+sg<ddlmZddlZddlZddlZddlZddlmZmZddl m Z ddl m Z m Z m Z ddlmZddlmZddlmZdd lmZmZmZddlZddlZdd lmZmZdd lmZdd lmZ dd l!m"Z"m#Z#ddl$m%Z%ddlm&Z&ddl'm(Z(ddlm)Z)ddl*m+Z+ddl,m-Z-ddl.m/Z/m0Z0ddl1m2Z3ddl4m5Z5m6Z6ddl7m8Z8ddl9m:Z:m;Z;mZ>m?Z?m@Z@mAZAejeCZDerddlEmFZFddlGmHZHddlImJZJGdde)ZKgdZLgd ZMd%d!ZNe Gd"d#eZOd&d$ZPy)') annotationsN)Counter defaultdict)copy) dataclassfieldfields)Path)python_versionindent) TYPE_CHECKINGAnyLiteral)CardData ModelCard) dataset_info) model_info) EvalResulteval_results_to_model_index) yaml_dump)nn)tqdm)TrainerCallback)CodeCarbonCallback)make_markdown_table)TrainerControl TrainerState __version__)StaticEmbedding Transformer)$SentenceTransformerTrainingArguments)fullnameis_accelerate_availableis_datasets_available)Dataset DatasetDictIterableDatasetValue)SentenceEvaluator)SentenceTransformer)SentenceTransformerTrainerceZdZdfd Z ddZ ddZ ddZ d dZxZS) ModelCardCallbackct|||_||_|jjj Dcgc]}t |tr|}}|r|d|jj_ ||jj_|jjjdycc}w)Nrgenerated_from_trainer) super__init__trainerdefault_args_dictcallback_handler callbacks isinstancermodelmodel_card_datacode_carbon_callbackadd_tags)selfr4r5callbackr7 __class__s S/var/www/html/venv/lib/python3.12/site-packages/sentence_transformers/model_card.pyr3zModelCardCallback.__init__.s  !2!LL99CC ($67   AJ1GMM ) ) >07 %%- %%../GH sB<c ddlm}m}m}|jj rT|j j|jj |j jd|j _|jjrT|j j|jj|j jd|j _ t|jjtr.t|jjj} n|jjg} d} | t!| kr]| | } t| |||fr5t#| dr)| j| vr| j%| j| dz } | t!| kr]|j j'| y)Nr)AdaptiveLayerLossMatryoshka2dLossMatryoshkaLosstrainevalloss)sentence_transformers.lossesrBrCrDr4 train_datasetr:extract_dataset_metadatatrain_datasets eval_dataset eval_datasetsr8rGdictlistvalueslenhasattrappend set_losses) r=argsstatecontrolr9kwargsrBrCrDlossesloss_idxrGs r@ on_init_endzModelCardCallback.on_init_end>sk ed << % %383H3H3a3a **E,A,A,P,PRY4E ! ! 0 << $ $272G2G2`2` ))5+@+@+N+NPV3E ! ! / dll'' .$,,++2245Fll''(FV$(#D4.2CEU!VWD&)IIV+ dii( MHV$ ((0c bhd}|j}|jD cic] \}} ||vs || c} }|j_|jD cic],\}} ||vr#||jvr| |j|k7r|| .c} }|j_ycc} }wcc} }w)N>do_evaldo_testdo_trainrun_name hub_token report_to eval_delay eval_steps output_dir save_steps logging_dir logging_steps save_strategylogging_strategysave_total_limitgreater_is_betterpush_to_hub_tokensamples_per_labelshow_progress_barlogging_first_stepevaluation_strategymetric_for_best_model)to_dictitemsr:all_hyperparametersr5non_default_hyperparameters) r=rVrWrXr9rY ignore_keys args_dictkeyvalues r@on_train_beginz ModelCardCallback.on_train_beginfs 0LLN )2):5 %3c>TCJ5 1 (oo/= U+%#1G1G*GEUYUkUkloUpLp J= 95 = s B%B% 1B+c |Dcic];}|jdsdj|jddd||=}}t|dk(r d|vrd|di}|jj rR|jj dd|j k(r)|jj dj|y|jj j|j|j d |ycc}w) N_loss _rHrGValidation LossStepEpochr) endswithjoinsplitrRr: training_logs global_stepupdaterTepoch) r=rVrWrXr9metricsrYr{ loss_dicts r@ on_evaluatezModelCardCallback.on_evaluatesLSlCVYVbVbcjVkSXXciinQR0173<?l l y>Q 6Y#6*If,=>I  ! ! / /%%33B7?5CTCTT  ! ! / / 3 : :9 E  ! ! / / 6 6"[[!