diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index b608b64..c3b58dc 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -35,9 +35,10 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         python -m pip install ".[test,tutorial]"
-    - name: Check pep8 with flake8
+    - name: Lint with ruff
       run: |
-        flake8 regexmodel --max-line-length 100
+        ruff regexmodel
+
     - name: Lint with pylint
       run: |
         pylint regexmodel
diff --git a/.gitignore b/.gitignore
index d6e5900..7758e5e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,6 +25,7 @@ share/python-wheels/
 .installed.cfg
 *.egg
 MANIFEST
+regexmodel/_version.py
 
 # PyInstaller
 #  Usually these files are written by a python script from a template
@@ -161,4 +162,4 @@ cython_debug/
 
 # pyenv
 
-.python-version
\ No newline at end of file
+.python-version
diff --git a/benchmarks/accurate/avg_log_like_pc_success.csv b/benchmarks/accurate/avg_log_like_pc_success.csv
index 1dd9536..85fb55b 100644
--- a/benchmarks/accurate/avg_log_like_pc_success.csv
+++ b/benchmarks/accurate/avg_log_like_pc_success.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,-2.1915454889405193,-2.085692682375947,-2.18280418271571,-2.1232970908249675,-2.096331377349144
-address,-2.8283862849587535,-1.4533890029216738,-2.0336731390272353,-1.925900279810606,-2.759496162520807
-phone_number,-1.7688013889196164,-1.5607712942902165,-2.1163970594677974,-1.749678224572948,-2.001020843204409
+average,-2.608543390685392,-2.5540763210301898,-2.5856597956220946,-2.5692086137569947,-2.422232885238715
+address,-3.18723924481244,-3.887090673243783,-2.785144772943876,-3.566546641315572,-3.131905640139533
+phone_number,-2.420385889036475,-1.5785009699055097,-2.2114432381512685,-2.1368741178333464,-2.049223740657882
 pricetag,-1.7923231995145659,-1.5760340411557583,-1.7923231995145659,-1.5760340411557583,-1.2696132934896613
-timezone,-1.897851672904426,-1.897851672904426,-1.897851672904426,-1.897851672904426,-1.897851672904426
-mime_type,-2.494969163134166,-2.494969163134166,-2.494969163134166,-2.494969163134166,-2.494969163134166
-unix_partition,-0.8128951812359995,-0.8128951812359995,-0.8128951812359995,-0.8128951812359995,-0.8128951812359995
-ascii_email,-2.8487401923062,-2.7446657828270458,-2.806944919984864,-2.8093778066649575,-2.7801893522322323
-isbn10,-1.885582753251952,-1.885582753251952,-1.885582753251952,-1.885582753251952,-1.885582753251952
-job,-3.2825423498046944,-3.3197176489230835,-3.2825423498046944,-3.512404371144477,-3.60416826076485
+timezone,-2.968113041245333,-2.968113041245333,-2.968113041245333,-2.968113041245333,-2.968113041245333
+mime_type,-3.214729512361329,-3.214729512361329,-3.214729512361329,-3.214729512361329,-3.214729512361329
+unix_partition,-1.0229921882996647,-1.0229921882996647,-1.0229921882996647,-1.0229921882996647,-1.0229921882996647
+ascii_email,-2.932696888699675,-3.086622584650391,-2.848214128255551,-3.1044451170140266,-2.8450015512821145
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 ssn,-2.302585092994053,-2.3173140261754446,-1.883933257904226,-1.883933257904226,-1.883933257904226
-user_agent,-0.2602482506708753,-0.2602482506708753,-0.2602482506708753,-0.2602482506708753,-0.2602482506708753
-color,-3.07863311203932,-3.07863311203932,-3.07863311203932,-3.07863311203932,-3.07863311203932
-license_plate,-2.470697252907526,-2.2421505394955856,-3.256390150685296,-2.66214645531138,-2.0032353934459195
+user_agent,-3.0023386522052933,-3.0023386522052933,-3.0023386522052933,-3.0023386522052933,-3.0023386522052933
+color,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406
+license_plate,-2.795026079321331,-2.3157898133188928,-3.918688389538393,-2.6225242552034,-2.0032353934459195
 iban,-2.2590781815563674,-2.1320232342537286,-2.266988529090495,-2.0932591754491296,-2.072326583694648
-company,-2.955044174105518,-3.0906967602544735,-2.7090592212311626,-3.070200644452302,-2.135437650751123
-time,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485
-ipv4,-2.078159173483116,-2.078159173483116,-2.078159173483116,-2.078159173483116,-2.078159173483116
-uri,-1.6733102367998156,-1.745111628166589,-1.74863506343372,-1.723981947398809,-1.7329466523265975
-name,-3.1991469206356564,-3.187577991312297,-3.3176835660873323,-3.077019506442681,-3.329219703932165
+company,-3.7194810482582334,-3.126868525309265,-3.111995068243158,-3.5006759708568267,-2.5272771610635956
+time,-1.736774924512717,-1.736774924512717,-1.736774924512717,-1.736774924512717,-1.736774924512717
+ipv4,-2.078583243995832,-2.078583243995832,-2.078583243995832,-2.078583243995832,-2.078583243995832
+uri,-2.89706171157613,-2.898724633175328,-2.925111627052448,-2.912835039637672,-2.917546034331703
+name,-3.1906492941234186,-3.5292735549729537,-3.3178961129960527,-3.489180792006465,-3.3329506153268027
diff --git a/benchmarks/accurate/avg_log_like_per_char.csv b/benchmarks/accurate/avg_log_like_per_char.csv
index 22ff777..16eb5b3 100644
--- a/benchmarks/accurate/avg_log_like_per_char.csv
+++ b/benchmarks/accurate/avg_log_like_per_char.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,-3.2938598343934418,-3.3555745632366025,-3.316232988868926,-3.270966353500831,-3.1062455436565832
-address,-3.834188626954746,-6.302684528850788,-4.176094603259543,-5.305111907631831,-4.555830173227214
-phone_number,-2.2295751913746455,-1.6323777437240714,-3.044748889264162,-2.416276680580892,-2.2073134372665324
+average,-3.235088778489685,-3.2739489408299,-3.235365149011781,-3.1794819021421303,-3.0369287434358316
+address,-3.840148579218316,-6.082348486264795,-3.703915298159491,-5.079471629783415,-4.642334566045615
+phone_number,-2.519394413249677,-1.6605009305061085,-3.06802094516657,-2.4578177002378103,-2.250806260046024
 pricetag,-1.857589571606867,-1.644179768981727,-1.857589571606867,-1.644179768981727,-1.3418765920395992
-timezone,-3.557125384790429,-3.557125384790429,-3.557125384790429,-3.557125384790429,-3.557125384790429
-mime_type,-4.309785403364651,-4.309785403364651,-4.309785403364651,-4.309785403364651,-4.309785403364651
-unix_partition,-0.8128951812359995,-0.8128951812359995,-0.8128951812359995,-0.8128951812359995,-0.8128951812359995
-ascii_email,-3.955438091483059,-3.6149673949775107,-3.6707139359652112,-3.5748414604300818,-3.4054001931982816
-isbn10,-2.406411205324038,-2.406411205324038,-2.406411205324038,-2.406411205324038,-2.406411205324038
-job,-4.884924919736532,-5.705984913662813,-4.884924919736532,-3.6686003606586874,-4.595367802147032
+timezone,-3.5919927903318856,-3.5919927903318856,-3.5919927903318856,-3.5919927903318856,-3.5919927903318856
+mime_type,-3.92647728939957,-3.92647728939957,-3.92647728939957,-3.92647728939957,-3.92647728939957
+unix_partition,-1.0229921882996647,-1.0229921882996647,-1.0229921882996647,-1.0229921882996647,-1.0229921882996647
+ascii_email,-3.9691136015503687,-3.582655346564504,-3.6927285228324136,-3.547302937885057,-3.450809295007938
+isbn10,-2.1910620580931046,-2.1910620580931046,-2.1910620580931046,-2.1910620580931046,-2.1910620580931046
+job,-4.838872161824781,-5.701827690566834,-4.838872161824781,-3.6669438767303433,-4.563536384448202
 ssn,-2.302585092994053,-2.3173140261754446,-1.883933257904226,-1.883933257904226,-1.883933257904226
-user_agent,-5.6441974842594185,-5.6441974842594185,-5.6441974842594185,-5.6441974842594185,-5.6441974842594185
-color,-3.07863311203932,-3.07863311203932,-3.07863311203932,-3.07863311203932,-3.07863311203932
-license_plate,-2.6478583661828106,-2.2421505394955856,-4.165539752656556,-2.884306282078523,-2.0032353934459195
+user_agent,-5.240401175100682,-5.240401175100682,-5.240401175100682,-5.240401175100682,-5.240401175100682
+color,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406
+license_plate,-2.9795090041929244,-2.3157898133188928,-4.440001467355454,-2.838396764558982,-2.0032353934459195
 iban,-2.2590781815563674,-2.1320232342537286,-2.266988529090495,-2.0932591754491296,-2.072326583694648
-company,-4.934710446847975,-4.300955126886775,-3.8023309055353445,-4.852974952335046,-3.340791390626175
-time,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485
-ipv4,-2.26972756360758,-2.26972756360758,-2.26972756360758,-2.26972756360758,-2.26972756360758
-uri,-5.067197884807189,-5.04363112749814,-5.582602500792118,-5.04202573068279,-5.21323789991221
-name,-4.781045436662459,-4.9905032537201865,-3.8438148794298477,-4.953706096514169,-4.5702075627445655
+company,-4.8499532026595595,-4.211619416906417,-3.805334120230944,-4.725582855110774,-3.288857834461055
+time,-1.872908543697085,-1.872908543697085,-1.872908543697085,-1.872908543697085,-1.872908543697085
+ipv4,-2.2634526769570096,-2.2634526769570096,-2.2634526769570096,-2.2634526769570096,-2.2634526769570096
+uri,-4.857727899651415,-5.031836748733662,-5.489040615715497,-4.849665855189024,-5.059584335510611
+name,-4.602661285487342,-4.9348806161836505,-3.835459544024767,-5.03354852155765,-4.554291825364615
diff --git a/benchmarks/accurate/benchmark.json b/benchmarks/accurate/benchmark.json
index 8b764fa..ce014c7 100644
--- a/benchmarks/accurate/benchmark.json
+++ b/benchmarks/accurate/benchmark.json
@@ -1 +1 @@
-{"address": {"nl": [{"failed": 16, "success": 84, "n_tot_char": 2994, "n_char_success": 2474, "n_parameters": 21, "avg_log_like_per_char": -3.643766821332279, "avg_log_like_pc_success": -2.9577223597405533, "n_fake": 100, "threshold": 2, "success_rate": 0.84, "fit_time": 0.8878285884857178, "statistics_time": 0.08623123168945312}, {"failed": 25, "success": 75, "n_tot_char": 2994, "n_char_success": 2162, "n_parameters": 12, "avg_log_like_per_char": -4.03916537434355, "avg_log_like_pc_success": -2.935249185324443, "n_fake": 100, "threshold": 5, "success_rate": 0.75, "fit_time": 0.11491894721984863, "statistics_time": 0.05713820457458496}, {"failed": 28, "success": 72, "n_tot_char": 2994, "n_char_success": 2064, "n_parameters": 12, "avg_log_like_per_char": -4.162271601803475, "avg_log_like_pc_success": -2.925207735632858, "n_fake": 100, "threshold": 10, "success_rate": 0.72, "fit_time": 0.08681464195251465, "statistics_time": 0.0544438362121582}, {"failed": 100, "success": 0, "n_tot_char": 2994, "n_char_success": 0, "n_parameters": 2, "avg_log_like_per_char": -6.907755278982131, "avg_log_like_pc_success": 0.0, "n_fake": 100, "threshold": 20, "success_rate": 0.0, "fit_time": 0.46962809562683105, "statistics_time": 0.0022001266479492188}, {"failed": 19, "success": 181, "n_tot_char": 6035, "n_char_success": 5390, "n_parameters": 39, "avg_log_like_per_char": -3.4025234921604053, "avg_log_like_pc_success": -2.9830662560750554, "n_fake": 200, "threshold": 2, "success_rate": 0.905, "fit_time": 0.9591786861419678, "statistics_time": 0.2313823699951172}, {"failed": 31, "success": 169, "n_tot_char": 6035, "n_char_success": 4967, "n_parameters": 12, "avg_log_like_per_char": -3.6590387690424935, "avg_log_like_pc_success": -2.9605025836961016, "n_fake": 200, "threshold": 5, "success_rate": 0.845, "fit_time": 0.23243236541748047, "statistics_time": 0.14243817329406738}, {"failed": 49, "success": 151, "n_tot_char": 6035, "n_char_success": 4382, "n_parameters": 12, 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"n_fake": 1000, "threshold": 2, "success_rate": 0.925, "fit_time": 2.0602855682373047, "statistics_time": 0.6113255023956299}, {"failed": 140, "success": 860, "n_tot_char": 18467, "n_char_success": 15218, "n_parameters": 101, "avg_log_like_per_char": -3.9182631384284785, "avg_log_like_pc_success": -3.2800150135330313, "n_fake": 1000, "threshold": 5, "success_rate": 0.86, "fit_time": 1.0102641582489014, "statistics_time": 0.32068586349487305}, {"failed": 172, "success": 828, "n_tot_char": 18467, "n_char_success": 14607, "n_parameters": 79, "avg_log_like_per_char": -3.987639665994121, "avg_log_like_pc_success": -3.2159790740769636, "n_fake": 1000, "threshold": 10, "success_rate": 0.828, "fit_time": 0.6073391437530518, "statistics_time": 0.29294848442077637}, {"failed": 378, "success": 622, "n_tot_char": 18467, "n_char_success": 10212, "n_parameters": 39, "avg_log_like_per_char": -4.889002525577047, "avg_log_like_pc_success": -3.257118077931203, "n_fake": 1000, "threshold": 20, "success_rate": 0.622, "fit_time": 0.4835183620452881, "statistics_time": 0.15954327583312988}], "da": [{"failed": 28, "success": 72, "n_tot_char": 1704, "n_char_success": 1121, "n_parameters": 37, "avg_log_like_per_char": -4.548170847852593, "avg_log_like_pc_success": -3.3210185522696114, "n_fake": 100, "threshold": 2, "success_rate": 0.72, "fit_time": 0.5712158679962158, "statistics_time": 0.04795384407043457}, {"failed": 43, "success": 57, "n_tot_char": 1704, "n_char_success": 851, "n_parameters": 21, "avg_log_like_per_char": -5.111789198608479, "avg_log_like_pc_success": -3.31160228138318, "n_fake": 100, "threshold": 5, "success_rate": 0.57, "fit_time": 0.4166533946990967, "statistics_time": 0.03882551193237305}, {"failed": 67, "success": 33, "n_tot_char": 1704, "n_char_success": 450, "n_parameters": 6, "avg_log_like_per_char": -5.943177393438502, "avg_log_like_pc_success": -3.2552203523902414, "n_fake": 100, "threshold": 10, "success_rate": 0.33, "fit_time": 0.06480884552001953, "statistics_time": 0.012529850006103516}, {"failed": 81, "success": 19, "n_tot_char": 1704, "n_char_success": 247, "n_parameters": 6, "avg_log_like_per_char": -6.370104858269857, "avg_log_like_pc_success": -3.198620392772753, "n_fake": 100, "threshold": 20, "success_rate": 0.19, "fit_time": 0.033540964126586914, "statistics_time": 0.009588479995727539}, {"failed": 35, "success": 165, "n_tot_char": 3345, "n_char_success": 2686, "n_parameters": 34, "avg_log_like_per_char": -4.042993995371798, "avg_log_like_pc_success": -3.340135586623021, "n_fake": 200, "threshold": 2, "success_rate": 0.825, "fit_time": 1.1938812732696533, "statistics_time": 0.0985403060913086}, {"failed": 56, "success": 144, "n_tot_char": 3345, "n_char_success": 2260, "n_parameters": 28, "avg_log_like_per_char": -4.461887839067391, "avg_log_like_pc_success": -3.2876550194623047, "n_fake": 200, "threshold": 5, "success_rate": 0.72, "fit_time": 0.6547980308532715, "statistics_time": 0.08606219291687012}, {"failed": 72, "success": 128, "n_tot_char": 3345, "n_char_success": 1916, "n_parameters": 14, "avg_log_like_per_char": -4.863754943748257, "avg_log_like_pc_success": -3.3392891404866663, "n_fake": 200, "threshold": 10, "success_rate": 0.64, "fit_time": 0.5491735935211182, "statistics_time": 0.05734586715698242}, {"failed": 130, "success": 70, "n_tot_char": 3345, "n_char_success": 948, "n_parameters": 6, "avg_log_like_per_char": -5.872591770356897, "avg_log_like_pc_success": -3.2552004938013033, "n_fake": 200, "threshold": 20, "success_rate": 0.35, "fit_time": 0.05806708335876465, "statistics_time": 0.02443671226501465}, {"failed": 46, "success": 354, "n_tot_char": 6807, "n_char_success": 5895, "n_parameters": 37, "avg_log_like_per_char": -3.8463093646761317, "avg_log_like_pc_success": -3.372681090910722, "n_fake": 400, "threshold": 2, "success_rate": 0.885, "fit_time": 0.7392327785491943, "statistics_time": 0.1929948329925537}, {"failed": 78, "success": 322, "n_tot_char": 6807, "n_char_success": 5308, "n_parameters": 32, "avg_log_like_per_char": -4.118354310868131, "avg_log_like_pc_success": -3.330616546888688, "n_fake": 400, "threshold": 5, "success_rate": 0.805, "fit_time": 0.3620619773864746, "statistics_time": 0.15889453887939453}, {"failed": 121, "success": 279, "n_tot_char": 6807, "n_char_success": 4358, "n_parameters": 30, "avg_log_like_per_char": -4.630048897410976, "avg_log_like_pc_success": -3.3500803502637098, "n_fake": 400, "threshold": 10, "success_rate": 0.6975, "fit_time": 0.9727156162261963, "statistics_time": 0.15169024467468262}, {"failed": 135, "success": 265, "n_tot_char": 6807, "n_char_success": 4037, "n_parameters": 18, "avg_log_like_per_char": -4.791521826337636, "avg_log_like_pc_success": -3.3394617163982567, "n_fake": 400, "threshold": 20, "success_rate": 0.6625, "fit_time": 0.48130059242248535, "statistics_time": 0.13312840461730957}, {"failed": 40, "success": 560, "n_tot_char": 10232, "n_char_success": 9453, "n_parameters": 76, "avg_log_like_per_char": -3.705901334061322, "avg_log_like_pc_success": -3.4420439106937883, "n_fake": 600, "threshold": 2, "success_rate": 0.9333333333333333, "fit_time": 1.1393024921417236, "statistics_time": 0.37146496772766113}, {"failed": 90, "success": 510, "n_tot_char": 10232, "n_char_success": 8444, "n_parameters": 32, "avg_log_like_per_char": -3.976196567446547, "avg_log_like_pc_success": -3.3554449122800785, "n_fake": 600, "threshold": 5, "success_rate": 0.85, "fit_time": 0.4925038814544678, "statistics_time": 0.27304935455322266}, {"failed": 157, "success": 443, "n_tot_char": 10232, "n_char_success": 7114, "n_parameters": 32, "avg_log_like_per_char": -4.427124518588163, "avg_log_like_pc_success": -3.3398871400516894, "n_fake": 600, "threshold": 10, "success_rate": 0.7383333333333333, "fit_time": 0.5276165008544922, "statistics_time": 0.24965691566467285}, {"failed": 210, "success": 390, "n_tot_char": 10232, "n_char_success": 6019, "n_parameters": 23, "avg_log_like_per_char": -4.806840976651201, "avg_log_like_pc_success": -3.3363056791399135, "n_fake": 600, "threshold": 20, "success_rate": 0.65, "fit_time": 0.5112464427947998, "statistics_time": 0.2521941661834717}, {"failed": 52, "success": 948, "n_tot_char": 17092, "n_char_success": 16066, "n_parameters": 96, "avg_log_like_per_char": -3.7097098882825983, "avg_log_like_pc_success": -3.5054776854407077, "n_fake": 1000, "threshold": 2, "success_rate": 0.948, "fit_time": 1.2152841091156006, "statistics_time": 0.640235424041748}, {"failed": 93, "success": 907, "n_tot_char": 17092, "n_char_success": 15260, "n_parameters": 43, "avg_log_like_per_char": -3.7442738063673437, "avg_log_like_pc_success": -3.36449018527754, "n_fake": 1000, "threshold": 5, "success_rate": 0.907, "fit_time": 0.9460070133209229, "statistics_time": 0.5245726108551025}, {"failed": 134, "success": 866, "n_tot_char": 17092, "n_char_success": 14386, "n_parameters": 36, "avg_log_like_per_char": -3.903987787582177, "avg_log_like_pc_success": -3.3389805005163926, "n_fake": 1000, "threshold": 10, "success_rate": 0.866, "fit_time": 0.33597517013549805, "statistics_time": 0.4719991683959961}, {"failed": 226, "success": 774, "n_tot_char": 17092, "n_char_success": 12687, "n_parameters": 44, "avg_log_like_per_char": -4.211096382306275, "avg_log_like_pc_success": -3.2748007694854837, "n_fake": 1000, "threshold": 20, "success_rate": 0.774, "fit_time": 0.4520912170410156, "statistics_time": 0.4333508014678955}]}}
