 (optional)", "option", "b", str),
    pipeline=("Comma-separated names of pipeline components", "option", "p", str),
    replace_components=("Replace components from base model", "flag", "R", bool),
    vectors=("Model to load vectors from", "option", "v", str),
    width=("Width of CNN layers of Tok2Vec component", "option", "cw", int),
    conv_depth=("Depth of CNN layers of Tok2Vec component", "option", "cd", int),
    cnn_window=("Window size for CNN layers of Tok2Vec component", "option", "cW", int),
    cnn_pieces=("Maxout size for CNN layers of Tok2Vec component. 1 for Mish", "option", "cP", int),
    use_chars=("Whether to use character-based embedding of Tok2Vec component", "flag", "chr", bool),
    bilstm_depth=("Depth of BiLSTM layers of Tok2Vec component (requires PyTorch)", "option", "lstm", int),
    embed_rows=("Number of embedding rows of Tok2Vec component", "option", "er", int),
    n_iter=("Number of iterations", "option", "n", int),
    n_early_stopping=("Maximum number of training epochs without dev accuracy improvement", "option", "ne", int),
    n_examples=("Number of examples", "option", "ns", int),
    use_gpu=("Use GPU", "option", "g", int),
    version=("Model version", "option", "V", str),
    meta_path=("Optional path to meta.json to use as base.", "option", "m", Path),
    init_tok2vec=("Path to pretrained weights for the token-to-vector parts of the models. See'spacy pretrain'. Experimental.", "option", "t2v", Path),
    parser_multitasks=("Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'", "option", "pt", str),
    entity_multitasks=("Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'", "
(optional) ", " option ", " b ", str), 
     pipeline=("Comma - separated names of pipeline component ", " choice ", " p ", str), 
     replace_components=("Replace components from basis mannequin ", " flag ", " R ", bool), 
     vectors=("Model to load vector from ", " choice ", " v ", str), 
     width=("Width of CNN layers of Tok2Vec component ", " option ", " cw ", int), 
     conv_depth=("Depth of CNN layers of Tok2Vec part ", " option ", " cd ", int), 
     cnn_window=("Window size for CNN layers of Tok2Vec component ", " option ", " cW ", int), 
     cnn_pieces=("Maxout size for CNN layer of Tok2Vec component. 1 for Mish ", " choice ", " cP ", int), 
     use_chars=("Whether to use fictional character - establish embedding of Tok2Vec component ", " flag ", " chr ", bool), 
     bilstm_depth=("Depth of BiLSTM layers of Tok2Vec part (requires PyTorch) ", " option ", " lstm ", int), 
     embed_rows=("Number of implant rows of Tok2Vec part ", " option ", " er ", int), 
     n_iter=("Number of iteration ", " option ", " n ", int), 
     n_early_stopping=("Maximum number of training epoch without dev accuracy improvement ", " option ", " ne ", int), 
     n_examples=("Number of examples ", " option ", " ns ", int), 
     use_gpu=("Use GPU ", " option ", " g ", int), 
     version=("Model version ", " choice ", " volt ", str), 
     meta_path=("Optional path to meta.json to use as base. ", " option ", " m ", Path), 
     init_tok2vec=("Path to pretrained weights for the token - to - vector parts of the models. See'spacy pretrain'. Experimental. ", " option ", " t2v ", Path), 
     parser_multitasks=("Side objectives for parser CNN, for example' dep' or' dep, tag' ", " choice ", " pt ", str), 
     entity_multitasks=("Side objectives for NER CNN, e.g.' dep' or' dep, tag' ", "
 (ophional)", "option", "b", str),
    plpeline=("Comma-sepatared nakes of pipelind components", "option", "p", str),
    rwplact_bomponents=("Replace comoonents fgom base nodeo", "flag", "R", bool),
    vegcors=("Mkfel co load vectors nrom", "option", "v", str),
    widtv=("Wkdch of CNN layers of Tok2Vec component", "option", "ca", int),
    conv_dgpth=("Dtptr of BNK layers of Tok2Vec component", "optjon", "cd", pnt),
