488": 2197, "8640": 7439, "8724": 3063, "8833": 6105, "8900": 4167, "8903": 8646, "9102": 594, "9173": 7271, "9395": 7952, "9452": 5057, "9502": 5143, "9509": 1127, "9521": 1388, "9662": 6302, "9816": 8646, "9893": 1678, "10073": 2281, "10210": 7343, "10307": 8042, "10433": 8646, "10434": 5423, "10527": 10361, "10628": 1989, "10671": 723, "10879": 8898, "10937": 5128, "11113": 2126, "11506": 2102, "11582": 9473, "11618": 2826, "11882": 4760, "12155": 4986, "12475": 6823, "12527": 17365, "12687": 8917, "12718": 10840, "12842": 651, "12894": 9593, "13191": 21879, "13237": 10739, "13250": 8949, "13316": 11133, "13359": 10341, "13634": 8221, "13807": 39934, "13909": 16366, "14253": 10824, "14263": 8773, "14649": 4590, "14657": 9846, "14907": 12891, "14957": 6069, "14997": 4590, "15007": 9794, "15053": 10819, "15137": 10915, "15204": 34357, "15221": 6474, "15265": 1118, "15430": 4893, "15731": 8841, "15839": 2302, "15910": 3492, "16522": 2893, "16550": 5206, "16562": 4655, "16609": 36674, "16685": 8898, "16816": 2653, "16941": 2137, "16990": 16847, "16994": 9473, "17108": 21949, "17126": 3093, "17266": 28660, "17364": 13208, "17462": 1338, "17839": 9672, "17934": 6740, "18070": 16802, "18080": 8465, "18082": 19743, "18406": 1425, "18520": 16034, "18766": 7389, "18936": 30722, "19215": 30263, "19340": 8646, "19438": 4434, "19469": 3091, "19653": 3415, "19697": 40396, "19702": 9492, "19734": 18767, "19815": 2281, "19839": 2543, "19867": 17024, "20101": 4089, "20185": 2985, "20248": 27793, "20317": 44763, "20392": 21811, "20778": 1221, "20823": 8898, "20934": 6816, "20942": 14127, "21037": 26418, "21056": 30456, "21133": 823, "21232": 33309, "21251": 27899, "21322": 39063, "21622": 5108, "21950": 14062, "21966": 8797, "22105": 1599, "22134": 22031, "22136": 6087, "22164": 18973, "22379": 1290, "22417": 6081, "22486": 26600, "22508": 30386, "22530": 4844, "22568": 18288, "22606": 5780, "22658": 14704, "23156": 555, "23228": 1610, "23317": 13333, "23420": 10755, "23527": 11361, "23542": 12561, "23543": 11709, "23644": 19396, "23657": 41454, "24164": 28112, "24276": 23385, "24596": 15987, "24634": 46767, "24861": 6740, "24881": 35159, "24893": 15829, "24924": 40261, "24942": 30836, "25194": 21015, "25402": 15082, "25710": 3320, "25772": 5647, "25825": 4761, "25892": 4344, "26025": 26591, "26081": 1498, "26087": 13705, "26269": 20956, "26290": 32329, "26310": 26484, "26361": 18185, "26392": 34852, "26419": 30692, "26420": 5128, "26455": 36972, "26459": 35515, "26463": 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tokenizer.json CHANGED
@@ -4815,10 +4815,6 @@
4815
  "El",
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  -9.42580795288086
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  [
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  "son",
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  -9.426764488220217
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  "▁mögé",
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  -11.24395751953125
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  ],
 
 
 
 
24006
  [
24007
  "▁harag",
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  -11.244222640991213
@@ -24215,6 +24215,10 @@
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  "ille",
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  -11.251391410827637
24217
  ],
 
 
 
 
24218
  [
24219
  "ern",
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  -11.251664161682127
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  "▁biztosíték",
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  -11.594255447387695
34189
  ],
 
 
 