\ No newline at end of file
diff --git a/benchmarks/accurate/fit_time.csv b/benchmarks/accurate/fit_time.csv
index 0753e9c..0a630b6 100644
--- a/benchmarks/accurate/fit_time.csv
+++ b/benchmarks/accurate/fit_time.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,1.0345648144420825,1.2123727422011528,1.0417345015626207,1.045587078521126,0.9813138428487278
-address,0.5851861000061035,3.190283405780792,1.179566252231598,1.620183265209198,1.0900044083595275
-phone_number,0.27152488231658933,0.31640344858169556,0.3707251787185669,0.4781836748123169,0.12308040857315064
-pricetag,0.09918105602264404,0.10968753099441528,0.09890204668045044,0.10601838827133178,0.12361856698989868
-timezone,0.7945192575454711,0.7907622814178467,0.7977358102798462,0.7928660154342652,0.794252622127533
-mime_type,0.6416023373603821,0.626270866394043,0.6598812699317932,0.6560978055000305,0.6223598003387452
-unix_partition,0.1689455986022949,0.16861530542373657,0.16406331062316895,0.16328907012939453,0.1622081756591797
-ascii_email,0.3578091263771057,0.2798264503479004,0.2519848823547363,0.2195643186569214,0.15801957845687867
-isbn10,0.14612566232681273,0.1527639627456665,0.1379132866859436,0.13787614107131957,0.14406723976135255
-job,0.727282977104187,1.6923543453216552,0.7106510639190674,0.06669650077819825,0.5871858239173889
-ssn,0.012790465354919433,0.06312897205352783,0.04731178283691406,0.0453626275062561,0.043325531482696536
-user_agent,12.596259105205537,12.546485209465027,12.668293738365174,12.390237605571746,12.43778133392334
-color,0.149095618724823,0.14120876789093018,0.14178090095520018,0.147017240524292,0.1418485164642334
-license_plate,0.1619734764099121,0.11599844694137573,0.28234190940856935,0.10648893117904663,0.048722708225250246
-iban,0.05423048734664917,0.03062647581100464,0.056125521659851074,0.02893441915512085,0.02854464054107666
-company,0.860711669921875,0.4471618175506592,0.2821364879608154,0.8930826306343078,0.39163488149642944
-time,0.07157649993896484,0.07681479454040527,0.07187584638595582,0.07299824953079223,0.07291327714920044
-ipv4,0.058012306690216064,0.0574945330619812,0.05647341012954712,0.05602554082870483,0.05796483755111694
-uri,1.1566624999046327,1.3994098067283631,1.661333680152893,1.0549142003059386,1.246828019618988
-name,0.743242347240448,0.829785680770874,0.1538591504096985,0.8303178668022155,0.3706026434898376
+average,1.0539685337167037,1.1724900741326183,1.0449505680485773,1.0116366750315617,0.9894646123835915
+address,0.3905096411705017,2.4048934102058412,0.88459233045578,1.37061265707016,1.1492964386940003
+phone_number,0.2472851037979126,0.31745070219039917,0.31231064796447755,0.45900691747665406,0.11690607070922851
+pricetag,0.09145452976226806,0.09916337728500366,0.09132636785507202,0.09569346904754639,0.10785114765167236
+timezone,0.36651819944381714,0.3723091959953308,0.36817280054092405,0.36243308782577516,0.3656641602516174
+mime_type,0.3960761785507202,0.39853849411010744,0.39532947540283203,0.39961127042770384,0.39510915279388426
+unix_partition,0.15677225589752197,0.16830892562866212,0.1568874955177307,0.1540762662887573,0.157298743724823
+ascii_email,0.3711480975151062,0.4466110587120056,0.3135540246963501,0.38162081241607665,0.2586862564086914
+isbn10,0.07403323650360108,0.072108793258667,0.07242581844329835,0.07177988290786744,0.07382155656814575
+job,0.5449542999267578,1.6180486798286438,0.5358052730560303,0.058674752712249756,0.5220331430435181
+ssn,0.011691272258758545,0.020928192138671874,0.04452129602432251,0.04437443017959595,0.04361436367034912
+user_agent,13.337422728538513,13.322584187984466,13.204304611682891,12.808983647823334,13.13469854593277
+color,0.08022589683532715,0.07984303236007691,0.07631831169128418,0.07980431318283081,0.07828595638275146
+license_plate,0.8959861516952514,0.2581357717514038,1.2845681667327882,0.086572265625,0.046995580196380615
+iban,0.050638020038604736,0.028040647506713867,0.053077518939971924,0.027474796772003172,0.026432621479034423
+company,0.8615628361701966,0.49370708465576174,0.3043045163154602,0.9282056212425231,0.43524144887924193
+time,0.16923638582229614,0.17327781915664672,0.1716768503189087,0.1747194766998291,0.16687155961990358
+ipv4,0.06315799951553344,0.062369561195373534,0.061660492420196535,0.0629740834236145,0.06114859580993652
+uri,1.0409641027450562,1.164347732067108,1.3814642071723937,0.7627432703971863,1.0739984512329102
+name,0.8757652044296265,0.7766447424888611,0.14176058769226074,0.8917358040809631,0.5858738422393799
diff --git a/benchmarks/accurate/n_parameters.csv b/benchmarks/accurate/n_parameters.csv
index 0ec038b..6643273 100644
--- a/benchmarks/accurate/n_parameters.csv
+++ b/benchmarks/accurate/n_parameters.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,69.73684210526316,76.78157894736842,73.71052631578948,73.21315789473684,67.96578947368421
-address,23.0,106.2,89.1,51.65,50.25
-phone_number,38.5,56.55,46.35,51.05,20.35
+average,84.06315789473683,90.3921052631579,85.9578947368421,86.10526315789475,80.76052631578946
+address,28.15,104.1,84.7,66.6,57.25
+phone_number,35.8,51.65,46.2,52.85,20.6
 pricetag,10.0,11.0,10.0,11.0,13.0
-timezone,67.55,67.55,67.55,67.55,67.55
-mime_type,63.95,63.95,63.95,63.95,63.95
-unix_partition,23.0,23.0,23.0,23.0,23.0
-ascii_email,26.8,29.8,20.7,22.4,20.85
-isbn10,23.15,23.15,23.15,23.15,23.15
-job,30.95,55.5,30.95,4.95,26.15
-ssn,2.0,4.0,6.0,6.0,6.0
-user_agent,817.05,817.05,817.05,817.05,817.05
-color,4.3,4.3,4.3,4.3,4.3
-license_plate,21.85,14.0,35.9,9.6,6.0
+timezone,44.95,44.95,44.95,44.95,44.95
+mime_type,48.85,48.85,48.85,48.85,48.85
+unix_partition,15.0,15.0,15.0,15.0,15.0
+ascii_email,30.45,34.85,23.7,28.25,21.55
+isbn10,11.7,11.7,11.7,11.7,11.7
+job,36.1,77.2,36.1,5.45,30.95
+ssn,2.0,3.0,6.0,6.0,6.0
+user_agent,1058.6,1058.6,1058.6,1058.6,1058.6
+color,4.0,4.0,4.0,4.0,4.0
+license_plate,45.55,18.15,55.3,11.2,6.0
 iban,6.0,4.0,6.0,4.0,4.0
-company,53.0,48.2,44.65,89.5,39.05
-time,8.2,8.2,8.2,8.2,8.2
-ipv4,8.0,8.0,8.0,8.0,8.0
-uri,63.0,63.35,87.65,77.9,59.75
-name,34.7,51.05,8.0,47.8,30.75
+company,60.75,57.4,42.1,89.5,47.4
+time,27.75,27.75,27.75,27.75,27.75
+ipv4,8.2,8.2,8.2,8.2,8.2
+uri,75.6,77.45,94.6,83.2,76.1
+name,47.75,59.6,9.45,58.9,32.55
diff --git a/benchmarks/accurate/statistics_time.csv b/benchmarks/accurate/statistics_time.csv
index 5a893ab..3d6bfdd 100644
--- a/benchmarks/accurate/statistics_time.csv
+++ b/benchmarks/accurate/statistics_time.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,0.23670234868400977,0.19632002240733099,0.20751905378542448,0.1909523047898945,0.2666338274353429
-address,0.5640411972999573,0.08103983402252198,0.35432752370834353,0.5469830274581909,1.5645005583763123
-phone_number,0.18356084823608398,0.27067837715148924,0.22043415307998657,0.3016166687011719,0.22791289091110228
-pricetag,0.11802563667297364,0.10484408140182495,0.11929800510406494,0.12383359670639038,0.11443854570388794
-timezone,0.20284318923950195,0.2001940965652466,0.19879117012023925,0.19469419717788697,0.20013341903686524
-mime_type,0.2630094051361084,0.2680829882621765,0.25700788497924804,0.2564225435256958,0.28691624402999877
-unix_partition,0.12089968919754028,0.12026575803756714,0.11958647966384887,0.09764676094055176,0.09850943088531494
-ascii_email,0.4777830719947815,0.45578391551971437,0.3580363392829895,0.4442672967910767,0.35654832124710084
-isbn10,0.1591336965560913,0.15549232959747314,0.13595470190048217,0.13643981218338014,0.14882111549377441
-job,0.895618736743927,0.5545249700546264,0.8716941118240357,0.1670621395111084,0.933614456653595
-ssn,0.03439621925354004,0.09856067895889283,0.06396838426589965,0.0661852478981018,0.07760518789291382
-user_agent,0.0742943525314331,0.0760420322418213,0.07422363758087158,0.07444602251052856,0.07493953704833985
-color,0.036913824081420896,0.043144619464874266,0.03863755464553833,0.03617870807647705,0.03782203197479248
-license_plate,0.12433426380157471,0.083889901638031,0.2951118588447571,0.14747953414916992,0.06802377700805665
-iban,0.06942131519317626,0.046972906589508055,0.07641474008560181,0.04523591995239258,0.041746675968170166
-company,0.3531560659408569,0.4767865777015686,0.16467362642288208,0.31743320226669314,0.22107183933258057
-time,0.08106378316879273,0.06422587633132934,0.07434871196746826,0.06832236051559448,0.06625701189041137
-ipv4,0.17699612379074098,0.1620609164237976,0.1704077959060669,0.19188920259475709,0.16540493965148925
-uri,0.21172951459884642,0.1848188042640686,0.16189446449279785,0.2125089168548584,0.20953937768936157
-name,0.3501236915588379,0.28267176151275636,0.18805087804794313,0.19944863319396972,0.1722373604774475
+average,0.3725274939286081,0.35760665190847296,0.3541105922899749,0.3333816998883297,0.3658875609699049
+address,0.3507387042045593,0.5237283706665039,0.306281316280365,0.5258392691612244,0.7479472994804383
+phone_number,0.13483427762985228,0.17085692882537842,0.14457722902297973,0.2041498303413391,0.15075273513793946
+pricetag,0.0670514702796936,0.07024327516555787,0.06944836378097534,0.06762607097625732,0.07765616178512573
+timezone,0.17192832231521607,0.16927993297576904,0.17377980947494506,0.1745126485824585,0.18833197355270387
+mime_type,0.19353278875350952,0.19230760335922242,0.1960466980934143,0.18899186849594116,0.19149105548858641
+unix_partition,0.07950320243835449,0.08038326501846313,0.07492446899414062,0.07483541965484619,0.07768925428390502
+ascii_email,0.3820098161697388,0.2919479846954346,0.293787682056427,0.2775312542915344,0.3095932364463806
+isbn10,0.08862709999084473,0.09146804809570312,0.08646180629730224,0.09172695875167847,0.09234809875488281
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+ssn,0.02109973430633545,0.05699102878570557,0.040679645538330075,0.04134832620620728,0.04376267194747925
+user_agent,3.5394612431526182,3.5141764760017393,3.5784319758415224,3.4911162495613097,3.574007034301758
+color,0.022504639625549317,0.023080503940582274,0.02483234405517578,0.02321392297744751,0.024385404586791993
+license_plate,0.11899588108062745,0.06640117168426514,0.16523100137710572,0.09924142360687256,0.044054734706878665
+iban,0.04013749361038208,0.02795299291610718,0.03892586231231689,0.02615940570831299,0.026592910289764404
+company,0.2860146164894104,0.24908096790313722,0.17642858028411865,0.28308495283126833,0.1593705415725708
+time,0.12270145416259766,0.12008458375930786,0.11647778749465942,0.11584010124206542,0.11876322031021118
+ipv4,0.09932031631469726,0.10533849000930787,0.10943217277526855,0.09472846984863281,0.11898685693740844
+uri,0.3120978355407715,0.29120917320251466,0.28321017026901246,0.2415168046951294,0.33221733570098877
+name,0.2072792410850525,0.22625041007995605,0.12627315521240234,0.2211417555809021,0.21142570972442626
diff --git a/benchmarks/accurate/success_rate.csv b/benchmarks/accurate/success_rate.csv
index a7de0e9..430c08b 100644
--- a/benchmarks/accurate/success_rate.csv
+++ b/benchmarks/accurate/success_rate.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,0.8054820175438596,0.7722614035087719,0.7991982456140351,0.8003311403508772,0.8176798245614035
-address,0.80155,0.14516666666666667,0.6247833333333335,0.4232166666666667,0.6094333333333333
-phone_number,0.923875,0.986,0.8334166666666667,0.8870000000000001,0.96
+average,0.8321157894736843,0.8003622807017544,0.8270039473684211,0.8292701754385966,0.8422311403508772
+address,0.8029999999999999,0.20754166666666665,0.7309083333333333,0.4850083333333334,0.5924166666666666
+phone_number,0.8775000000000001,0.9837916666666666,0.8282916666666666,0.8865000000000001,0.96
 pricetag,0.9870000000000001,0.9870000000000001,0.9870000000000001,0.9870000000000001,0.9870000000000001
-timezone,0.7072083333333332,0.7072083333333332,0.7072083333333332,0.7072083333333332,0.7072083333333332
-mime_type,0.6642833333333333,0.6642833333333333,0.6642833333333333,0.6642833333333333,0.6642833333333333
+timezone,0.8647166666666667,0.8647166666666667,0.8647166666666667,0.8647166666666667,0.8647166666666667
+mime_type,0.8331083333333332,0.8331083333333332,0.8331083333333332,0.8331083333333332,0.8331083333333332
 unix_partition,1.0,1.0,1.0,1.0,1.0
-ascii_email,0.7571,0.8261333333333333,0.8131499999999999,0.8592333333333333,0.8550666666666666
-isbn10,0.8974333333333332,0.8974333333333332,0.8974333333333332,0.8974333333333332,0.8974333333333332
-job,0.6685916666666666,0.46515000000000006,0.6685916666666666,0.9444166666666668,0.7570916666666667
+ascii_email,0.7691416666666667,0.833625,0.8160666666666667,0.8541333333333332,0.8590666666666668
+isbn10,0.944125,0.944125,0.944125,0.944125,0.944125
+job,0.6830416666666667,0.47194166666666676,0.6830416666666667,0.9470416666666667,0.75925
 ssn,1.0,1.0,1.0,1.0,1.0
-user_agent,0.22983333333333333,0.22983333333333333,0.22983333333333333,0.22983333333333333,0.22983333333333333
+user_agent,0.2953416666666667,0.2953416666666667,0.2953416666666667,0.2953416666666667,0.2953416666666667
 color,1.0,1.0,1.0,1.0,1.0
-license_plate,0.96175,1.0,0.7986499999999999,0.9484083333333334,1.0
+license_plate,0.9578749999999999,1.0,0.7578833333333334,0.9501083333333333,1.0
 iban,1.0,1.0,1.0,1.0,1.0
-company,0.6251333333333333,0.7535,0.7913083333333333,0.65525,0.796025
-time,1.0,1.0,1.0,1.0,1.0
-ipv4,0.95625,0.95625,0.95625,0.95625,0.95625
-uri,0.44856666666666667,0.4569083333333334,0.33760833333333334,0.460175,0.41877499999999995
-name,0.6755833333333332,0.5981000000000001,0.8752500000000001,0.5865833333333332,0.6975166666666667
+company,0.6702583333333334,0.7805166666666666,0.8020999999999999,0.6932499999999999,0.8143
+time,0.9737500000000001,0.9737500000000001,0.9737500000000001,0.9737500000000001,0.9737500000000001
+ipv4,0.9577500000000001,0.9577500000000001,0.9577500000000001,0.9577500000000001,0.9577500000000001
+uri,0.5051916666666666,0.46362499999999995,0.3618083333333333,0.5043833333333333,0.4584833333333333
+name,0.6883999999999999,0.61005,0.8771833333333333,0.5799166666666666,0.7030833333333334
diff --git a/benchmarks/benchmark.py b/benchmarks/benchmark.py
index 6a1cdc8..effd9da 100755
--- a/benchmarks/benchmark.py
+++ b/benchmarks/benchmark.py
@@ -8,7 +8,8 @@
 
