TextEmbeddingModel: Text embedding model
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TextEmbeddingModel R Documentation
Text embedding model
Description
This R6 class stores a text embedding model which can be used to tokenize, encode, decode, and embed
raw texts. The object provides a unique interface for different text processing methods.
Value
Objects of class TextEmbeddingModel transform raw texts into numerical representations which can be used
for downstream tasks. For this aim objects of this class allow to tokenize raw texts, to encode tokens to sequences
of integers, and to decode sequences of integers back to tokens.
Public fields
last_training('list()')
List for storing the history and the results of the last training. This
information will be overwritten if a new training is started.
tokenizer_statistics('matrix()')
Matrix containing the tokenizer statistics for the creation of the tokenizer
and all training runs according to Kaya & Tantuğ (2024).
Kaya, Y. B., & Tantuğ, A. C. (2024). Effect of tokenization granularity for Turkish
large language models. Intelligent Systems with Applications, 21, 200335.
https://doi.org/10.1016/j.iswa.2024.200335
Methods
Public methods
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Method configure()
Method for creating a new text embedding model
Usage
TextEmbeddingModel$configure(
model_name = NULL,
model_label = NULL,
model_language = NULL,
method = NULL,
ml_framework = "pytorch",
max_length = 0,
chunks = 2,
overlap = 0,
emb_layer_min = "middle",
emb_layer_max = "2_3_layer",
emb_pool_type = "average",
model_dir = NULL,
trace = FALSE
)
Arguments
model_namestring containing the name of the new model.
model_labelstring containing the label/title of the new model.
model_languagestring containing the language which the model
represents (e.g., English).
methodstring determining the kind of embedding model. Currently
the following models are supported:
method="bert" for Bidirectional Encoder Representations from Transformers (BERT),
method="roberta" for A Robustly Optimized BERT Pretraining Approach (RoBERTa),
method="longformer" for Long-Document Transformer,
method="funnel" for Funnel-Transformer,
method="deberta_v2" for Decoding-enhanced BERT with Disentangled Attention (DeBERTa V2),
method="glove"`` for GlobalVector Clusters, and method="lda"' for topic modeling. See
details for more information.
ml_frameworkstring Framework to use for the model.
ml_framework="tensorflow" for 'tensorflow' and ml_framework="pytorch"
for 'pytorch'. Only relevant for transformer models. To request bag-of-words model
set ml_framework=NULL.
max_lengthint determining the maximum length of token
sequences used in transformer models. Not relevant for the other methods.
chunksint Maximum number of chunks. Must be at least 2.
overlapint determining the number of tokens which should be added
at the beginning of the next chunk. Only relevant for transformer models.
emb_layer_minint or string determining the first layer to be included
in the creation of embeddings. An integer correspondents to the layer number. The first
layer has the number 1. Instead of an integer the following strings are possible:
"start" for the first layer, "middle" for the middle layer,
"2_3_layer" for the layer two-third layer, and "last" for the last layer.
emb_layer_maxint or string determining the last layer to be included
in the creation of embeddings. An integer correspondents to the layer number. The first
layer has the number 1. Instead of an integer the following strings are possible:
"start" for the first layer, "middle" for the middle layer,
"2_3_layer" for the layer two-third layer, and "last" for the last layer.
emb_pool_typestring determining the method for pooling the token embeddings
within each layer. If "cls" only the embedding of the CLS token is used. If
"average" the token embedding of all tokens are averaged (excluding padding tokens).
"cls is not supported for method="funnel".
model_dirstring path to the directory where the
BERT model is stored.
tracebool TRUE prints information about the progress.
FALSE does not.
Details
In the case of any transformer (e.g.method="bert",
method="roberta", and method="longformer"),
a pretrained transformer model must be supplied via model_dir.
Returns
Returns an object of class TextEmbeddingModel.
Method load_from_disk()
loads an object from disk
and updates the object to the current version of the package.
Usage
TextEmbeddingModel$load_from_disk(dir_path)
Arguments
dir_pathPath where the object set is stored.
Returns
Method does not return anything. It loads an object from disk.
Method load()
Method for loading a transformers model into R.
Usage
TextEmbeddingModel$load(dir_path)
Arguments
dir_pathstring containing the path to the relevant
model directory.
Returns
Function does not return a value. It is used for loading a saved
transformer model into the R interface.
Method save()
Method for saving a transformer model on disk.Relevant
only for transformer models.
Usage
TextEmbeddingModel$save(dir_path, folder_name)
Arguments
dir_pathstring containing the path to the relevant
model directory.
folder_namestring Name for the folder created within the directory.
This folder contains all model files.
Returns
Function does not return a value. It is used for saving a transformer model
to disk.
Method encode()
Method for encoding words of raw texts into integers.