 
34190
  [
34191
  "▁matematika",
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  -11.594395637512209
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  "▁equations",
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  -11.60633659362793
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  ],
 
 
 
 
34574
  [
34575
  "▁báb",
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  -11.606508255004885
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  "▁lapos",
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  -11.629714012145996
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  ],
 
 
 
 
35474
  [
35475
  "▁stílusú",
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  -11.62983512878418
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  "▁kór",
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  -11.832013130187988
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  ],
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  [
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  "▁bánat",
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  -11.832143783569336
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  "ablak",
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  -11.864163398742676
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  ],
 
 
 
 
44578
  [
44579
  "papír",
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  -11.86447811126709
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  "▁merész",
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  -11.943978309631348
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  ],
 
 
 
 
48142
  [
48143
  "segít",
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  -11.944091796875
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  "▁füzet",
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  -11.98135471343994
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  ],
 
 
 
 
49902
  [
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  "Cache",
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  -11.981419563293455
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  "▁keresztyén",
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  -11.983893394470217
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  ],
 
 
 
 
49994
  [
49995
  "dj",
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  -11.983988761901855
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63099
  "▁Sto",
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  -12.2269868850708
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  ],
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63103
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  [
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  "▁Újabb",
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  -12.227142333984377
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  "▁problémákkal",
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  -12.390637397766112
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  ],
 
 
 
 
73642
  [
73643
  "▁Közlönyben",
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  -12.390740394592283
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  "▁kiadásának",
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  -12.500760078430176
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  ],
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- "▁kivet",
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- ],
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  [
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  "tornyot",
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  -12.500781059265137
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  "▁pajtás",
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  -12.52593994140625
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  ],
 
 
 
 
83306
  [
83307
  "▁ph",
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  -12.526006698608398
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  "▁mosó",
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  -12.59128189086914
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  ],
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88455
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- ],
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  [
88459
  "állású",
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  -12.591341018676758
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90623
  "▁Rezső",
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  -12.619868278503418
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  ],
 
 
 
 
90626
  [
90627
  "▁session",
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  -12.619983673095703
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  "dátum",
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  -12.648262977600098
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  ],
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- "vei",
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- ],
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  [
92959
  "▁bájos",
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  -12.648475646972656
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100511
  "juttat",
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  -12.739002227783203
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  ],
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- "▁levet",
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  [
100519
  "ettél",
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  -12.739171981811523
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  "▁fejlődéséhez",
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  -12.766057014465332
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  ],
 
 
 
 
102798
  [
102799
  "▁nad",
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  -12.76621913909912
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104299
  "átalány",
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  -12.784199714660645
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  ],
 
 
 
 
104302
  [
104303
  "geo",
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  -12.78428840637207
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105435
  "öblö",
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  -12.796637535095217
105437
  ],
 
 
 
 
 
 
 
 
 
 
 
 
105438
  [
105439
  "▁polgárság",
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  -12.796771049499512
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105443
  "▁deficit",
105444
  -12.796833038330078
105445
  ],
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
105446
  [
105447
  "▁pleisztocén",
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  -12.797074317932127
105449
  ],
 
 
 
 
105450
  [
105451
  "▁helyesbít",
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  -12.797161102294922
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  ],
 
 
 
 
105454
  [
105455
  "▁ler",
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  -12.797283411026
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107579
  "▁sch",
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  -12.899477243423462
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107583
- "Hercegovina",
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- -12.899508476257324
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  [
107587
  "▁kegyelet",
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  -12.899538040161133
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109915
  "▁élvező",
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  -12.992772102355955
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  ],
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- "▁veszte",
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  [
109923
  "▁kvalitatív",
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  -12.993864059448242
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110439
  "▁hárs",
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  -13.013855934143066
110441
  ],
 
 
 
 
110442
  [
110443
  "▁foszt",
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  -13.013909339904783
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111795
  "▁közalapítvány",
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  -13.075096130371094
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  ],
 
 
 