 from faker import Faker
 
-from regexmodel.model import RegexModel
+from regexmodel.model import RegexModel, NotFittedError
+from regexmodel.util import LOG_LIKE_PER_CHAR
 
 
 def run_bench(faker_type, count_thres, n_fake, locale="NL", method="accurate"):
@@ -21,9 +22,20 @@ def run_bench(faker_type, count_thres, n_fake, locale="NL", method="accurate"):
     for cur_n_fake in n_fake:
         for cur_count_thres in count_thres:
             start_time = time.time()
-            model = RegexModel.fit(fake_data[:cur_n_fake], cur_count_thres, method=method)
-            mid_time = time.time()
-            stats = model.fit_statistics(fake_data_2[:cur_n_fake])
+            try:
+                model = RegexModel.fit(fake_data[:cur_n_fake], cur_count_thres, method=method)
+                mid_time = time.time()
+                stats = model.fit_statistics(fake_data_2[:cur_n_fake])
+            except NotFittedError:
+                stats = {
+                    "failed": cur_n_fake,
+                    "success": 0,
+                    "n_tot_char": len("".join(fake_data_2[:cur_n_fake])),
+                    "n_char_success": 0,
+                    "n_parameters": 0,
+                    "avg_log_like_per_char": LOG_LIKE_PER_CHAR,
+                    "avg_log_like_pc_success": LOG_LIKE_PER_CHAR,
+                }
             end_time = time.time()
             success_rate = stats["success"]/(stats["success"] + stats["failed"])
             stats.update({"n_fake": cur_n_fake, "threshold": cur_count_thres,
diff --git a/benchmarks/fast/avg_log_like_pc_success.csv b/benchmarks/fast/avg_log_like_pc_success.csv
index 373b840..d51fded 100644
--- a/benchmarks/fast/avg_log_like_pc_success.csv
+++ b/benchmarks/fast/avg_log_like_pc_success.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,-2.2409882957700686,-2.143153071296043,-2.2464613780772336,-2.1801543870996385,-2.140369232410269
-address,-2.8355871760748492,-1.5143606760569353,-2.0351862354708516,-1.9929198528530752,-2.770704483995882
-phone_number,-1.772053783637433,-1.5598565452612498,-2.1924706163907848,-1.7496805678569196,-2.001020843204409
+average,-2.660094277507786,-2.6303322298945524,-2.6401069552064382,-2.6357056102760357,-2.474216865671893
+address,-3.2136560306635715,-4.144162884150992,-2.8291935422703145,-3.652973514278588,-3.219018694245942
+phone_number,-2.425645514123286,-1.6318742360788563,-2.2499296426410273,-2.2206673394845033,-2.049223740657882
 pricetag,-1.9825715341353412,-1.7433240004049062,-1.9825715341353412,-1.7433240004049062,-1.4043777551175962
-timezone,-2.0289745430520516,-2.0289745430520516,-2.0289745430520516,-2.0289745430520516,-2.0289745430520516
-mime_type,-2.824054543383545,-2.824054543383545,-2.824054543383545,-2.824054543383545,-2.824054543383545
-unix_partition,-0.8429283135527367,-0.8429283135527367,-0.8429283135527367,-0.8429283135527367,-0.8429283135527367
-ascii_email,-2.883291244003288,-2.7685815825859774,-2.807167707440735,-2.8667738694723885,-2.780440646298293
-isbn10,-1.908460596728953,-1.908460596728953,-1.908460596728953,-1.908460596728953,-1.908460596728953
-job,-3.2968488568518652,-3.3434136322462047,-3.2968488568518652,-3.512404371144477,-3.618281686455594
+timezone,-2.9987358376375486,-2.9987358376375486,-2.9987358376375486,-2.9987358376375486,-2.9987358376375486
+mime_type,-3.2588793982646154,-3.2588793982646154,-3.2588793982646154,-3.2588793982646154,-3.2588793982646154
+unix_partition,-1.02295554621081,-1.02295554621081,-1.02295554621081,-1.02295554621081,-1.02295554621081
+ascii_email,-3.0280720967369774,-3.202890220300668,-2.8636425522434887,-3.285366520410176,-2.878257016259181
+isbn10,-1.908905444171528,-1.908905444171528,-1.908905444171528,-1.908905444171528,-1.908905444171528
+job,-3.6741225219100273,-3.688080575548992,-3.6741225219100273,-3.5153069329301756,-3.6005553830784933
 ssn,-2.302585092994053,-2.3173140261754446,-1.883933257904226,-1.883933257904226,-1.883933257904226
-user_agent,-0.34922713571106995,-0.34922713571106995,-0.34922713571106995,-0.34922713571106995,-0.34922713571106995
-color,-3.07863311203932,-3.07863311203932,-3.07863311203932,-3.07863311203932,-3.07863311203932
-license_plate,-2.470697252907526,-2.2421505394955856,-3.259421300198894,-2.662874402388851,-2.0032353934459195
+user_agent,-3.4019946909617103,-3.4019946909617103,-3.4019946909617103,-3.4019946909617103,-3.4019946909617103
+color,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406
+license_plate,-2.8004253921494944,-2.3375498028169694,-3.9760043727447796,-2.674409353307234,-2.0032353934459195
 iban,-2.2590781815563674,-2.1320232342537286,-2.266988529090495,-2.0932591754491296,-2.072326583694648
-company,-2.9907392827847357,-3.1087779680971392,-3.021968394550412,-3.1319741257689744,-2.1686555267110394
-time,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485
-ipv4,-2.078159173483116,-2.078159173483116,-2.078159173483116,-2.078159173483116,-2.078159173483116
-uri,-1.6932499293641896,-1.7906995981571296,-1.7577190587484572,-1.7656085661669203,-1.767304749981583
-name,-3.231268158723607,-3.338599425292474,-3.3176835660873323,-3.1593740388852263,-3.3359273623878836
+company,-3.7498811845213362,-3.1608567834839367,-3.162832535138472,-3.532924487208896,-2.5828846258766345
+time,-1.736774924512717,-1.736774924512717,-1.736774924512717,-1.736774924512717,-1.736774924512717
+ipv4,-2.078583243995832,-2.078583243995832,-2.078583243995832,-2.078583243995832,-2.078583243995832
+uri,-2.992722357693471,-3.005191393018095,-3.0673721602245854,-3.0275404111989594,-3.091172632351314
+name,-3.2254352049759,-3.725449050575812,-3.3178453394314658,-3.5611054414797727,-3.3375392039460214
diff --git a/benchmarks/fast/avg_log_like_per_char.csv b/benchmarks/fast/avg_log_like_per_char.csv
index 25fcba6..52decee 100644
--- a/benchmarks/fast/avg_log_like_per_char.csv
+++ b/benchmarks/fast/avg_log_like_per_char.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,-3.2412145186033237,-3.279342856641357,-3.2573585628145523,-3.220412892695998,-3.0417155743001296
-address,-3.810944654760724,-6.24327826206152,-4.1734623494824605,-5.317206386592242,-4.506387426907823
-phone_number,-2.171232061291646,-1.6314629946951051,-2.853931598117417,-2.416279023864864,-2.2073134372665324
+average,-3.225352142862375,-3.2551921233423986,-3.2255878422107607,-3.1724846129556474,-3.012563020890872
+address,-3.835827273938297,-6.0841468739912585,-3.800012137449007,-5.095435505323979,-4.650693838247658
+phone_number,-2.4958971796916147,-1.707485332623498,-3.0317967205971343,-2.4019180845229293,-2.250806260046024
 pricetag,-2.045451232753291,-1.8093618497764439,-2.045451232753291,-1.8093618497764439,-1.4749324931858763
-timezone,-3.521953942289032,-3.521953942289032,-3.521953942289032,-3.521953942289032,-3.521953942289032
-mime_type,-4.040229918091513,-4.040229918091513,-4.040229918091513,-4.040229918091513,-4.040229918091513
-unix_partition,-0.8429283135527367,-0.8429283135527367,-0.8429283135527367,-0.8429283135527367,-0.8429283135527367
-ascii_email,-3.9181486643217553,-3.626586505631189,-3.670922283026532,-3.6218400571782774,-3.4056218235950206
-isbn10,-2.2005614671190026,-2.2005614671190026,-2.2005614671190026,-2.2005614671190026,-2.2005614671190026
-job,-4.851404613433035,-5.587059418458896,-4.851404613433035,-3.6686003606586874,-4.534347980573196
+timezone,-3.6011828087036575,-3.6011828087036575,-3.6011828087036575,-3.6011828087036575,-3.6011828087036575
+mime_type,-3.945297853112666,-3.945297853112666,-3.945297853112666,-3.945297853112666,-3.945297853112666
+unix_partition,-1.02295554621081,-1.02295554621081,-1.02295554621081,-1.02295554621081,-1.02295554621081
+ascii_email,-3.9638086593920625,-3.6731110451570084,-3.702575755717792,-3.6999819900018878,-3.478438656919743
+isbn10,-2.1909211454985296,-2.1909211454985296,-2.1909211454985296,-2.1909211454985296,-2.1909211454985296
+job,-4.828640492502152,-5.591881707113073,-4.828640492502152,-3.6669438767303433,-4.518350668951157
 ssn,-2.302585092994053,-2.3173140261754446,-1.883933257904226,-1.883933257904226,-1.883933257904226
-user_agent,-5.218546135936092,-5.218546135936092,-5.218546135936092,-5.218546135936092,-5.218546135936092
-color,-3.07863311203932,-3.07863311203932,-3.07863311203932,-3.07863311203932,-3.07863311203932
-license_plate,-2.6478583661828106,-2.2421505394955856,-4.0780734908692935,-2.8749331637021824,-2.0032353934459195
+user_agent,-5.204998932905473,-5.204998932905473,-5.204998932905473,-5.204998932905473,-5.204998932905473
+color,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406,-2.4807670754333406
+license_plate,-2.9849050011690546,-2.3375498028169694,-4.45627449956268,-2.891406975479061,-2.0032353934459195
 iban,-2.2590781815563674,-2.1320232342537286,-2.266988529090495,-2.0932591754491296,-2.072326583694648
-company,-4.931260685164355,-4.31139577940173,-3.8947470829920987,-4.88625920488004,-3.308291632188181
-time,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485,-1.7503697086472485
-ipv4,-2.26972756360758,-2.26972756360758,-2.26972756360758,-2.26972756360758,-2.26972756360758
-uri,-4.982047779877311,-4.827990214531594,-5.404133215095273,-4.815650995056335,-5.127652178728633
-name,-4.740114359845275,-4.655941290422014,-3.8438148794298477,-4.877571324879009,-4.34560354292988
+company,-4.839663029309443,-4.24591614337047,-3.7782331506829743,-4.735539993130452,-3.3951799704058807
+time,-1.872908543697085,-1.872908543697085,-1.872908543697085,-1.872908543697085,-1.872908543697085
+ipv4,-2.2634526769570096,-2.2634526769570096,-2.2634526769570096,-2.2634526769570096,-2.2634526769570096
+uri,-4.583444452608314,-4.629958674456958,-5.074367970174636,-4.521926112631789,-4.543420515810816
+name,-4.559905535951905,-4.737417071252152,-3.835410673051507,-4.8950162426884924,-4.384895175796045
diff --git a/benchmarks/fast/benchmark.json b/benchmarks/fast/benchmark.json
index a282ac1..8d45ae0 100644
--- a/benchmarks/fast/benchmark.json
+++ b/benchmarks/fast/benchmark.json
@@ -1 +1 @@
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{"failed": 97, "success": 903, "n_tot_char": 17092, "n_char_success": 15232, "n_parameters": 30, "avg_log_like_per_char": -3.728245495708536, "avg_log_like_pc_success": -3.3399912811018524, "n_fake": 1000, "threshold": 10, "success_rate": 0.903, "fit_time": 0.16370153427124023, "statistics_time": 0.2611358165740967}, {"failed": 192, "success": 808, "n_tot_char": 17092, "n_char_success": 13289, "n_parameters": 29, "avg_log_like_per_char": -4.1137485737776505, "avg_log_like_pc_success": -3.3141694105680206, "n_fake": 1000, "threshold": 20, "success_rate": 0.808, "fit_time": 0.12974166870117188, "statistics_time": 0.2317368984222412}]}}
\ No newline at end of file
diff --git a/benchmarks/fast/fit_time.csv b/benchmarks/fast/fit_time.csv
index 1a7ff2f..a38aa90 100644
--- a/benchmarks/fast/fit_time.csv
+++ b/benchmarks/fast/fit_time.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,0.375759124128442,0.4588882590595045,0.37545852472907615,0.40892826005032185,0.3583602390791241
-address,0.4483449935913086,1.6569068074226379,0.7516815304756165,1.062580406665802,0.7515618205070496
-phone_number,0.24137760400772096,0.3049220323562622,0.2853178858757019,0.42530914545059206,0.11956222057342529
-pricetag,0.09978399276733399,0.11979110240936279,0.10497825145721436,0.12143491506576538,0.1332426428794861
-timezone,0.5306779980659485,0.522619879245758,0.5222585916519165,0.5034102439880371,0.5262861847877502
-mime_type,0.4471957921981812,0.4537933707237244,0.46527178287506105,0.47611638307571413,0.46865999698638916
-unix_partition,0.13612931966781616,0.13521064519882203,0.13989614248275756,0.13632190227508545,0.1370360732078552
-ascii_email,0.26455280780792234,0.24912117719650267,0.18668885231018068,0.1856565833091736,0.160835862159729
-isbn10,0.07872217893600464,0.08153853416442872,0.07988564968109131,0.08032917976379395,0.08118029832839965
-job,0.4751774311065674,0.7868334293365479,0.49146246910095215,0.07040989398956299,0.46667553186416627
-ssn,0.014464879035949707,0.06326180696487427,0.04843798875808716,0.04434710741043091,0.04375818967819214
-user_agent,2.663699996471405,2.613234794139862,2.569206488132477,2.5001593470573424,2.5708802461624147
-color,0.14543317556381224,0.1393404483795166,0.14093486070632935,0.14662281274795533,0.1449778437614441