--  ms D)Dc dht|z}|r|jjrW|jjdd|jk(r.||j |jjdd<y|jjj |j |j||j dyy)NrGrr Training Loss)rrr)setr:rrpoprTr)r=rVrWrXr9logsrYkeyss r@on_logzModelCardCallback.on_logsx#d)# %%33))77;FCuGXGXXKOPTPXPXPZK[%%33B7H%%33::!& % 1 1)-dhhj)9 r])r4r-r5dict[str, Any]returnNone) rVr#rWrrXrr9r,rr) rVr#rWrrXrr9r,rdict[str, float]rr) rVr#rWrrXrr9r,rrrr) __name__ __module__ __qualname__r3r\r}rr __classcell__)r?s@r@r/r/-sI &12&1&1 &1 # &1 &1P) 2) )  ) # )  ) V2  #  "  42  #    r]r/) languagelicense library_nametagsdatasetsr pipeline_tagwidget model-indexco2_eq_emissions base_model)r9r4eval_results_dictctttjtjd}t r ddlm}||d<tr ddlm}||d<ddl m}||d<|S)N)pythonsentence_transformers transformerstorchrr accelerater tokenizers) r sentence_transformers_versionrr rr%rr&rr)versionsaccelerate_versiondatasets_versiontokenizers_versions r@ get_versionsrs^ "!>$00"" H  @!3</</H\ Or]ceZdZUdZeeZded<dZded<dZ ded<dZ ded <eeZ d ed <eeZ d ed <d Z ded<edZded<dZded<eddZded<eddZded<eedZded<eedZded<eedZded<eedZd ed!<eedZd ed"<eddZded#<eedZd ed$<eddZd%ed&<eedZd'ed(<eddZd)ed*<eddd+Zd,ed-<eedd.Zd/ed0<ed1dZd2ed3<ed4dZ d5ed6<ed7dZ!ded8<ed9dZ"ded:<ee#dZ$d'ed;<eddd+Z%dZ&dUdVd?Z'dWd@Z(dXdAZ)dYdBZ*dZdCZ+d[dDZ,d\d]dEZ- d^dFZ. d_dGZ/d`dHZ0dadIZ1d\dbdJZ2dcdKZ3dddLZ4dedMZ5dTdNZ6dfdOZ7dPZ8dgdQZ9dfdRZ:d\dhdSZ;y)i SentenceTransformerModelCardDataaA dataclass storing data used in the model card. Args: language (`Optional[Union[str, List[str]]]`): The model language, either a string or a list, e.g. "en" or ["en", "de", "nl"] license (`Optional[str]`): The license of the model, e.g. "apache-2.0", "mit", or "cc-by-nc-sa-4.0" model_name (`Optional[str]`): The pretty name of the model, e.g. "SentenceTransformer based on microsoft/mpnet-base". model_id (`Optional[str]`): The model ID when pushing the model to the Hub, e.g. "tomaarsen/sbert-mpnet-base-allnli". train_datasets (`List[Dict[str, str]]`): A list of the names and/or Hugging Face dataset IDs of the training datasets. e.g. [{"name": "SNLI", "id": "stanfordnlp/snli"}, {"name": "MultiNLI", "id": "nyu-mll/multi_nli"}, {"name": "STSB"}] eval_datasets (`List[Dict[str, str]]`): A list of the names and/or Hugging Face dataset IDs of the evaluation datasets. e.g. [{"name": "SNLI", "id": "stanfordnlp/snli"}, {"id": "mteb/stsbenchmark-sts"}] task_name (`str`): The human-readable task the model is trained on, e.g. "semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more". tags (`Optional[List[str]]`): A list of tags for the model, e.g. ["sentence-transformers", "sentence-similarity", "feature-extraction"]. .. tip:: Install `codecarbon `_ to automatically track carbon emission usage and include it in your model cards. Example:: >>> model = SentenceTransformer( ... "microsoft/mpnet-base", ... model_card_data=SentenceTransformerModelCardData( ... model_id="tomaarsen/sbert-mpnet-base-allnli", ... train_datasets=[{"name": "SNLI", "id": "stanfordnlp/snli"}, {"name": "MultiNLI", "id": "nyu-mll/multi_nli"}], ... eval_datasets=[{"name": "SNLI", "id": "stanfordnlp/snli"}, {"name": "MultiNLI", "id": "nyu-mll/multi_nli"}], ... license="apache-2.0", ... language="en", ... ), ... ) )default_factoryzstr | list[str] | NonerN str | Noner model_namemodel_idlist[dict[str, str]]rLrNzjsemantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and morestr task_namec gdS)N)sentence-transformerssentence-similarityzfeature-extractionrr]r@z)SentenceTransformerModelCardData.s ! r]zlist[str] | Noner deprecatedzLiteral['deprecated']generate_widget_examplesF)defaultinitrbase_model_revision)rrrrxrwz.dict[SentenceEvaluator, dict[str, Any]] | Nonerzlist[dict[str, float]]rrpredict_examplelabel_example_listzCodeCarbonCallback | Noner;dict[str, str] citationsz int | Nonebest_model_step)rrreprz!SentenceTransformerTrainer | Noner4)rrr list[str]rTbool first_saverint widget_steprrrrversionzSentenceTransformer | Noner9c|j }t|jtr|jg|_|j|j||_|j|j ||_|j rJ|j jddk7r+tjd|j dd|_yyy)N)infer_languages/rHz The provided z} model ID should include the organization or user, such as "tomaarsen/mpnet-base-nli-matryoshka". Setting `model_id` to None.) rr8rvalidate_datasetsrLrNrcountloggerwarning)r=rs r@ __post_init__z.SentenceTransformerModelCardData.__post_init__Bs"mm+ dmmS )!]]ODM"44T5H5HZi4j!33D4F4FXg3h ==T]]005: NN 01^^ !DM ;=r]cRg}|D]}d|vr d|vr|d|d<d|vr t|d}|jrq|rod|jvra|jjd}|Dt|tr|g}|D],}||j vs|j j |.|j|jvr&|jj |j |j ||S#t$r"tjd|dd|d=Y?wxYw)NnameidrzThe dataset `id` z5 does not exist on the Hub. Setting the `id` to None.) get_dataset_infocardDatagetr8rrrTrr Exceptionrr)r= dataset_listroutput_dataset_listdatasetinfodataset_languagers r@rz2SentenceTransformerModelCardData.validate_datasetsRs7 # 0GW$7?&-dmGFOw6+GDM:D}}Z4===X+/==+<+SentenceTransformerModelCardData.set_losses..join_lists:6{Qyy-7&*DD!9 r]zloss:)rZrrr) citationr?rrrrPrvrTrr<)r=rZrrGinverted_citationsrrs r@rUz+SentenceTransformerModelCardData.set_lossesss # &   D 59]] $..112  ).'oo/ 6ND( x ( / / 5 6  OaNfNfNhi:J(F)F+X5i ]c2dUY4>>3J3JD3P2de$tf~ef  j2des##C$C3-C9 C>$ C0/C0c||_yN)r)r=steps r@set_best_model_stepz4SentenceTransformerModelCardData.set_best_model_steps #r]c t|tryt|tr t|}g|_t t jt|jd}d}t|jdddD]g\}}||jjDcgc]%\}}t|tr|jd vr|'}}}||j|} t!| } | d k(ryi} t#| j%t j&t)| t+|| D](\} } t-d | jD| | <*t/t1| jd \}}|d|t||dddd}}|D]<} | | jDcgc]\}}|dk7r|j3ds|}}}t!|dkr|r|j5}| |jDcgc]\}}|dk7r|j3ds|}}}t!|dk(r|j7|n|j9|dt!|dkr|rt!|dkr|jj9|d t j&|ddt!