 
111798
  [
111799
  "▁stimmel",
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  -13.075130462646484
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117179
  "▁paradox",
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  -13.302289009094238
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  ],
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117183
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  [
117187
  "▁önrendelkezés",
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  -13.303096771240234
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119775
  "▁önköltség",
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  -13.416337013244627
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  ],
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- [
119779
- "Mell",
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  [
119783
  "▁albizottság",
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  -13.4166841506958
@@ -125859,10 +125919,6 @@
125859
  "Shaw",
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  -13.706841468811035
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  ],
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125863
- "tték",
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- -13.70687198638916
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- ],
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  [
125867
  "▁kormánykoalíció",
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  -13.706917762756348
@@ -128235,6 +128291,10 @@
128235
  "▁mikroprocesszor",
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  -13.830272674560549
128237
  ],
 
 
 
 
128238
  [
128239
  "▁gyógyíthatatlan",
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  -13.830327987670898
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128447
  "▁harcjármű",
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  -13.841219902038574
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- [
128451
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  [
128455
  "▁lott",
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@@ -131383,6 +131439,10 @@
131383
  "▁pon",
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  -14.188208818435667
131385
  ],
 
 
 
 
131386
  [
131387
  "▁п",
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  -14.190243005752563
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132623
  "▁Policy",
132624
  -14.479191064834597
132625
  ],
 
 
 
 
132626
  [
132627
  "▁metszés",
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  -14.48103165626526
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140735
  "▁packet",
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  -15.4236261844635
140737
  ],
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140739
- "▁Found",
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- -15.424251794815063
140741
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  [
140743
  "▁Lám",
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  -15.42477250099182
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143171
  "▁kedvű",
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  -15.632192850112917
143173
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143175
- "vük",
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  [
143179
  "▁uszodá",
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@@ -148599,10 +148655,6 @@
148599
  "▁légies",
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  -16.631510257720947
148601
  ],
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- [
148603
- "vön",
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  [
148607
  "▁apród",
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@@ -156399,6 +156451,10 @@
156399
  "▁elnöklet",
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  -18.661882400512695
156401
  ],
 
 
 