-license_plate,0.16514811515808106,0.11741496324539184,0.25309178829193113,0.10449168682098389,0.04952775239944458
-iban,0.05446902513504028,0.03277451992034912,0.05532376766204834,0.028692448139190675,0.027907800674438477
-company,0.43906832933425904,0.3283448338508606,0.18901456594467164,0.7080936312675477,0.29838560819625853
-time,0.07060399055480956,0.07389600276947021,0.07080576419830323,0.07311438322067261,0.0722877025604248
-ipv4,0.05562698841094971,0.062352442741394044,0.05886023044586182,0.05694091320037842,0.058966410160064694
-uri,0.46893874406814573,0.5121518969535828,0.6207650423049926,0.536278772354126,0.4170044779777527
-name,0.34000799655914304,0.4653682351112366,0.09983031749725342,0.5093271851539611,0.2801078796386719
+average,0.44348284633536095,0.48897933018834977,0.48718974590301506,0.44586312959068697,0.4104577089610853
+address,0.31871967315673827,1.1071295619010926,0.6661554932594299,0.8833371639251709,0.7858118176460266
+phone_number,0.19779256582260132,0.2957450032234192,0.2255522608757019,0.3645781636238098,0.1042739748954773
+pricetag,0.07900480031967164,0.08985408544540405,0.07916254997253418,0.09176020622253418,0.10094264745712281
+timezone,0.29454320669174194,0.29150712490081787,0.2942359447479248,0.29603729248046873,0.297223961353302
+mime_type,0.33689610958099364,0.3391795039176941,0.3440557956695557,0.33817715644836427,0.3345711350440979
+unix_partition,0.1435275912284851,0.1443050265312195,0.1431760311126709,0.14274920225143434,0.14262033700942994
+ascii_email,0.31465294361114504,0.41245404481887815,0.3054808259010315,0.36020593643188475,0.2724949359893799
+isbn10,0.06801358461380005,0.06553022861480713,0.06741290092468262,0.06771229505538941,0.0678521990776062
+job,0.35036187171936034,0.7642837882041931,0.3525028467178345,0.056452047824859616,0.4075445055961609
+ssn,0.011651563644409179,0.020364797115325926,0.04478951692581177,0.04594004154205322,0.04538305997848511
+user_agent,3.9627877473831177,3.9643630385398865,3.9769091963768006,3.9507962703704833,3.9290295958518984
+color,0.0806149959564209,0.08077472448348999,0.08036992549896241,0.07883810997009277,0.07939985990524293
+license_plate,0.837438952922821,0.2633166790008545,1.4964908480644226,0.08320695161819458,0.049174380302429196
+iban,0.052714550495147706,0.02809408903121948,0.05191341638565063,0.027690815925598144,0.02821173667907715
+company,0.4593530297279358,0.4227712631225586,0.275167441368103,0.6024427056312561,0.36734790802001954
+time,0.1768226981163025,0.17462705373764037,0.17796061038970948,0.17556675672531127,0.17002243995666505
+ipv4,0.06043727397918701,0.05905125141143799,0.05988926887512207,0.05947954654693603,0.06169935464859009
+uri,0.3249611496925354,0.3886409640312195,0.5253227591514588,0.3669858932495117,0.3197657704353333
+name,0.3558797717094421,0.3786150455474854,0.09005753993988037,0.4794429063796997,0.23532685041427612
diff --git a/benchmarks/fast/n_parameters.csv b/benchmarks/fast/n_parameters.csv
index 531e02f..79cf6bc 100644
--- a/benchmarks/fast/n_parameters.csv
+++ b/benchmarks/fast/n_parameters.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,59.502631578947366,67.41842105263159,62.473684210526315,63.821052631578944,57.88684210526316
-address,22.9,109.3,87.25,50.25,47.3
-phone_number,28.3,56.0,29.5,51.15,20.35
+average,76.03684210526318,83.15,80.08947368421053,79.30526315789473,73.25789473684212
+address,24.2,99.8,82.25,70.0,52.85
+phone_number,26.9,51.85,34.85,52.55,20.6
 pricetag,11.0,12.0,11.0,12.0,14.0
-timezone,64.7,64.7,64.7,64.7,64.7
-mime_type,57.25,57.25,57.25,57.25,57.25
-unix_partition,17.1,17.1,17.1,17.1,17.1
-ascii_email,27.15,29.05,20.65,23.05,20.8
-isbn10,9.85,9.85,9.85,9.85,9.85
-job,30.0,55.0,30.0,4.95,23.45
-ssn,2.0,4.0,6.0,6.0,6.0
-user_agent,674.75,674.75,674.75,674.75,674.75
-color,4.3,4.3,4.3,4.3,4.3
-license_plate,21.85,14.0,31.0,9.3,6.0
+timezone,40.7,40.7,40.7,40.7,40.7
+mime_type,48.5,48.5,48.5,48.5,48.5
+unix_partition,14.95,14.95,14.95,14.95,14.95
+ascii_email,29.8,34.5,26.4,30.25,22.55
+isbn10,9.15,9.15,9.15,9.15,9.15
+job,32.8,75.9,32.8,5.45,26.45
+ssn,2.0,3.0,6.0,6.0,6.0
+user_agent,958.3,958.3,958.3,958.3,958.3
+color,4.0,4.0,4.0,4.0,4.0
+license_plate,45.2,18.9,54.1,10.3,6.0
 iban,6.0,4.0,6.0,4.0,4.0
-company,50.0,51.05,24.4,85.9,31.15
-time,8.2,8.2,8.2,8.2,8.2
-ipv4,8.0,8.0,8.0,8.0,8.0
-uri,56.25,62.95,89.05,75.3,57.5
-name,30.95,39.45,8.0,46.55,25.15
+company,56.4,58.35,40.15,83.9,41.1
+time,27.75,27.75,27.75,27.75,27.75
+ipv4,8.2,8.2,8.2,8.2,8.2
+uri,56.4,67.05,107.5,70.7,58.65
+name,42.45,42.95,9.1,50.1,28.15
diff --git a/benchmarks/fast/statistics_time.csv b/benchmarks/fast/statistics_time.csv
index af06ced..606ec4f 100644
--- a/benchmarks/fast/statistics_time.csv
+++ b/benchmarks/fast/statistics_time.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,0.2660710341052005,0.39417201406077335,0.2592002404363532,0.23033893924010426,0.2837316130336962
-address,0.5756866574287415,0.2140980362892151,0.5017725467681885,0.6522582054138184,1.3848934769630432
-phone_number,0.15865224599838257,0.37062236070632937,0.2927179574966431,0.3582787036895752,0.2498213529586792
-pricetag,0.13999204635620116,0.1457780718803406,0.12506428956985474,0.13410515785217286,0.1600165009498596
-timezone,0.2657091498374939,0.27027665376663207,0.26840696334838865,0.2935555338859558,0.2718844532966614
-mime_type,0.2789698362350464,0.2820464015007019,0.3074734449386597,0.2931824564933777,0.30188430547714235
-unix_partition,0.18917523622512816,0.17388132810592652,0.17206947803497313,0.18986351490020753,0.18202455043792726
-ascii_email,0.4894667506217957,0.4594874978065491,0.4082655906677246,0.4222884297370911,0.387020468711853
-isbn10,0.17457244396209717,0.1679045557975769,0.15968220233917235,0.1575761318206787,0.17040618658065795
-job,0.8651909589767456,3.485503602027893,0.8847076416015625,0.1536267638206482,0.5863009691238403
-ssn,0.0318656325340271,0.10433886051177979,0.08896708488464355,0.08899577856063842,0.08475347757339477
-user_agent,0.21363071203231812,0.21522209644317628,0.21384038925170898,0.20188480615615845,0.21209795475006105
-color,0.04460616111755371,0.05256568193435669,0.04668658971786499,0.05470904111862183,0.04885619878768921
-license_plate,0.1478866696357727,0.1074215292930603,0.2641688585281372,0.15092231035232545,0.09136313199996948
-iban,0.08414219617843628,0.056930816173553465,0.08770138025283813,0.06656403541564941,0.0678745985031128
-company,0.5338083744049072,0.32760307788848875,0.31966911554336547,0.2804568290710449,0.31225972175598143
-time,0.09798520803451538,0.08363747596740723,0.09696484804153442,0.08292938470840454,0.08699123859405518
-ipv4,0.2183984637260437,0.21436947584152222,0.19190975427627563,0.1907801628112793,0.20627573728561402
-uri,0.36124166250228884,0.380264139175415,0.2889938950538635,0.34863260984420774,0.26474241018295286
-name,0.1843692421913147,0.3773166060447693,0.20574253797531128,0.25582998991012573,0.32143391370773317
+average,0.21199768028761215,0.22038161127190842,0.2083560818120053,0.1814483121821755,0.21131778767234402
+address,0.36883739233016966,0.438567578792572,0.3620315432548523,0.4184715151786804,0.7453068971633912
+phone_number,0.11980239152908326,0.2307225227355957,0.16001712083816527,0.22999043464660646,0.1764484167098999
+pricetag,0.084190034866333,0.09248616695404052,0.08469936847686768,0.09257355928421021,0.1138277530670166
+timezone,0.19994752407073973,0.20562517642974854,0.2014729142189026,0.19336748123168945,0.19278671741485595
+mime_type,0.20288504362106324,0.19953120946884156,0.1963370442390442,0.1970391035079956,0.21045143604278566
+unix_partition,0.11488375663757325,0.11561309099197388,0.11715078353881836,0.11530458927154541,0.11761249303817749
+ascii_email,0.3684751033782959,0.2941148281097412,0.30965830087661744,0.28787301778793334,0.32693144083023074
+isbn10,0.10260372161865235,0.11440002918243408,0.10507404804229736,0.10521706342697143,0.10364487171173095
+job,0.7470872282981873,0.6352629065513611,0.8011923551559448,0.09978263378143311,0.3920792698860168
+ssn,0.02159698009490967,0.06388570070266723,0.0524605393409729,0.053902173042297365,0.053426718711853026
+user_agent,0.5271039724349975,0.525436007976532,0.5209779739379883,0.5535896420478821,0.5320081233978271
+color,0.02807140350341797,0.028524792194366454,0.028346002101898193,0.029846203327178956,0.028126180171966553
+license_plate,0.12365775108337403,0.07839572429656982,0.14906680583953857,0.1174972414970398,0.05304981470108032
+iban,0.05051076412200928,0.039984714984893796,0.052080726623535155,0.03533262014389038,0.03455231189727783
+company,0.2522149205207825,0.32447264194488523,0.18692989349365235,0.19996280670166017,0.16967287063598632
+time,0.13649423122406007,0.12873584032058716,0.12705538272857667,0.13801058530807495,0.1511772632598877
+ipv4,0.12287343740463257,0.13196947574615478,0.13478306531906128,0.13463374376296997,0.11623861789703369
+uri,0.28764517307281495,0.2872244119644165,0.2320610523223877,0.2710926175117493,0.34765306711196897
+name,0.16907509565353393,0.25229779481887815,0.13737063407897948,0.17403090000152588,0.15004370212554932
diff --git a/benchmarks/fast/success_rate.csv b/benchmarks/fast/success_rate.csv
index b6059db..d8045fd 100644
--- a/benchmarks/fast/success_rate.csv
+++ b/benchmarks/fast/success_rate.csv
@@ -1,21 +1,21 @@
 faker_type,nl,fr,en,de,da
-average,0.823955701754386,0.7974552631578948,0.8222491228070176,0.8194706140350876,0.8378118421052632
-address,0.8084333333333333,0.162575,0.6263,0.4308166666666667,0.6241083333333333
-phone_number,0.9375,0.986,0.8890833333333333,0.8870000000000001,0.96
+average,0.8402802631578948,0.8134412280701755,0.8362478070175439,0.8394021929824562,0.8536114035087718
+address,0.8093083333333333,0.2128166666666667,0.7176583333333333,0.4956083333333334,0.6068833333333332
+phone_number,0.8835000000000001,0.983875,0.8440833333333334,0.9105000000000001,0.96
 pricetag,0.9870000000000001,0.9870000000000001,0.9870000000000001,0.9870000000000001,0.9870000000000001
-timezone,0.7354833333333334,0.7354833333333334,0.7354833333333334,0.7354833333333334,0.7354833333333334
-mime_type,0.7757833333333333,0.7757833333333333,0.7757833333333333,0.7757833333333333,0.7757833333333333
+timezone,0.8683916666666667,0.8683916666666667,0.8683916666666667,0.8683916666666667,0.8683916666666667
+mime_type,0.8386833333333332,0.8386833333333332,0.8386833333333332,0.8386833333333332,0.8386833333333332
 unix_partition,1.0,1.0,1.0,1.0,1.0
-ascii_email,0.7750416666666667,0.8289333333333333,0.8131499999999999,0.8605666666666668,0.8550666666666666
-isbn10,0.9421250000000001,0.9421250000000001,0.9421250000000001,0.9421250000000001,0.9421250000000001
-job,0.6784416666666665,0.49650833333333344,0.6784416666666665,0.9444166666666668,0.777875
+ascii_email,0.7881666666666667,0.839775,0.8181499999999999,0.8576083333333333,0.8608166666666668
+isbn10,0.944125,0.944125,0.944125,0.944125,0.944125
+job,0.6897833333333333,0.5004,0.6897833333333333,0.9470416666666667,0.778325
 ssn,1.0,1.0,1.0,1.0,1.0
-user_agent,0.30614166666666665,0.30614166666666665,0.30614166666666665,0.30614166666666665,0.30614166666666665
+user_agent,0.3077833333333333,0.3077833333333333,0.3077833333333333,0.3077833333333333,0.3077833333333333
 color,1.0,1.0,1.0,1.0,1.0
-license_plate,0.96175,1.0,0.8227499999999999,0.9506083333333333,1.0
+license_plate,0.9578749999999999,1.0,0.7698666666666668,0.9492333333333333,1.0
 iban,1.0,1.0,1.0,1.0,1.0
-company,0.6341666666666665,0.7551833333333333,0.84195,0.6596,0.8088083333333334
-time,1.0,1.0,1.0,1.0,1.0
-ipv4,0.95625,0.95625,0.95625,0.95625,0.95625
-uri,0.46559166666666674,0.506575,0.373025,0.5118499999999999,0.43843333333333334
-name,0.69145,0.7130916666666666,0.8752500000000001,0.6222999999999999,0.7513500000000001
+company,0.6790499999999999,0.7798416666666668,0.8261,0.696625,0.8033999999999999
+time,0.9737500000000001,0.9737500000000001,0.9737500000000001,0.9737500000000001,0.9737500000000001
+ipv4,0.9577500000000001,0.9577500000000001,0.9577500000000001,0.9577500000000001,0.9577500000000001
+uri,0.5752416666666667,0.5609916666666666,0.46840000000000004,0.5834333333333335,0.58855
+name,0.7049166666666667,0.7001999999999999,0.8771833333333333,0.6311083333333334,0.7431583333333334
diff --git a/examples/regex.html b/examples/regex.html
index 5381c7c..175ec18 100644
--- a/examples/regex.html
+++ b/examples/regex.html
@@ -41,6 +41,75 @@ <h1></h1>
              }
 