|dz d|dd|_?jycc}}wcc}}wcc}}w)N)r)kzComputing widget examplesexampleF)descunitleave>string large_stringrc3dK|](\}}|dk7r|jds t|*yw) dataset_name_prompt_lengthN)rrR.0r{r|s r@ zGSentenceTransformerModelCardData.set_widget_examples..s4#"Un,S\\BR5SJ#s.0c |dS)NrHr)xs r@rzFSentenceTransformerModelCardData.set_widget_examples..s AaDr]r{rrrrH)source_sentence sentences)r8r)r'r(rrrandomchoicesrPrrrvfeaturesr*dtypeselect_columnsrR enumerateselectsamplerangeminsumzipsortedrrextendrTr)r=r dataset_namesnum_samples_to_checkr num_samplescolumnfeaturecolumns str_dataset dataset_sizelengthsidxrindicesrtarget_indicesbackup_indicesr{sentencer  backup_idx backup_samples r@set_widget_examplesz4SentenceTransformerModelCardData.set_widget_exampless g /  gw '!'2G tGLLN/Cq IJ #)-    !(C)[`* 8 5 %L+(/|'<'E'E'K'K'M#FGgu-'--C]2]G ",/>>wGK{+Lq G(""6==|1DL`bnHo#pq   V ##&,lln#   fW]]_.IJJGQ-4\k-BDQ\Q]I^_cac_cIdDeNN& 5*5S)9)?)?)A%Xn,S\\BR5S  )nq(^!/!3!3!5J.9-D-J-J-L%)C.0FV9W!%M% =)Q.!((7"((q)9:)nq(^y>A% ""(1! 6==QZ[\[]Q^befobpstbtCuv(1!}$7 5;8 5 4%s2*K7 "K= ("L cddlm}t||j|<t |dr-|j x}rt ||r$|jDcgc]}|j }}nt |tr|g}|jd}d}n@|jjj}|jjj}|jD cic] \}} ||vs || } }} |jr4|jdd|k(r|jdj| y|jj!||d| yyycc}wcc} }w)Nr)SequentialEvaluatorprimary_metricrrr) sentence_transformers.evaluationr+rrrSr,r8 evaluatorsrr4rWrrrvrrrT) r= evaluatorrr+primary_metrics sub_evaluatorrrr{r|training_log_metricss r@set_evaluation_metricsz7SentenceTransformerModelCardData.set_evaluation_metricssPH,0My) 9. / H`H`5`_5`)%89U^UiUi"jM=#?#?"j"jOS1#2"3||#||))55 **00AH#k:3TW[jTjCJ#k #k!!d&8&8&zz
    z
)LabelExamples)rrPrrTrrRadd num_classesrvr9labelsr8rrr) r=rnum_examples_per_labelexamplesfinished_labelsrr5r6 example_sets r@set_label_examplesz3SentenceTransformerModelCardData.set_label_exampless !"t$% F&>D7OEO+&&d4j\'?@x'+AA#''.?#t'7'77 '/nn&6 # #{6:ZZ5F5F:V[]`Ka**51gl"RWW[%99GC # # s!A#Dc 4t|tr=|jDcgc]\}}|j||D]}|!c}}}S|rt j d|rd}|xs|j jt|jd}|j jrR|j|j jvr0|j j|jj|d<|jx}rt|jd}|jdrUd|vrQ|t!ddjd}|d|d<|d jd dx}rt!|d k(r||d <|gScc}}}w) N)rz _dataset_\d+)rrsizerzhf://datasets/@rrHr(revision)r8r(rvinfer_datasetsrematchrrrrsplits num_examplesdownload_checksumsrPr startswithrR) r=rr sub_datasetdataset_output checksumssource source_partsrFs r@rGz/SentenceTransformerModelCardData.infer_datasetss g{ +29-L+#22;\2Z  BHH_lCL!=GLL$=$='  <<  7==GLL4G4G#G%,\\%8%8%G%T%TN6 " 22 29 2)..*+A.F  !12sf}%c*:&;&=>DDSI '3At$ ,Q 5 5c :1 ==H=3x=TVCV19N:.7s$Fc & |siSd|vrt|trt||d<|jDcgc]}d|d c}|d<i|d<t|tr|jD]t}|dd|}|d}t|tr|j j |}t|tr*d |vr&|d jd j}d } n|D cgc] } t| }} d } dtt|dd| tt|t|z dd| tt|dd| dd|d|<t|ttfrTt|} dt!| D cic])} | t| d kDrdnd| | t|z d+c} d|d|<^t|t"rVdtt|dtt|t|z dtt|ddd|d|<t|t$rt|D cgc] } t| c} } t| d k(rddt|did|d|<dt| dt| t| z ddt| ddd|d|<at'|id|d|<wd5d}ddi|dj)D cic] \} }| |d c}} ddi|dj)D cic]\} }| ||dc}} g}t+t-|j/d d!d"|d#<|dd$|d%<t|d%t%|d%d}g}t1|D]}i}|jD]}|d%||}t|t$r"t|d&kDrt |dd&dd'd(z}t|trt|dkDr|ddd)z}t |j/d*d+}d|d||<|j3|t+t-|j/d d!d"|d,<d-t'|i|d.<t5|d/r>|j7} t9j:|d01}t+d2|d3d"|d.d4<|Scc}wcc} wcc} wcc} wcc}} wcc}} w#t<$rt |}YNwxYw)6a Given a dataset, compute the following: * Dataset Size * Dataset Columns * Dataset Stats - Strings: min, mean, max word count/token length - Integers: Counter() instance - Floats: min, mean, max range - List: number of elements or min, mean, max number of elements * 3 Example samples * Loss function name - Loss function config rCzzrstatsNrrattention_maskrH)dimtokens charactersrr)rmeanmax)rdatar~r7z.2%floatrPz elementsz.2fr\cZddjd|jDzdzS)Nz