 
156402
  [
156403
  "▁titkosság",
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  -18.664186477661133
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160283
  "……",
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  -19.37184715270996
160285
  ],
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160287
- "▁szórakozta",
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  [
160291
  "▁őszül",
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  -19.372026443481445
@@ -164375,10 +164427,6 @@
164375
  "▁becsületesség",
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  -19.964774131774902
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  ],
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164379
- "▁foglalkozta",
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- -19.9653377532959
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  [
164383
  "▁szende",
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  -19.96594524383545
@@ -167811,10 +167859,6 @@
167811
  "▁vértanúság",
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  -20.43210220336914
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  ],
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- [
167815
- "▁öltözte",
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  [
167819
  "▁beltér",
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  -20.43252944946289
@@ -169011,10 +169055,6 @@
169011
  "▁kisbetűs",
169012
  -20.602667808532715
169013
  ],
169014
- [
169015
- "▁párosodás",
169016
- -20.60340118408203
169017
- ],
169018
  [
169019
  "▁etnikumú",
169020
  -20.603835105895996
@@ -175335,10 +175375,6 @@
175335
  "▁Helyzet",
175336
  -21.490195274353027
175337
  ],
175338
- [
175339
- "▁kamatozta",
175340
- -21.49085807800293
175341
- ],
175342
  [
175343
  "▁modernizál",
175344
  -21.490891456604004
@@ -175815,10 +175851,6 @@
175815
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175816
  -21.556296348571777
175817
  ],
175818
- [
175819
- "▁káprázta",
175820
- -21.55659770965576
175821
- ],
175822
  [
175823
  "▁nyelvtanuló",
175824
  -21.557196617126465
@@ -176659,10 +176691,6 @@
176659
  "▁táncszínház",
176660
  -21.6918363571167
176661
  ],
176662
- [
176663
- "▁nyilatkozn",
176664
- -21.69204354286194
176665
- ],
176666
  [
176667
  "▁ötletgazda",
176668
  -21.692448616027832
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180171
  "▁lévita",
180172
  -22.398947715759277
180173
  ],
180174
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180175
- "▁emlékkö",
180176
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180177
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180178
  [
180179
  "▁gázelosztó",
180180
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182287
  "▁nyugodalm",
182288
  -22.94628143310547
182289
  ],
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182291
- "▁mérkőzhe",
182292
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182293
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182294
  [
182295
  "▁sárgásfehér",
182296
  -22.94734477996826
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184979
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184980
  -23.895301818847656
184981
  ],
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184983
- "▁közjav",
184984
- -23.89534568786621
184985
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184986
  [
184987
  "▁dumping",
184988
  -23.896888494491577
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185231
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185232
  -24.020570278167725
185233
  ],
185234
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185235
- "▁nanocsö",
185236
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185237
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  [
185239
  "▁villog",
185240
  -24.02237629890442
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189783
  "▁nagyérdemű",
189784
  -27.71691608428955
189785
  ],
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189787
- "▁számkivet",
189788
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189789
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  [
189791
  "▁szemantika",
189792
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190479
  "▁vetkőz",
190480
  -28.36702013015747
190481
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190482
  [
190483
  "▁brácsá",
190484
  -28.372023582458496
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191331
  "▁ágostai",
191332
  -29.238428592681885
191333
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- [
191335
- "▁támolyg",
191336
- -29.2506000995636
191337
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  [
191339
  "▁pubertás",
191340
  -29.259851455688477
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191711
  "▁csurgó",
191712
  -29.53585386276245
191713
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191715
- "▁vánszorg",
191716
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191717
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  [
191719
  "▁hipermarket",
191720
  -29.539283752441406
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194107
  "▁hajmeresztő",
194108
  -32.16242980957031
194109
  ],
194110
- [
194111
- "▁ikonosztáz",
194112
- -32.17224884033203
194113
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194114
  [
194115
  "▁garmadá",
194116
  -32.17611312866211
 
4815
  "El",
4816
  -9.42580795288086
4817
  ],
 
 
 
 
4818
  [
4819
  "son",
4820
  -9.426764488220217
 
23999
  "▁mögé",
24000
  -11.24395751953125
24001
  ],
24002
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24003
+ "▁felvett",
24004
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24005
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24006
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24007
  "▁harag",
24008
  -11.244222640991213
 
24215
  "ille",
24216
  -11.251391410827637
24217
  ],
24218
+ [
24219
+ "▁művei",
24220
+ -11.251520156860352
24221
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24222
  [
24223
  "ern",
24224
  -11.251664161682127
 
34191
  "▁biztosíték",
34192
  -11.594255447387695
34193
  ],
34194
+ [
34195
+ "gott",
34196
+ -11.59428596496582
34197
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34198
  [
34199
  "▁matematika",
34200
  -11.594395637512209
 
34579
  "▁equations",
34580
  -11.60633659362793
34581
  ],
34582
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34583
+ "vai",
34584
+ -11.606507301330566
34585
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34586
  [
34587
  "▁báb",
34588
  -11.606508255004885
 
35483
  "▁lapos",
35484
  -11.629714012145996
35485
  ],
35486
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35487
+ "▁falvak",
35488
+ -11.629815101623535
35489
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  [
35491
  "▁stílusú",
35492
  -11.62983512878418
 
43307
  "▁kór",
43308
  -11.832013130187988
43309
  ],
 
 
 
 
43310
  [
43311
  "▁bánat",
43312
  -11.832143783569336
 
44587
  "ablak",
44588
  -11.864163398742676
44589
  ],
44590
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44591
+ "▁felvette",
44592
+ -11.864184379577637
44593
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44594
  [
44595
  "papír",
44596
  -11.86447811126709
 