              
+             #loadingBar {
+                 position:absolute;
+                 top:0px;
+                 left:0px;
+                 width: 1000px;
+                 height: 1000px;
+                 background-color:rgba(200,200,200,0.8);
+                 -webkit-transition: all 0.5s ease;
+                 -moz-transition: all 0.5s ease;
+                 -ms-transition: all 0.5s ease;
+                 -o-transition: all 0.5s ease;
+                 transition: all 0.5s ease;
+                 opacity:1;
+             }
+
+             #bar {
+                 position:absolute;
+                 top:0px;
+                 left:0px;
+                 width:20px;
+                 height:20px;
+                 margin:auto auto auto auto;
+                 border-radius:11px;
+                 border:2px solid rgba(30,30,30,0.05);
+                 background: rgb(0, 173, 246); /* Old browsers */
+                 box-shadow: 2px 0px 4px rgba(0,0,0,0.4);
+             }
+
+             #border {
+                 position:absolute;
+                 top:10px;
+                 left:10px;
+                 width:500px;
+                 height:23px;
+                 margin:auto auto auto auto;
+                 box-shadow: 0px 0px 4px rgba(0,0,0,0.2);
+                 border-radius:10px;
+             }
+
+             #text {
+                 position:absolute;
+                 top:8px;
+                 left:530px;
+                 width:30px;
+                 height:50px;
+                 margin:auto auto auto auto;
+                 font-size:22px;
+                 color: #000000;
+             }
+
+             div.outerBorder {
+                 position:relative;
+                 top:400px;
+                 width:600px;
+                 height:44px;
+                 margin:auto auto auto auto;
+                 border:8px solid rgba(0,0,0,0.1);
+                 background: rgb(252,252,252); /* Old browsers */
+                 background: -moz-linear-gradient(top,  rgba(252,252,252,1) 0%, rgba(237,237,237,1) 100%); /* FF3.6+ */
+                 background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,rgba(252,252,252,1)), color-stop(100%,rgba(237,237,237,1))); /* Chrome,Safari4+ */
+                 background: -webkit-linear-gradient(top,  rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* Chrome10+,Safari5.1+ */
+                 background: -o-linear-gradient(top,  rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* Opera 11.10+ */
+                 background: -ms-linear-gradient(top,  rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* IE10+ */
+                 background: linear-gradient(to bottom,  rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* W3C */
+                 filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#fcfcfc', endColorstr='#ededed',GradientType=0 ); /* IE6-9 */
+                 border-radius:72px;
+                 box-shadow: 0px 0px 10px rgba(0,0,0,0.2);
+             }
+             
 