  • z
  • c30K|]\}}|d|yw)z: Nrrs r@rzaSentenceTransformerModelCardData.compute_dataset_metrics..to_html_list..s 4f:3PUuBug5F4fsz
)rrv)r\s r@ to_html_listzNSentenceTransformerModelCardData.compute_dataset_metrics..to_html_lists.!K$4$44fY]YcYcYe4f$ffiuuur]typerdetails-:|--|z stats_tabler r>rrz, ...]z... z
examples_tabler$rGget_config_dictrr z```json z ``` config_code)r\rO)r8r'rR column_namesrr9tokenizerOrtolistroundrr[rrrrr^rPr$rvr rreplacerrTrSrijsondumps TypeError)r=rrrGr subsectionfirst tokenizedr!suffixr&counterr{lstrar| stats_linesrexamples_lines sample_idxrconfig str_configs r@compute_dataset_metricsz8SentenceTransformerModelCardData.compute_dataset_metrics.s&I  %*Wg*F#&wF(4V #E"1Is 3h >!%-#e*t2C %et u 4J..tV%%g. /.44G4W4_4_`egl4mos-tL) *  V 4* +))+F )!ZZq9 399ZLPU9VX\2]L  /G#`#M!'G0 e"t< ) [  )s5 S$S .S#S( ?S-,S3 *S99TTc x|rW|rft|trt|t|k7st|tr/t|dk7r!tj d|d|d|dg}|s|j |}t|trt|j|j|Dcgc]j\}}}|j||t|jjtr|jj|n|jjl}}}}n+|j||d|jjg}|dk(r?t|Dcgc]}|jddc}}|r|j!d ||j#|Scc}}}wcc}w) NrHzThe number of `z?_datasets` in the model card data does not match the number of z1 datasets in the Trainer. Removing the provided `z$_datasets` from the model card data.rrErCz dataset_size:)r8r(rRr'rrrGrrrQr~r4rGrOrrr<r) r=rdataset_metadata dataset_typer dataset_valuermetadatanum_training_sampless r@rKz9SentenceTransformerModelCardData.extract_dataset_metadatas G[1c:J6KsSZ|6[w0S9I5Ja5O%l^3rssAA..:^;_a$& ##'#6#6w#? ';/FI (8:JF $ $ B m\ 00%$;EdllFWFWY];^ )),7dhdpdpdudu $ $%)$@$@JZ[\J]_c_k_k_p_p$q#r  7 "#&P`'aH VQ(?'a#b # .B-CDE%%&677' $(bs .sQ3CNN4#:6Qs!rHrcb t|dr|jS |S#t$rY|SwxYw)z^Try to convert a value from a Numpy or Torch scalar to pure Python, if not already pure Pythonr)rSitemr)r|s r@try_to_pure_pythonzPSentenceTransformerModelCardData.format_eval_metrics..try_to_pure_pythonsBug.$zz|+/ ! s ! ..**r)Metricr*) class_name descriptionr table_linesr-unknownUnknown)r task_typerr metric_name metric_type metric_valuerrr*c3&K|] }|d yw)rNr)rlines r@rzGSentenceTransformerModelCardData.format_eval_metrics..Us1pT$x.1psrrrdretable) eval_metricsrr)r|rrr)rrvgetattrallrrRrMrnrrTr$rr8rr^rlowerrotitlerrrrPrrr)r=r all_metrics eval_resultsr/rr,r{r|r metric_keyrrrrgrouped_eval_metrics eval_metricreval_metric_mappingeval_metric_metricsgrouped_eval_metricgrouped_eval_metric_metricsrs @r@format_eval_metricsz4SentenceTransformerModelCardData.format_eval_metrics s.   "&"8"8">">"@7 ( Iw9fd3D$Y0@$GNQ',,.QQIPY:33s4y1}/6YY!n&?