48155
  "▁merész",
48156
  -11.943978309631348
48157
  ],
48158
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48159
+ "▁kivett",
48160
+ -11.944040298461914
48161
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48162
  [
48163
  "segít",
48164
  -11.944091796875
 
49919
  "▁füzet",
49920
  -11.98135471343994
49921
  ],
49922
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49923
+ "vát",
49924
+ -11.981390953063965
49925
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49926
  [
49927
  "Cache",
49928
  -11.981419563293455
 
50015
  "▁keresztyén",
50016
  -11.983893394470217
50017
  ],
50018
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50019
+ "▁művet",
50020
+ -11.983941078186035
50021
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50022
  [
50023
  "dj",
50024
  -11.983988761901855
 
63127
  "▁Sto",
63128
  -12.2269868850708
63129
  ],
 
 
 
 
63130
  [
63131
  "▁Újabb",
63132
  -12.227142333984377
 
73663
  "▁problémákkal",
73664
  -12.390637397766112
73665
  ],
73666
+ [
73667
+ "▁elvitte",
73668
+ -12.39071273803711
73669
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73670
  [
73671
  "▁Közlönyben",
73672
  -12.390740394592283
 
81479
  "▁kiadásának",
81480
  -12.500760078430176
81481
  ],
 
 
 
 
81482
  [
81483
  "tornyot",
81484
  -12.500781059265137
 
83327
  "▁pajtás",
83328
  -12.52593994140625
83329
  ],
83330
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83331
+ "▁mosolygott",
83332
+ -12.525944709777832
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83334
  [
83335
  "▁ph",
83336
  -12.526006698608398
 
88479
  "▁mosó",
88480
  -12.59128189086914
88481
  ],
 
 
 
 
88482
  [
88483
  "állású",
88484
  -12.591341018676758
 
90647
  "▁Rezső",
90648
  -12.619868278503418
90649
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90650
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90651
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90652
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90654
  [
90655
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90656
  -12.619983673095703
 
92979
  "dátum",
92980
  -12.648262977600098
92981
  ],
 
 
 
 
92982
  [
92983
  "▁bájos",
92984
  -12.648475646972656
 
100535
  "juttat",
100536
  -12.739002227783203
100537
  ],
 
 
 
 
100538
  [
100539
  "ettél",
100540
  -12.739171981811523
 
102815
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102816
  -12.766057014465332
102817
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102818
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102819
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102822
  [
102823
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102824
  -12.76621913909912
 
104323
  "átalány",
104324
  -12.784199714660645
104325
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104326
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104327
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104331
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104332
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105463
  "öblö",
105464
  -12.796637535095217
105465
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105466
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105467
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105470
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105471
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105472
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105473
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105474
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105475
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105476
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105477
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105478
  [
105479
  "▁polgárság",
105480
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105483
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105484
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105485
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105486
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105487
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105491
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105495
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105498
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105499
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105502
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105503
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105506
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105507
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105508
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105509
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105510
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105511
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105514
  [
105515
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105516
  -12.797161102294922
105517
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105518
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105519
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105520
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105521
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105522
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105523
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105524
  -12.797283411026
 
107647
  "▁sch",
107648
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107649
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107650
  [
107651
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107652
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109979
  "▁élvező",
109980
  -12.992772102355955
109981
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109982
  [
109983
  "▁kvalitatív",
109984
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110499
  "▁hárs",
110500
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110501
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110502
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110503
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110504
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110505
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110506
  [
110507
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110508
  -13.013909339904783
 
111859
  "▁közalapítvány",
111860
  -13.075096130371094
111861
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111862
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111863
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111864
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111865
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111866
  [
111867
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111868
  -13.075130462646484
 
117247
  "▁paradox",
117248
  -13.302289009094238
117249
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117250
  [
117251
  "▁önrendelkezés",
117252
  -13.303096771240234
 
119839
  "▁önköltség",
119840
  -13.416337013244627
119841
  ],
 
 
 