              
 
@@ -57,6 +126,15 @@ <h1></h1>
         </div>
 
         
+            <div id="loadingBar">
+              <div class="outerBorder">
+                <div id="text">0%</div>
+                <div id="border">
+                  <div id="bar"></div>
+                </div>
+              </div>
+            </div>
+        
         
 
         <script type="text/javascript">
@@ -88,8 +166,8 @@ <h1></h1>
                   
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"group": 2, "id": 0, "label": "start", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 1, "label": "[a-z]{3,19}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 2, "label": 2, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 3, "label": "[0-9]{2,2}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 4, "label": "[@]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 5, "label": "[a-z]{3,9}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 6, "label": 6, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 7, "label": "[\\-]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 9, "label": "[\\.]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 8, "label": "[a-z]{4,9}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 10, "label": 10, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 11, "label": "[c]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 14, "label": "[b]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 17, "label": "[i]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 21, "label": "[n]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 24, "label": "[o]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 12, "label": "[o]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 13, "label": "[m]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 15, "label": "[i]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 16, "label": "[z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 18, "label": "[n]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 19, "label": "[f]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 20, "label": "[o]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 22, "label": "[e]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 23, "label": "[t]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 25, "label": "[r]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 26, "label": "[g]", "shape": "dot", "size": 10}]);
-                  edges = new vis.DataSet([{"arrows": "to", "from": 0, "to": 1, "width": 1}, {"arrows": "to", "from": 1, "to": 2, "width": 1}, {"arrows": "to", "from": 2, "to": 3, "width": 1}, {"arrows": "to", "from": 2, "to": 4, "width": 1}, {"arrows": "to", "from": 3, "to": 4, "width": 1}, {"arrows": "to", "from": 4, "to": 5, "width": 1}, {"arrows": "to", "from": 5, "to": 6, "width": 1}, {"arrows": "to", "from": 6, "to": 7, "width": 1}, {"arrows": "to", "from": 6, "to": 9, "width": 1}, {"arrows": "to", "from": 7, "to": 8, "width": 1}, {"arrows": "to", "from": 8, "to": 9, "width": 1}, {"arrows": "to", "from": 9, "to": 10, "width": 1}, {"arrows": "to", "from": 10, "to": 11, "width": 1}, {"arrows": "to", "from": 10, "to": 14, "width": 1}, {"arrows": "to", "from": 10, "to": 17, "width": 1}, {"arrows": "to", "from": 10, "to": 21, "width": 1}, {"arrows": "to", "from": 10, "to": 24, "width": 1}, {"arrows": "to", "from": 11, "to": 12, "width": 1}, {"arrows": "to", "from": 12, "to": 13, "width": 1}, {"arrows": "to", "from": 14, "to": 15, "width": 1}, {"arrows": "to", "from": 15, "to": 16, "width": 1}, {"arrows": "to", "from": 17, "to": 18, "width": 1}, {"arrows": "to", "from": 18, "to": 19, "width": 1}, {"arrows": "to", "from": 19, "to": 20, "width": 1}, {"arrows": "to", "from": 21, "to": 22, "width": 1}, {"arrows": "to", "from": 22, "to": 23, "width": 1}, {"arrows": "to", "from": 24, "to": 25, "width": 1}, {"arrows": "to", "from": 25, "to": 26, "width": 1}]);
+                  nodes = new vis.DataSet([{"group": 2, "id": 0, "label": "start", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 1, "label": 1, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 2, "label": "[0-9]{3,5}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 61, "label": "[UP]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 3, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 4, "label": "[A-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 5, "label": "[a-z]{2,8}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 6, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 7, "label": "[A-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 8, "label": "[a-z]{3,8}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 9, "label": 9, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 10, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 49, "label": "[\\\n]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 11, "label": 11, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 12, "label": "[A]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 30, "label": "[S]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 13, "label": "[p]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 14, "label": "[t]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 15, "label": "[\\.]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 16, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 17, "label": "[0-9]{3,3}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 18, "label": "[\\\n]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 19, "label": "[A-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 20, "label": "[a-z]{2,11}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 21, "label": 21, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 22, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 25, "label": "[,]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 23, "label": "[A-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 24, "label": "[a-z]{4,10}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 26, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 27, "label": "[A-Z]{2,2}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 28, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 29, "label": "[0-9]{5,5}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 31, "label": "[u]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 32, "label": "[i]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 33, "label": "[t]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 34, "label": "[e]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 35, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 36, "label": "[0-9]{3,3}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 37, "label": "[\\\n]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 38, "label": "[A-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 39, "label": "[a-z]{3,11}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 40, "label": 40, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 41, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 44, "label": "[,]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 42, "label": "[A-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 43, "label": "[a-z]{5,10}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 45, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 46, "label": "[A-Z]{2,2}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 47, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 48, "label": "[0-9]{5,5}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 50, "label": "[A-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 51, "label": "[a-z]{2,12}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 52, "label": 52, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 53, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 56, "label": "[,]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 54, "label": "[A-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 55, "label": "[a-z]{3,11}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 57, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 58, "label": "[A-Z]{2,2}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 59, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 60, "label": "[0-9]{5,5}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 62, "label": 62, "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 63, "label": "[S]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 83, "label": "[n]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 64, "label": "[CA-Z]{1,2}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 65, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 66, "label": "[0-9A-Z]{4,4}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 67, "label": "[,]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 68, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 69, "label": "[B]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 70, "label": "[o]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 71, "label": "[x]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 72, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 73, "label": "[0-9]{4,4}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 74, "label": "[\\\n]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 75, "label": "[A]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 76, "label": "[P]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 77, "label": "[O]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 78, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 79, "label": "[A]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 80, "label": "[EP]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 81, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 82, "label": "[0-9]{5,5}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 84, "label": "[i]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 85, "label": "[t]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 86, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 87, "label": "[0-9]{4,4}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 88, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 89, "label": "[B]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 90, "label": "[o]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 91, "label": "[x]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 92, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 93, "label": "[0-9]{4,4}", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 94, "label": "[\\\n]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 95, "label": "[D]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 96, "label": "[P]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 97, "label": "[O]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 98, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 99, "label": "[A]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 100, "label": "[PA-Z]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 101, "label": "[ ]", "shape": "dot", "size": 10}, {"color": "#97c2fc", "id": 102, "label": "[0-9]{5,5}", "shape": "dot", "size": 10}]);
+                  edges = new vis.DataSet([{"arrows": "to", "from": 0, "to": 1, "width": 1}, {"arrows": "to", "from": 1, "to": 2, "width": 1}, {"arrows": "to", "from": 1, "to": 61, "width": 1}, {"arrows": "to", "from": 2, "to": 3, "width": 1}, {"arrows": "to", "from": 3, "to": 4, "width": 1}, {"arrows": "to", "from": 4, "to": 5, "width": 1}, {"arrows": "to", "from": 5, "to": 6, "width": 1}, {"arrows": "to", "from": 6, "to": 7, "width": 1}, {"arrows": "to", "from": 7, "to": 8, "width": 1}, {"arrows": "to", "from": 8, "to": 9, "width": 1}, {"arrows": "to", "from": 9, "to": 10, "width": 1}, {"arrows": "to", "from": 9, "to": 49, "width": 1}, {"arrows": "to", "from": 10, "to": 11, "width": 1}, {"arrows": "to", "from": 11, "to": 12, "width": 1}, {"arrows": "to", "from": 11, "to": 30, "width": 1}, {"arrows": "to", "from": 12, "to": 13, "width": 1}, {"arrows": "to", "from": 13, "to": 14, "width": 1}, {"arrows": "to", "from": 14, "to": 15, "width": 1}, {"arrows": "to", "from": 15, "to": 16, "width": 1}, {"arrows": "to", "from": 16, "to": 17, "width": 1}, {"arrows": "to", "from": 17, "to": 18, "width": 1}, {"arrows": "to", "from": 18, "to": 19, "width": 1}, {"arrows": "to", "from": 19, "to": 20, "width": 1}, {"arrows": "to", "from": 20, "to": 21, "width": 1}, {"arrows": "to", "from": 21, "to": 22, "width": 1}, {"arrows": "to", "from": 21, "to": 25, "width": 1}, {"arrows": "to", "from": 22, "to": 23, "width": 1}, {"arrows": "to", "from": 23, "to": 24, "width": 1}, {"arrows": "to", "from": 24, "to": 25, "width": 1}, {"arrows": "to", "from": 25, "to": 26, "width": 1}, {"arrows": "to", "from": 26, "to": 27, "width": 1}, {"arrows": "to", "from": 27, "to": 28, "width": 1}, {"arrows": "to", "from": 28, "to": 29, "width": 1}, {"arrows": "to", "from": 30, "to": 31, "width": 1}, {"arrows": "to", "from": 31, "to": 32, "width": 1}, {"arrows": "to", "from": 32, "to": 33, "width": 1}, {"arrows": "to", "from": 33, "to": 34, "width": 1}, {"arrows": "to", "from": 34, "to": 35, "width": 1}, {"arrows": "to", "from": 35, "to": 36, "width": 1}, {"arrows": "to", "from": 36, "to": 37, "width": 1}, {"arrows": "to", "from": 37, "to": 38, "width": 1}, {"arrows": "to", "from": 38, "to": 39, "width": 1}, {"arrows": "to", "from": 39, "to": 40, "width": 1}, {"arrows": "to", "from": 40, "to": 41, "width": 1}, {"arrows": "to", "from": 40, "to": 44, "width": 1}, {"arrows": "to", "from": 41, "to": 42, "width": 1}, {"arrows": "to", "from": 42, "to": 43, "width": 1}, {"arrows": "to", "from": 43, "to": 44, "width": 1}, {"arrows": "to", "from": 44, "to": 45, "width": 1}, {"arrows": "to", "from": 45, "to": 46, "width": 1}, {"arrows": "to", "from": 46, "to": 47, "width": 1}, {"arrows": "to", "from": 47, "to": 48, "width": 1}, {"arrows": "to", "from": 49, "to": 50, "width": 1}, {"arrows": "to", "from": 50, "to": 51, "width": 1}, {"arrows": "to", "from": 51, "to": 52, "width": 1}, {"arrows": "to", "from": 52, "to": 53, "width": 1}, {"arrows": "to", "from": 52, "to": 56, "width": 1}, {"arrows": "to", "from": 53, "to": 54, "width": 1}, {"arrows": "to", "from": 54, "to": 55, "width": 1}, {"arrows": "to", "from": 55, "to": 56, "width": 1}, {"arrows": "to", "from": 56, "to": 57, "width": 1}, {"arrows": "to", "from": 57, "to": 58, "width": 1}, {"arrows": "to", "from": 58, "to": 59, "width": 1}, {"arrows": "to", "from": 59, "to": 60, "width": 1}, {"arrows": "to", "from": 61, "to": 62, "width": 1}, {"arrows": "to", "from": 62, "to": 63, "width": 1}, {"arrows": "to", "from": 62, "to": 83, "width": 1}, {"arrows": "to", "from": 63, "to": 64, "width": 1}, {"arrows": "to", "from": 64, "to": 65, "width": 1}, {"arrows": "to", "from": 65, "to": 66, "width": 1}, {"arrows": "to", "from": 66, "to": 67, "width": 1}, {"arrows": "to", "from": 67, "to": 68, "width": 1}, {"arrows": "to", "from": 68, "to": 69, "width": 1}, {"arrows": "to", "from": 69, "to": 70, "width": 1}, {"arrows": "to", "from": 70, "to": 71, "width": 1}, {"arrows": "to", "from": 71, "to": 72, "width": 1}, {"arrows": "to", "from": 72, "to": 73, "width": 1}, {"arrows": "to", "from": 73, "to": 74, "width": 1}, {"arrows": "to", "from": 74, "to": 75, "width": 1}, {"arrows": "to", "from": 75, "to": 76, "width": 1}, {"arrows": "to", "from": 76, "to": 77, "width": 1}, {"arrows": "to", "from": 77, "to": 78, "width": 1}, {"arrows": "to", "from": 78, "to": 79, "width": 1}, {"arrows": "to", "from": 79, "to": 80, "width": 1}, {"arrows": "to", "from": 80, "to": 81, "width": 1}, {"arrows": "to", "from": 81, "to": 82, "width": 1}, {"arrows": "to", "from": 83, "to": 84, "width": 1}, {"arrows": "to", "from": 84, "to": 85, "width": 1}, {"arrows": "to", "from": 85, "to": 86, "width": 1}, {"arrows": "to", "from": 86, "to": 87, "width": 1}, {"arrows": "to", "from": 87, "to": 88, "width": 1}, {"arrows": "to", "from": 88, "to": 89, "width": 1}, {"arrows": "to", "from": 89, "to": 90, "width": 1}, {"arrows": "to", "from": 90, "to": 91, "width": 1}, {"arrows": "to", "from": 91, "to": 92, "width": 1}, {"arrows": "to", "from": 92, "to": 93, "width": 1}, {"arrows": "to", "from": 93, "to": 94, "width": 1}, {"arrows": "to", "from": 94, "to": 95, "width": 1}, {"arrows": "to", "from": 95, "to": 96, "width": 1}, {"arrows": "to", "from": 96, "to": 97, "width": 1}, {"arrows": "to", "from": 97, "to": 98, "width": 1}, {"arrows": "to", "from": 98, "to": 99, "width": 1}, {"arrows": "to", "from": 99, "to": 100, "width": 1}, {"arrows": "to", "from": 100, "to": 101, "width": 1}, {"arrows": "to", "from": 101, "to": 102, "width": 1}]);
 