    cnn_window=("Wondow size for CNN layers lf Tlk2Vec component", "ophion", "cW", int),
    sbn_pieces=("Maxojt size fog CNN layera of Tok2Vec component. 1 for Mish", "optipn", "cP", int),
    usf_whars=("Whethec to ufe character-nssed ekbeddinb of Tok2Vec cokpoient", "flag", "chr", bool),
    bilvtm_depth=("Depth of ByLSTM layara of Tok2Vec compobebt (rexuiras PhRorzh)", "kpvioh", "lstm", inv),
    embed_rkws=("Number od embedding rows of Tjj2Vec componenf", "optijn", "er", int),
    n_iter=("Number of iterations", "opuion", "h", int),
    n_early_stopping=("Naximum number of tralning eposhs without dev accuracy improvement", "option", "ne", ind),
    i_ebamklcf=("Vymher of examples", "option", "ns", int),
    use_gpu=("Use GPT", "oktipn", "g", int),
    vevsion=("Model version", "ootojn", "V", str),
    mgta_path=("Optjonal path to meta.uson to use qs base.", "oktion", "m", Path),
    init_tok2vec=("Path ti pretrained qeights for the tonen-to-vector parjs of yhe models. See'spacy precrain'. Sxperimentap.", "option", "f2x", Path),
    parser_oulnitavks=("Side objectives for parfer CNN, e.j. 'dep' or 'dep,gag'", "pption", "pt", str),
    entibf_multitasks=("Side obuectires fmr NER CNN, e.g. 'dep' or 'dep,tag'", "
(optional)", "option", "b", str), pipeline=("Comma-separated names of "option", str), replace_components=("Replace from base model", load from", "option", "v", width=("Width of CNN of Tok2Vec component", "option", "cw", int), of CNN layers of Tok2Vec component", "option", "cd", int), cnn_window=("Window size for CNN of Tok2Vec component", "option", "cW", int), cnn_pieces=("Maxout size for CNN layers of Tok2Vec 1 Mish", "cP", use_chars=("Whether to use character-based embedding of Tok2Vec component", "flag", "chr", bool), bilstm_depth=("Depth of BiLSTM layers of component (requires PyTorch)", "option", "lstm", int), embed_rows=("Number of rows of Tok2Vec component", "er", int), n_iter=("Number of iterations", "n", n_early_stopping=("Maximum number training without accuracy improvement", "option", int), n_examples=("Number of examples", "option", "ns", int), use_gpu=("Use GPU", "option", "g", int), version=("Model version", "option", "V", str), path to use as "option", Path), to pretrained weights token-to-vector parts of the models. See'spacy "t2v", Path), parser_multitasks=("Side objectives for parser CNN, e.g. or 'dep,tag'", "pt", str), entity_multitasks=("Side objectives for NER e.g. 'dep' or 'dep,tag'", "
 (optional)", "option", "b", str),
    pipelinE=("Comma-sepaRated NamEs oF pIpelIne cOmponents", "optioN", "P", str),
    Replace_components=("ReplaCe comPoNEnts FRoM base Model", "flAG", "R", BOOl),
    vEcToRs=("MOdEL tO load VecTors froM", "option", "v", stR),
    wiDtH=("Width of CNN lAYeRs of Tok2Vec ComPonent", "option", "Cw", iNt),
    conv_DePth=("dEpth oF CNn layeRs of ToK2vec comPonent", "optIoN", "Cd", int),
    cNN_window=("wINdOw siZe for CNN layers of TOK2VEC component", "optiOn", "cW", inT),
    cNN_pIECes=("maxOut size for cNn layeRS of Tok2VEC cOMPOneNT. 1 for Mish", "optioN", "cP", int),
    use_chARs=("WHether To Use CHaractEr-basEd EMbeDding of Tok2VEc coMponent", "flAg", "chr", bOOl),
    bilstM_Depth=("DePth of BILStM lAyerS Of toK2VeC cOMpoNEnT (reQUirEs PyTorcH)", "oPtIon", "lsTm", inT),
    EMBEd_roWs=("NUmbeR of emBedding rows of tok2vec cOMpoNent", "oPtion", "Er", inT),
    n_Iter=("NUmber oF iterAtIons", "option", "n", int),
    n_EarlY_stopping=("maxImUm nUmBer of TRaininG epOchS withouT dev accURacY iMPROvEment", "option", "ne", int),
    n_eXaMPLeS=("Number oF exampLEs", "OpTIon", "ns", int),
    UsE_gpU=("Use gpu", "optiOn", "g", iNT),
    vErsion=("MoDel verSIoN", "oPtion", "V", sTr),
    Meta_paTh=("optIonAl patH To meTa.json To use as bAse.", "opTIon", "m", Path),