&?s &K%3CIMO%DN IP X*#us.u55XGX18 -J 6@>5Q:,b1Wa!^3 "% a"8!9<|Q/ K$//K"9fd;L   "*9"5#.$0#.     5YYe\%9%9#s%C%K%KCQT%Ukt$.$6$6sC$@$F$F$H$.%1     w 'o7 (t "' 9KMXYfMg"hT4>4=#@"h "h"%&9": '; 9#.11pM`anMo1p.p+ -1D\1RR+/JJ#N37J>7ZZ!4M B`"d?HLQXHYD!4^!DEc+N;'7>>{>?Z[# 9&%++K8- 90$8  +>?R?V?VWd?e+f+n+nu,  (  1K,,./6t U  iZY, &#is:NN9NBN N c g|jD]-}|jD]}|vsj|/dfd }t|}|jDcgc]N}|Dcic]@}||d|jk(rd||vrt ||dnddn|j |dBc}P}}}t|}|d|vdScc}wcc}}w) Nc|dk(ry|dk(ry|dk(ry|dk(ry|jd ry j|d zS) NrrrrHrrYrr rGrr)rindex)r{eval_lines_keyss r@ sort_metricszKSentenceTransformerModelCardData.format_training_logs..sort_metrics}sSg~f}o%''||F#"((-1 1r]rrrrr) eval_linesexplain_bold_in_eval)r{rrr)rrrTrrrnrr) r=linesr{rsorted_eval_lines_keysrrrrs @r@format_training_logsz5SentenceTransformerModelCardData.format_training_logsts '' 0Ezz| 0o-#**3/ 0 0 2"(\!J** 2  <4#7#77%S 1-#FbIXXc3'(   )7 $$(J$6    s" C+AC 0C Cc f|jjj}dt|jdzt|j dd|j dk(|j|jt|jdz ddi}|jr|j|dd <|S) Nrr codecarbonz fine-tuningYir ) emissionsenergy_consumedrQ training_typeon_cloud cpu_modelram_total_size hours_used hardware_used) r;tracker_prepare_emissions_datar^rrrrrrnduration gpu_model)r=emissions_dataresultss r@get_codecarbon_dataz4SentenceTransformerModelCardData.get_codecarbon_datas22::RRT ">#;#;),D,>,>(?#(E $%  % %))11))11==I   d668 9*.)F)F)H %&.2jj.Y.Y.[ *+%(_ >" :: ( (-$11 0 c$**//1N1N1V1VWZ\_1`1f1f1hi + ,0CJ+ ,! &C NN3 % &o  W  !LSERS   O!GuMNN OsH3J6(K;K 'K66 KK K3K..K36 L"?LL"c t|jjDcic]\}}|tvs|dgfvs||c}}d|j Scc}}w)NF) sort_keys line_break)rrurv YAML_FIELDSstrip)r=rr{r|s r@to_yamlz(SentenceTransformerModelCardData.to_yamls\*.,,.*>*>*@ sJCC;DV[`imoqhr[rS%Z s!  %'   ssA A A )rr)T)rrrr)rZzlist[nn.Module]rr)rrrr)rDataset | DatasetDictrr)r/r+rrrr)rr'rrr)rrrrrr)rz Dataset | IterableDataset | NonerrrGz'dict[str, nn.Module] | nn.Module | Nonerr)rrrzLiteral['train', 'eval']rr)r9r,rr)rrrr)rrrFrrr)rstr | list[str]rr)rrrr)rrrrrr)rz1dict[Literal['co2_eq_emissions'], dict[str, Any]])rr)$D5L> * >{1{%{6 {  {z%8,%8Nf%8 %8N! ! &>,i V$ L&DLr]rcttjdz }tj|j |d}|j S)Nzmodel_card_template.mdu🤗) card_data template_pathhf_emoji)r __file__parentr from_templater:content)r9r model_cards r@generate_model_cardrs?N)),DDM((53H3HXepvwJ   r]r)r9r,rr)Q __future__rrploggingr rH collectionsrrr dataclassesrrr pathlibr platformr textwrapr typingrrrrrhuggingface_hubrrrrrrhuggingface_hub.repocard_datarrhuggingface_hub.utilsrrtqdm.autonotebookrrtransformers.integrationsrtransformers.modelcardrtransformers.trainer_callbackrrrr rsentence_transformers.modelsr!r"#sentence_transformers.training_argsr#sentence_transformers.utilr$r%r&rr'r(r)r* getLoggerrr2sentence_transformers.evaluation.SentenceEvaluatorr+)sentence_transformers.SentenceTransformerr,sentence_transformers.trainerr-r/rrrrrrr]r@rs"  ,00#.. /<8Q+"(86FNET__EE   8 $TMHUUp  ;, L xL  L ^r]