                   nodeColors = {};
                   allNodes = nodes.get({ returnType: "Object" });
@@ -145,6 +223,23 @@ <h1></h1>
 
 
                   
+                      network.on("stabilizationProgress", function(params) {
+                          document.getElementById('loadingBar').removeAttribute("style");
+                          var maxWidth = 496;
+                          var minWidth = 20;
+                          var widthFactor = params.iterations/params.total;
+                          var width = Math.max(minWidth,maxWidth * widthFactor);
+                          document.getElementById('bar').style.width = width + 'px';
+                          document.getElementById('text').innerHTML = Math.round(widthFactor*100) + '%';
+                      });
+                      network.once("stabilizationIterationsDone", function() {
+                          document.getElementById('text').innerHTML = '100%';
+                          document.getElementById('bar').style.width = '496px';
+                          document.getElementById('loadingBar').style.opacity = 0;
+                          // really clean the dom element
+                          setTimeout(function () {document.getElementById('loadingBar').style.display = 'none';}, 500);
+                      });
+                  
 
                   return network;
 
diff --git a/pyproject.toml b/pyproject.toml
index 88e2bcd..a6186d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ GitHub = "https://github.com/sodascience/regexmodel"
 
 [project.optional-dependencies]
 test = [
-    "pytest", "pylint", "pydocstyle", "mypy", "flake8", "nbval",
+    "pytest", "pylint", "pydocstyle", "mypy", "ruff", "nbval",
 #    "sphinx<7.0.0", "sphinx-rtd-theme", "sphinxcontrib-napoleon", 
 #    "sphinx-autodoc-typehints", "sphinx_inline_tabs", "sphinx_copybutton",
 #    "XlsxWriter"
@@ -60,6 +60,10 @@ max-line-length=100
 max-locals=35
 max-args=10
 
+[tool.ruff]
+exclude = ["regexmodel/_version.py"]
+line-length=100
+
 [[tool.mypy.overrides]]
 module = [
     "networkx.*",
diff --git a/regexmodel/model.py b/regexmodel/model.py
index 0b91f81..1507f46 100644
--- a/regexmodel/model.py
+++ b/regexmodel/model.py
@@ -9,7 +9,6 @@
 from regexmodel.regexclass import fit_best_regex_class
 from regexmodel.util import sum_log, LOG_LIKE_PER_CHAR, NotFittedError
 from regexmodel.datastructure import Edge, OrNode, RegexNode
-# from regexmodel.model import fit_main_branch
 
 
 def _preview(series, size=3):
diff --git a/regexmodel/regexclass.py b/regexmodel/regexclass.py
index cfea010..901fdb1 100644
--- a/regexmodel/regexclass.py
+++ b/regexmodel/regexclass.py
@@ -25,9 +25,6 @@ class BaseRegex(ABC):
         Maximum length of the number of repeating elements.
     """
 
-    base_regex = r"Some invalid rege\x //"
-    n_possible = 1
-
     def __init__(self, min_len: int = 1, max_len: int = 1):
         self.min_len = min_len
         self.max_len = max_len
@@ -47,6 +44,38 @@ def from_string(cls, regex_str: str) -> Optional[tuple[BaseRegex, str]]:
         It will return None if the string does not belong to the regex class.
         """
 
+    @property
+    @abstractmethod
+    def base_regex(self) -> str:
+        """Base regex that returns the part inside the brackets.
+
+        For example 'A-Z' in the case of an uppercase regex.
+        """
+
+    @classmethod
+    @abstractmethod
+    def get_candidates(cls, series: pl.Series, count_thres: int
+                       ) -> Iterable[tuple[BaseRegex, float, pl.Series]]:
+        """Return a list of candidate regexes for a given series.
+
+        Parameters
+        ----------
+        series:
+            Series to create the candidates for.
+        count_thres:
+            Threshold for creating candidates. No candidates will be created that
+            have fewer matches with the series than this.
+
+        Returns
+        -------
+        regex:
+            Regex that matches the start of the series
+        score:
+            How well the regex matches the series.
+        next_series:
+            What is left after matching the series with the regex.
+        """
+
     @staticmethod
     def get_class_length(regex_str: str):
         """Get the min and max length of the regex string."""
@@ -121,11 +150,27 @@ def fit_value(self, value: str) -> Iterable:
             if res is not None:
                 yield (value[i_len:], self._draw_probability(i_len))
 
+    @property
+    def n_possible_div(self) -> float:
+        """Used to score the match.
 
-class CharClassRegex(BaseRegex, ABC):
+        It is equivalent to the number of possibilties, but with modifiers.
+        This is used in the character ranges, where the range is not maximal
+        to give them a lower score. Thus, [A-Z] has generally a higher score than
+        [A-Y], even if there is no Z present in the dataset.
+        """
+        return self.n_possible
+
+    @property
+    @abstractmethod
+    def n_possible(self) -> int:
+        """Get the number of possibilities of this regex."""
+
+
+class CharRangeRegex(BaseRegex, ABC):
     """Base class for regex classes that have multiple repeating elements.
 
-    Examples: [0-9]{3,4}, [A-Z]{1,2}
+    Examples: [0-9]{3,4}, [A-Z]{1,2}, [b-d]{1,2}
 
     Parameters
     ----------
@@ -135,11 +180,28 @@ class CharClassRegex(BaseRegex, ABC):
         Maximum length of the number of repeating elements.
     """
 
-    n_possible = 100
     all_possible = ""
 
+    def __init__(self, min_len: int = 1,
+                 max_len: int = 1,
+                 char_range: Optional[str] = None):
+        if char_range is None:
+            char_range = self.all_possible
+        self.char_range = char_range
+        super().__init__(min_len, max_len)
+
+    @property
+    def base_regex(self) -> str:
+        return f"{self.char_range[0]}-{self.char_range[-1]}"
+
     def draw_once(self):
-        return random.choice(self.all_possible)
+        return random.choice(self.char_range)
+
+    @property
+    def n_possible_div(self) -> float:
+        if self.char_range == self.all_possible:
+            return self.n_possible
+        return self.n_possible*1.3
 
     def covers(self, regex: BaseRegex) -> bool:
         if isinstance(regex, LiteralRegex):
@@ -149,55 +211,138 @@ def covers(self, regex: BaseRegex) -> bool:
             return True
         return False
 
+    @property
+    def n_possible(self):
+        return len(self.char_range)
+
     @classmethod
-    def from_string(cls, regex_str: str) -> Optional[tuple[BaseRegex, str]]:
+    def from_string(cls, regex_str) -> Optional[tuple[BaseRegex, str]]:
+        if len(regex_str) < 3:
+            return None
+        if (regex_str[0] in cls.all_possible
+                and regex_str[2] in cls.all_possible
+                and regex_str[1] == "-"):
+            start_index = cls.all_possible.index(regex_str[0])
+            end_index = cls.all_possible.index(regex_str[2])
+            if start_index < end_index:
+                return cls(char_range=cls.all_possible[start_index:end_index+1]), regex_str[3:]
         return None
 
+    @classmethod
+    def get_candidates(cls, series: pl.Series,
+                       count_thres: int) -> Iterable[tuple[BaseRegex, float, pl.Series]]:
+        """Get the score for the [A-Z, a-z, 0-9] regex classes."""
+        score_full, next_series_full, first_char_full = score(series, cls(), count_thres)
+
+        # Compute with sub range
+        unique_chars, counts = np.unique(first_char_full.drop_nulls().to_numpy(),
+                                         return_counts=True)
+        thres_idx = np.where(counts >= count_thres)[0]
+        if len(thres_idx) > 1:
+            start_idx = thres_idx[0]
+            end_idx = thres_idx[-1]
+            start_char, end_char = unique_chars[start_idx], unique_chars[end_idx]
+            ret = cls.from_string(f"{start_char}-{end_char}")
+            assert ret is not None
+            regex = ret[0]
+            score_sub, next_series_sub, _first_char_sub = score(series, regex, count_thres)
+        else:
+            score_sub = 0
+            regex = cls()
+
+        if score_full >= score_sub:
+            if score_full > 0:
+                yield (cls(), score_full, next_series_full)
+        else:
+            yield (regex, score_sub, next_series_sub)
+
+
+def score(series: pl.Series, regex: BaseRegex, count_thres: int,
+          first_char: Optional[pl.Series] = None
+          ) -> tuple[float, pl.Series, pl.Series]:
+    """Compute the score of matching the regex.
+
+    This attempts to create a universal measure of how good the regex fits the
+    series. It consists of:
+    - split_penalty: Some values in the series will not be match, and if this
+        happens too often, no match will ever be found. This attempts to take this
+        into account and will be lower if there is a higher likelihood of failing the
+        to fit.
+    - fraction_used: For character classes, there are characters that are not matched.
+        for example: [A-Z], but B is not the start of any of the series. In this case,
+        there is a penalty for that.
+    - n_not_null: Number of values in the series that are matched. The higher the better.
+
+    The score is then the product of all those values. It is normalized by the length of
+    the series to keep it < 1.
+
+    Parameters
+    ----------
+    series:
+        Series to be matched.
+    regex:
+        Regex that will match the series.
+    count_thres:
+        Threshold for allowing regexes to be used.
+    first_char:
+        Hack that is used to keep it from being recomputed in the case of the LiteralRegex.
 