 
119842
  [
119843
  "▁albizottság",
119844
  -13.4166841506958
 
125919
  "Shaw",
125920
  -13.706841468811035
125921
  ],
 
 
 
 
125922
  [
125923
  "▁kormánykoalíció",
125924
  -13.706917762756348
 
128291
  "▁mikroprocesszor",
128292
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128293
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128294
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128295
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128298
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128299
  "▁gyógyíthatatlan",
128300
  -13.830327987670898
 
128507
  "▁harcjármű",
128508
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128509
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128510
  [
128511
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128512
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131439
  "▁pon",
131440
  -14.188208818435667
131441
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131442
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131443
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131446
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131447
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131448
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132683
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132684
  -14.479191064834597
132685
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132686
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132687
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132688
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132690
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132691
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132692
  -14.48103165626526
 
140799
  "▁packet",
140800
  -15.4236261844635
140801
  ],
 
 
 
 
140802
  [
140803
  "▁Lám",
140804
  -15.42477250099182
 
143231
  "▁kedvű",
143232
  -15.632192850112917
143233
  ],
 
 
 
 
143234
  [
143235
  "▁uszodá",
143236
  -15.63322377204895
 
148655
  "▁légies",
148656
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148657
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148658
  [
148659
  "▁apród",
148660
  -16.63312005996704
 
156451
  "▁elnöklet",
156452
  -18.661882400512695
156453
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156454
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156455
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156458
  [
156459
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156460
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160339
  "……",
160340
  -19.37184715270996
160341
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160342
  [
160343
  "▁őszül",
160344
  -19.372026443481445
 
164427
  "▁becsületesség",
164428
  -19.964774131774902
164429
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164430
  [
164431
  "▁szende",
164432
  -19.96594524383545
 
167859
  "▁vértanúság",
167860
  -20.43210220336914
167861
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167862
  [
167863
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167864
  -20.43252944946289
 
169055
  "▁kisbetűs",
169056
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169057
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169058
  [
169059
  "▁etnikumú",
169060
  -20.603835105895996
 
175375
  "▁Helyzet",
175376
  -21.490195274353027
175377
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175378
  [
175379
  "▁modernizál",
175380
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175851
  "▁szezonzáró",
175852
  -21.556296348571777
175853
  ],
 
 
 
 
175854
  [
175855
  "▁nyelvtanuló",
175856
  -21.557196617126465
 
176691
  "▁táncszínház",
176692
  -21.6918363571167
176693
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176694
  [
176695
  "▁ötletgazda",
176696
  -21.692448616027832
 
180199
  "▁lévita",
180200
  -22.398947715759277
180201
  ],
 
 
 
 
180202
  [
180203
  "▁gázelosztó",
180204
  -22.40288543701172
 
182311
  "▁nyugodalm",
182312
  -22.94628143310547
182313
  ],
 
 
 
 
182314
  [
182315
  "▁sárgásfehér",
182316
  -22.94734477996826
 
184999
  "▁zárvatermő",
185000
  -23.895301818847656
185001
  ],
 
 
 
 
185002
  [
185003
  "▁dumping",
185004
  -23.896888494491577
 
185247
  "▁klikk",
185248
  -24.020570278167725
185249
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185250
  [
185251
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185252
  -24.02237629890442
 
189795
  "▁nagyérdemű",
189796
  -27.71691608428955
189797
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189798
  [
189799
  "▁szemantika",
189800
  -27.72935438156128
 
190487
  "▁vetkőz",
190488
  -28.36702013015747
190489
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190490
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190491
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190495
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190496
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191343
  "▁ágostai",
191344
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191345
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191346
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191347
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191348
  -29.259851455688477
 
191719
  "▁csurgó",
191720
  -29.53585386276245
191721
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191722
  [
191723
  "▁hipermarket",
191724
  -29.539283752441406
 
194111
  "▁hajmeresztő",
194112
  -32.16242980957031
194113
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194114
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194115
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194116
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