msgid ""
msgstr ""
"Project-Id-Version: tabnet 0.8.0.9000\n"
"POT-Creation-Date: 2026-06-10 10:46+0200\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\n"
"Language: \n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=UTF-8\n"
"Content-Transfer-Encoding: 8bit\n"

#: dials.R:3
msgid ""
"Package {.pkg dials} is needed for this function to work. Please install it."
msgstr ""

#: dials.R:142
msgid "{.var cat_emb_dim} cannot be used as a {.fun tune} parameter yet."
msgstr ""

#: hardhat.R:111
msgid "{.fn tabnet_fit} is not defined for a {.type {class(x)[1])}}."
msgstr ""

#: hardhat.R:266
msgid "{.fn tabnet_pretrain} is not defined for a {.type {class(x)[1])}}."
msgstr ""

#: hardhat.R:344
msgid "{.var {tabnet_model}} is not recognised as a proper TabNet model"
msgstr ""

#: hardhat.R:350
msgid "The model was trained for less than {.val {from_epoch}} epochs"
msgstr ""

#: hardhat.R:362
msgid "Found missing values in the {.var {names(outcomes)}} outcome column."
msgstr ""

#: hardhat.R:372 hardhat.R:484
msgid "Model dimensions don't match."
msgstr ""

#: hardhat.R:397
msgid ""
"No model serialized weight can be found in {.var {tabnet_model}}, check the "
"model history"
msgstr ""

#: hardhat.R:405
msgid "Using {.fn tabnet_pretrain} from a model is not currently supported."
msgstr ""

#: hardhat.R:406
msgid "Pretraining will start from a new network initialization"
msgstr ""

#: hardhat.R:445
msgid "The model was trained for less than {.val {epoch}} epochs"
msgstr ""

#: hardhat.R:522
msgid ""
"Mixed multi-outcome type {.type {unique(purrr::map_chr(outcome_ptype, "
"~class(.x)[[1]]))}} is not supported"
msgstr ""

#: hardhat.R:530
msgid "Unknown outcome type {.type {class(outcome_ptype)}}"
msgstr ""

#: hardhat.R:537
msgid "Outcome is factor and the prediction type is {.type {type}}."
msgstr ""

#: hardhat.R:540
msgid "Outcome is numeric and the prediction type is {.type {type}}."
msgstr ""

#: hardhat.R:629
msgid "No factor levels found in outcomes : {str(outcomes)}"
msgstr ""

#: hardhat.R:645
msgid ""
"Factor level {.var {nm}} not found at tree level {lvl} (outcomes column {."
"var {level_cols[lvl - 1L]}})"
msgstr ""

#: loss.R:261
msgid "The criterion module has reduction={.val {module_reduction}}"
msgstr ""

#: loss.R:262
msgid "but nn_mc_loss was called with reduction={.val {reduction}}."
msgstr ""

#: loss.R:263
msgid "i"
msgstr ""

#: loss.R:263
msgid "The module's reduction will be used."
msgstr ""

#: loss.R:265
msgid "mc_loss_reduction_mismatch"
msgstr ""

#: loss.R:297
msgid "`criterion` must be a function or an `nn_module`."
msgstr ""

#: loss.R:298
msgid "x"
msgstr ""

#: loss.R:298
msgid "Got: {.class {class(criterion)[1]}}"
msgstr ""

#: loss.R:300
msgid "mc_loss_invalid_criterion"
msgstr ""

#: model_explain.R:47
msgid "{.fn tabnet_explain} is not defined for a {.type {class(object)[1]}}."
msgstr ""

#: model_pretraining.R:121 model_training.R:506
msgid "{.var optimizer} must be resolved into a torch optimizer generator."
msgstr ""

#: model_pretraining.R:135 model_training.R:519
msgid ""
"Currently only the {.str step} and {.str reduce_on_plateau} scheduler are "
"supported."
msgstr ""

#: model_pretraining.R:182 model_training.R:566
#, c-format
msgid "[Epoch %03d] Loss: %3f"
msgstr ""

#: model_pretraining.R:184 model_training.R:568
#, c-format
msgid "[Epoch %03d] Loss: %3f, Valid loss: %3f"
msgstr ""

#: model_pretraining.R:199 model_training.R:584
#, c-format
msgid "Early-stopping at epoch {.val epoch}"
msgstr ""

#: model_pretraining.R:224 model_training.R:609
msgid ""
"Computing importances for a dataset with size {.val {train_ds$.length()}}. \n"
"           This can consume too much memory. We are going to use a sample of "
"size 1e5. \n"
"           You can disable this message by using the "
"`importance_sample_size` argument."
msgstr ""

#: model_training.R:245
msgid "{.val {loss}} is not a valid loss for outcome of type {.type {dtype}}"
msgstr ""

#: model_training.R:256
msgid ""
"{.val {early_stopping_monitor}} is not a valid early-stopping metric to "
"monitor with {.val valid_split = {valid_split}}"
msgstr ""

#: model_training.R:498
msgid ""
"ancestor was configured. Expecting a tensor but got {.cls "
"{class(config$ancestor)}}"
msgstr ""

#: parsnip.R:474
msgid ""
"Package {.pkg parsnip} is needed for this function to work. Please install "
"it."
msgstr ""

#: parsnip.R:559
msgid "parsnip"
msgstr ""

#: tabnet_network.R:238 tabnet_network.R:405
msgid "{.var n_steps} should be a positive integer."
msgstr ""

#: tabnet_network.R:240 tabnet_network.R:407
msgid "{.var n_shared} and {.var n_independant} can't be both zero."
msgstr ""

#: tabnet_network.R:463
msgid ""
"Please choose either {.val sparsemax}, {.val sparsemax15}, {.val entmax} or "
"{.val entmax15} as {.var mask_type}"
msgstr ""

#: tabnet_network.R:627
msgid ""
"{.var cat_emb_dim} length must be 1 or the number of categorical predictors, \n"
"                  got length {.val {length(self$cat_emb_dims)}} for {.val "
"{length(cat_dims)}} \n"
"                  categorical predictors"
msgstr ""

#: utils.R:68
msgid ""
"The provided hierarchical object is not recognized with a valid format that "
"can be checked"
msgstr ""

#: utils.R:72
msgid ""
"The attributes or colnames in the provided hierarchical object use the "
"following reserved names:\n"
"                {.vars {actual_names[actual_names %in% reserved_names]}}. \n"
"                Please change those names as they will lead to unexpected "
"tabnet behavior."
msgstr ""

#: utils.R:157 utils.R:160
msgid ""
"Currently only {.val adam} is supported as character for {.var optimizer}."
msgstr ""