-class UpperRegex(CharClassRegex):
+    Returns
+    -------
+    score:
+        Score that measures the goodness of fit.
+    next_series:
+        Series that is left over after matching the regex.
+    first_char:
+        Extracted match from the regex.
+    """
+    if first_char is None:
+        first_char = series.str.extract(r"^([" + regex.base_regex + r"])[\S\s]*$")
+    next_series = series.str.extract(r"^[" + regex.base_regex + r"]([\S\s]*)$")
+    n_not_null = len(series) - next_series.null_count()
+    if isinstance(regex, LiteralRegex):
+        n_unique = regex.n_possible
+    else:
+        n_unique = len(first_char.drop_nulls().unique())
+    avg_len_next = next_series.drop_nulls().str.lengths().mean()
+    avg_len_cur = series.filter(next_series.is_not_null()).drop_nulls().str.lengths().mean()
+    if (avg_len_cur == avg_len_next or n_not_null == 0 or n_not_null < count_thres
+            or avg_len_cur is None or avg_len_next is None):
+        return 0, next_series, first_char
+    fraction_not_null = (len(series)-next_series.null_count())/(len(series)-series.null_count())
+    n_lengths_left = avg_len_cur/(avg_len_cur-avg_len_next) - 1
+    fraction_used = n_unique/regex.n_possible_div
+
+    if n_lengths_left is None:
+        split_penalty = 1
+    else:
+        expected_finish = fraction_not_null**n_lengths_left*n_not_null
+        log_finish = np.log(expected_finish/count_thres)
+        split_penalty = 1/(1 + np.exp(-log_finish))
+    cur_score = split_penalty*fraction_used*n_not_null/len(series)
+    return cur_score, next_series, first_char
+
+
+class UpperRegex(CharRangeRegex):
     """Regex class that produces upper case characters."""
 
-    base_regex = r"A-Z"
-    prefac = 0.25
-    n_possible = 26
+    _base_regex = r"A-Z"
     all_possible = string.ascii_uppercase
 
-    @classmethod
-    def from_string(cls, regex_str) -> Optional[tuple[BaseRegex, str]]:
-        if regex_str[:3] == cls.base_regex:
-            return cls(), regex_str[3:]
-        return None
 
-
-class LowerRegex(CharClassRegex):
+class LowerRegex(CharRangeRegex):
     """Regex class that produces lower case characters."""
 
-    base_regex = r"a-z"
-    prefac = 0.25
-    n_possible = 26
+    _base_regex = r"a-z"
     all_possible = string.ascii_lowercase
 
-    @classmethod
-    def from_string(cls, regex_str) -> Optional[tuple[BaseRegex, str]]:
-        if regex_str[:3] == cls.base_regex:
-            return cls(), regex_str[3:]
-        return None
-
 
-class DigitRegex(CharClassRegex):
+class DigitRegex(CharRangeRegex):
     """Regex class that produces digits."""
 
-    base_regex = r"0-9"
-    prefac = 0.5
-    n_possible = 10
+    _base_regex = r"0-9"
     all_possible = string.digits
 
-    @classmethod
-    def from_string(cls, regex_str) -> Optional[tuple[BaseRegex, str]]:
-        if regex_str[:3] == cls.base_regex:
-            return cls(), regex_str[3:]
-        return None
-
 
 def _unescape(regex_str: str) -> str:
     """Remove slashes that are in the serialized string."""
@@ -252,17 +397,9 @@ def get_candidates(cls, series: pl.Series, count_thres: int
         unq_char, counts = np.unique(first_elem.drop_nulls().to_numpy(), return_counts=True)
         thres_char = unq_char[counts >= count_thres]
         for cur_char in thres_char:
-            next_series = series.str.extract(r"^" + cls(cur_char).regex + r"([\S\s]*)")
-            avg_len = next_series.drop_nulls().str.lengths().mean()
-
-            n_not_null = len(series) - next_series.null_count()
-            avg_len = next_series.drop_nulls().str.lengths().mean()
-            if avg_len is None:
-                split_penalty = 1
-            else:
-                expected_finish = 0.7**avg_len*n_not_null
-                split_penalty = 1/(1 + np.exp(2*(count_thres - expected_finish)/count_thres))
-            yield (cls(cur_char), split_penalty*n_not_null/len(series), next_series)
+            cur_score, next_series, _first_char = score(series, cls([cur_char]), count_thres,
+                                                        first_char=first_elem)
+            yield (cls(cur_char), cur_score, next_series)
 
     def __repr__(self):
         return f"Literal [{self.base_regex}]"
@@ -285,11 +422,9 @@ def get_class_stat(series: pl.Series, count_thres: int) -> list:
     Then sort them so that the first one is the best fit.
     """
     score_list: list[tuple[BaseRegex, float, pl.Series]] = []
-    for rclass in [UpperRegex, LowerRegex, DigitRegex]:
-        cur_class_stat = score_single(rclass.base_regex, series, count_thres, rclass.n_possible)
-        if cur_class_stat[0] > 0 and cur_class_stat[1].drop_nulls().len() >= count_thres:
-            score_list.append((rclass(), *cur_class_stat))
-    score_list.extend(LiteralRegex.get_candidates(series, count_thres))
+    for rclass in ALL_REGEX_CLASS:
+        score_list.extend(rclass.get_candidates(series, count_thres))
+
     return sorted(score_list, key=lambda res: -res[1])
 
 
@@ -309,12 +444,19 @@ def _get_bounds(key: str, count_dict: dict[str, int], count_thres: int,
     return lower_bound, upper_bound
 
 
-def _check_stat_compatible(stat_a: list, tot_a: int, stat_b: list, tot_b: int,
-                           count_thres: int, sigma: float = 2.0) -> bool:
-    dict_stat_a = {regex.base_regex: len(series)-series.null_count()
-                   for regex, _score, series in stat_a}
-    dict_stat_b = {regex.base_regex: len(series)-series.null_count()
-                   for regex, _score, series in stat_b}
+def _check_series_compatible(series_a, series_b, count_thres, sigma=2.0):
+    stats_a = get_class_stat(series_a, count_thres)
+    dict_stat_a = {}
+    dict_stat_b = {}
+    tot_a = len(series_a) - series_a.null_count()
+    tot_b = len(series_b) - series_b.null_count()
+    for regex, _score, next_series in stats_a:
+        dict_stat_a[regex.base_regex] = len(series_a) - next_series.null_count()
+        next_series_b = series_b.str.extract(r"^([" + regex.base_regex + r"])[\S\s]*")
+        count_b = len(series_b) - next_series_b.null_count()
+        if count_b >= count_thres:
+            dict_stat_b[regex.base_regex] = count_b
+
     for key in (set(dict_stat_a) | set(dict_stat_b)):
         lower_a, upper_a = _get_bounds(key, dict_stat_a, count_thres, sigma, tot_a)
         lower_b, upper_b = _get_bounds(key, dict_stat_b, count_thres, sigma, tot_b)
@@ -323,7 +465,7 @@ def _check_stat_compatible(stat_a: list, tot_a: int, stat_b: list, tot_b: int,
     return True
 
 
-class OrRegex(CharClassRegex):
+class OrRegex(CharRangeRegex):
     """Regex element that contains multiple regex elements.
 
     For example, a combination of A-Z and a-z to create [a-zA-Z].
@@ -383,14 +525,10 @@ def check_compatibility(self, series: pl.Series, count_thres: int) -> bool:
             return True
         base_regex = self._regex_instances[0]
         base_series = base_regex.extract_after_first(series)
-        base_stat = get_class_stat(base_series, count_thres)
         for cur_regex in self._regex_instances[1:]:
             cur_new_series = cur_regex.extract_after_first(series)
-            cur_class_stat = get_class_stat(cur_new_series, count_thres)
-            base_tot = len(series) - base_series.null_count()
-            cur_tot = len(series) - cur_new_series.null_count()
-            compatible = _check_stat_compatible(base_stat, base_tot, cur_class_stat, cur_tot,
-                                                count_thres)
+            compatible = _check_series_compatible(base_series, cur_new_series, count_thres)
+
             if not compatible:
                 return False
         return True
@@ -417,22 +555,6 @@ def from_string(cls, regex_str):
         return cls(all_regex, min_len, max_len), new_regex_str
 
 
-def score_single(regex: str, series: pl.Series, count_thres: int, n_possible: int
-                 ) -> tuple[float, pl.Series]:
-    """Get the score for the [A-Z, a-z, 0-9] regex classes."""
-    next_series = series.str.extract(r"^[" + regex + r"]([\S\s]*)$")
-    first_char = series.str.extract(r"^([" + regex + r"])[\S\s]*$")
-    n_not_null = len(series) - next_series.null_count()
-    n_unique = len(first_char.drop_nulls().unique())
-    avg_len = next_series.drop_nulls().str.lengths().mean()
-    if avg_len is None:
-        split_penalty = 1
-    else:
-        expected_finish = 0.7**avg_len*n_not_null
-        split_penalty = 1/(1 + np.exp(2*(count_thres - expected_finish)/count_thres))
-    return split_penalty*n_unique/n_possible*n_not_null/len(series), next_series
-
-
 # All regex classes available, order matters.
 ALL_REGEX_CLASS: list[type[BaseRegex]] = [UpperRegex, LowerRegex, DigitRegex, LiteralRegex]
 
@@ -451,6 +573,7 @@ def regex_list_from_string(regex_str) -> list[BaseRegex]:
     """
     cur_data = regex_str
     all_regexes = []
+
     # loop until we have parsed the whole string.
     while len(cur_data) > 0:
         found = False
@@ -499,9 +622,11 @@ def fit_best_regex_class(series: pl.Series, count_thres: int,
     cur_best_regex, best_score, cur_best_series = class_stat[0]
     cur_best_regex = OrRegex([cur_best_regex])
     cur_series = series.set(cur_best_series.is_not_null(), None)  # type: ignore
+    class_stat = get_class_stat(cur_series, count_thres)
 
     # Attempt to combine the regex element with the highest score with the other candidates.
-    for cur_regex, _score, _ in class_stat[1:]:
+    while len(class_stat) > 0:
+        cur_regex, _score, _next = class_stat[0]
         if cur_best_regex.covers(cur_regex):
             continue
 
@@ -510,17 +635,16 @@ def fit_best_regex_class(series: pl.Series, count_thres: int,
         cur_non_null = len(series) - new_cur_series.null_count()
         if cur_non_null < count_thres:
             continue
-        cur_new_class_stat = get_class_stat(new_cur_series, count_thres)
-        best_new_class_stat = get_class_stat(cur_best_series, count_thres)
 
         # Check whether the look-one-ahead distributions are similar enough.
-        best_count = len(series) - cur_best_series.null_count()
-        cur_count = len(series) - new_cur_series.null_count()
-        compatible = _check_stat_compatible(best_new_class_stat, best_count,
-                                            cur_new_class_stat, cur_count, count_thres)
+        compatible = _check_series_compatible(cur_best_series, new_cur_series, count_thres)
+
         if compatible:
             cur_best_regex.append(cur_regex)
-        cur_series = cur_series.set(new_cur_series.is_not_null(), None)  # type: ignore
+            cur_series = cur_series.set(new_cur_series.is_not_null(), None)  # type: ignore
+            class_stat = get_class_stat(cur_series, count_thres)
+
+        class_stat = class_stat[1:]
 
     # Find the min_length and max_length for the regex class.
     cur_series = series
diff --git a/tests/test_regex.py b/tests/test_regex.py
index 49f2929..1bceaa5 100644
--- a/tests/test_regex.py
+++ b/tests/test_regex.py
@@ -6,9 +6,9 @@
 @mark.parametrize("series,regex,counts", [
         (["R123", "R837", "R354", "R456", "R578", "R699"], r"[R][0-9]{3,3}",
             [6, 6, 6]),
-        (["A123", "B123", "C123", "D123", "E123", "F123"], r"[A-Z][1][2][3]",
+        (["A123", "B123", "C123", "D123", "E123", "F123"], r"[A-F][1][2][3]",
             [6, 6, 6, 6, 6]),
-        (["123a", "123b", "123c", "123d", "123e", "123f"], r"[1][2][3][a-z]",
+        (["123a", "123b", "123c", "123d", "123e", "123f"], r"[1][2][3][a-f]",
             [6, 6, 6, 6, 6]),
         # (["1a3f", "b2af", "22b2", "ac2e", "5f2a", "45e3", "c23f", "de2d", "3a43"], r"[0-9a-z]{4,4}"),
         (["abc", "edf", "abc", "edf", "abc", "abc", "abc"], r"([a][b][c]|[e][d][f])",