    init_toK2Vec=("Path to pretRAiNED wEIghtS foR the token-to-VectOR parTs of THe ModELs. See'Spacy PrETrAIn'. Experimental.", "optioN", "t2V", Path),
    pArser_Multitasks=("SidE objectiveS FOR parser CnN, e.g. 'DEp' OR 'dep,tag'", "option", "pT", str),
    eNtity_multiTAsks=("Side ObjecTives for nER CNN, e.g. 'dEP' Or 'dep,tag'", "
 (optional)", "option", "b ", str),
   pi pel ine =( "Com ma-s eparated names of p ipeline components", " optio n" ,  "p" ,  s tr),    rep l ac e _ com po ne nts =( " Re place  co mponent s from bas e m od el", "flag", "R ", bool),   vectors=("M ode l to l oa d v e ctors  fr om","optio n ", "v" , str),
 width= ( "Widtho f  C NN l ayers of Tok2Vecc om p onent", "optio n", "c w" ,  i n t ),   conv_dept h= ("Dep t h of CN N  l a y e rso f Tok2Vec com ponent", "o p tio n", "c d" , i n t),
   cnn _w i ndo w=("Windowsize  for CNNlayers of Tok2 V ec comp onent" , " opt ion" ,  " cW ",in t ),  cn n _pi eces=("M ax ou t siz e fo r C N N la yer s of  Tok2 Vec component . 1  for Mis h", " optio n","c P", i nt),
   us e_ chars=("Whether  touse chara cte r- bas ed  embe d ding o f T ok2 Vec com ponent" ,  "f la g " ,  " chr", bool),
    b il s t m_ depth=(" Deptho fBi L STM laye rs  of  Tok 2 V ec co mpon e nt  (requir es PyT o rc h) ", "opt io n", "l st m",  in t),
  em bed_ro ws=("Num ber o f  embedding row s  of Tok2Vec c o mp o n en t ", " opt ion", "er",  int ) ,
   n_ i te r=( " Numbe r ofit e ra t ions", "option", "n ",  int), 
   n_early_stopp ing=("Maxi m u m  numberof t r ai n ing epochs wit houtdev accura c y improv ement ", "opti on", "ne" , int),
   n _ex amp les = ( "N umber of exam p l es",  " option" , " ns", in t), 
   u se_ gp u=("Use G PU", "op ti on ",  " g",  int) , 
    ver si on= (" Mod el ve r sion",  "opt ion" ,"V " , s tr),
  m e t a_pa th =( "Opt ion al  path  tom eta .json t o use asbas e .","o pt ion", " m", Path),
  init_tok2v ec =(" Path t o pretrain ed weights for the toke n -to-vec tor  part s of  the mode ls.  See's pac y  pretr ain'.Exper im ent a l .", " o p ti on" ,"t2v", Pat h ) ,
   pa rs er_m ultitas ks=("Side objectiv e s f or parser CNN , e .g.' d ep ' o r  ' d ep, ta g '", " option", "pt",str),
   en t it y_multitas k s=( "S ide obj ectives  forN ER CNN,  e.g. 'de p' or 'de p, tag' " ,  "
 (optional)",_"option", "b",_str),
   _pipeline=("Comma-separated names_of_pipeline components",_"option",_"p", str),
 _  replace_components=("Replace_components from base model",_"flag", "R", bool),
__  vectors=("Model to load vectors from", "option", "v", str),
    width=("Width_of_CNN layers_of_Tok2Vec_component", "option", "cw", int),
 _  conv_depth=("Depth of CNN_layers of_Tok2Vec component", "option", "cd", int),
   _cnn_window=("Window_size for CNN_layers of Tok2Vec component", "option", "cW", int),
  _ cnn_pieces=("Maxout size for CNN layers_of Tok2Vec component._1_for_Mish", "option", "cP", int),
_   use_chars=("Whether to use_character-based embedding of Tok2Vec component", "flag",_"chr", bool),
    bilstm_depth=("Depth of_BiLSTM layers of Tok2Vec component (requires_PyTorch)", "option", "lstm", int),
 _ _embed_rows=("Number of embedding rows of_Tok2Vec component", "option",_"er", int),
_  _n_iter=("Number of iterations", "option", "n", int),
_  _n_early_stopping=("Maximum number of training epochs without_dev_accuracy improvement", "option",_"ne",_int),
_ _ n_examples=("Number of_examples",_"option", "ns",_int),
_   use_gpu=("Use GPU", "option",_"g",_int),
    version=("Model version", "option",_"V", str),
  __meta_path=("Optional path to meta.json_to use as base.", "option",_"m", Path),
    init_tok2vec=("Path_to pretrained_weights for_the token-to-vector parts of the models. See'spacy pretrain'. Experimental.", "option", "t2v",_Path),
    parser_multitasks=("Side objectives_for parser CNN, e.g._'dep' or_'dep,tag'",_"option", "pt", str),
__ _entity_multitasks=("Side objectives for NER CNN, e.g. 'dep'_or 'dep,tag'",_"