Usage
TextEmbeddingModel$encode(
raw_text,
token_encodings_only = FALSE,
to_int = TRUE,
trace = FALSE
)
Arguments
raw_textvectorcontaining the raw texts.
token_encodings_onlybool If TRUE, only the token
encodings are returned. If FALSE, the complete encoding is returned
which is important for some transformer models.
to_intbool If TRUE the integer ids of the tokens are
returned. If FALSE the tokens are returned. Argument only applies
for transformer models and if token_encodings_only=TRUE.
tracebool If TRUE, information of the progress
is printed. FALSE if not requested.
Returns
list containing the integer or token sequences of the raw texts with
special tokens.
Method decode()
Method for decoding a sequence of integers into tokens
Usage
TextEmbeddingModel$decode(int_seqence, to_token = FALSE)
Arguments
int_seqencelist containing the integer sequences which
should be transformed to tokens or plain text.
to_tokenbool If FALSE plain text is returned.
If TRUE a sequence of tokens is returned. Argument only relevant
if the model is based on a transformer.
Returns
list of token sequences
Method get_special_tokens()
Method for receiving the special tokens of the model
Usage
TextEmbeddingModel$get_special_tokens()
Returns
Returns a matrix containing the special tokens in the rows
and their type, token, and id in the columns.
Method embed()
Method for creating text embeddings from raw texts.
This method should only be used if a small number of texts should be transformed
into text embeddings. For a large number of texts please use the method embed_large.
In the case of using a GPU and running out of memory while using 'tensorflow' reduce the
batch size or restart R and switch to use cpu only via set_config_cpu_only. In general,
not relevant for 'pytorch'.
Usage
TextEmbeddingModel$embed(
raw_text = NULL,
doc_id = NULL,
batch_size = 8,
trace = FALSE,
return_large_dataset = FALSE
)
Arguments
raw_textvector containing the raw texts.
doc_idvector containing the corresponding IDs for every text.
batch_sizeint determining the maximal size of every batch.
tracebool TRUE, if information about the progression
should be printed on console.
return_large_dataset'bool' If TRUE the retuned object is of class
LargeDataSetForTextEmbeddings. If FALSE it is of class EmbeddedText
Returns
Method returns an object of class EmbeddedText or LargeDataSetForTextEmbeddings. This object
contains the embeddings as a data.frame and information about the
model creating the embeddings.
Method embed_large()
Method for creating text embeddings from raw texts.
Usage
TextEmbeddingModel$embed_large(
large_datas_set,
batch_size = 32,
trace = FALSE,
log_file = NULL,
log_write_interval = 2
)
Arguments
large_datas_setObject of class LargeDataSetForText containing the
raw texts.
batch_sizeint determining the maximal size of every batch.
tracebool TRUE, if information about the progression
should be printed on console.
log_filestring Path to the file where the log should be saved.
If no logging is desired set this argument to NULL.
log_write_intervalint Time in seconds determining the interval in which
the logger should try to update the log files. Only relevant if log_file is not NULL.
Returns
Method returns an object of class LargeDataSetForTextEmbeddings.
Method fill_mask()
Method for calculating tokens behind mask tokens.
Usage
TextEmbeddingModel$fill_mask(text, n_solutions = 5)
Arguments
textstring Text containing mask tokens.
n_solutionsint Number estimated tokens for every mask.
Returns
Returns a list containing a data.frame for every
mask. The data.frame contains the solutions in the rows and reports
the score, token id, and token string in the columns.
Method set_publication_info()
Method for setting the bibliographic information of the model.
Usage
TextEmbeddingModel$set_publication_info(type, authors, citation, url = NULL)
Arguments
typestring Type of information which should be changed/added.
developer, and modifier are possible.
authorsList of people.
citationstring Citation in free text.
urlstring Corresponding URL if applicable.
Returns
Function does not return a value. It is used to set the private
members for publication information of the model.
Method get_publication_info()
Method for getting the bibliographic information of the model.
Usage
TextEmbeddingModel$get_publication_info()
Returns
list of bibliographic information.
Method set_model_license()
Method for setting the license of the model
Usage
TextEmbeddingModel$set_model_license(license = "CC BY")
Arguments
licensestring containing the abbreviation of the license or
the license text.
Returns
Function does not return a value. It is used for setting the private
member for the software license of the model.
Method get_model_license()
Method for requesting the license of the model
Usage
TextEmbeddingModel$get_model_license()
Returns
string License of the model
Method set_documentation_license()
Method for setting the license of models' documentation.
Usage
TextEmbeddingModel$set_documentation_license(license = "CC BY")
Arguments
licensestring containing the abbreviation of the license or
the license text.
Returns
Function does not return a value. It is used to set the private member for the
documentation license of the model.
Method get_documentation_license()
Method for getting the license of the models' documentation.
Usage
TextEmbeddingModel$get_documentation_license()
Arguments
licensestring containing the abbreviation of the license or
the license text.
Method set_model_description()
Method for setting a description of the model
Usage
TextEmbeddingModel$set_model_description(
eng = NULL,
native = NULL,
abstract_eng = NULL,
abstract_native = NULL,
keywords_eng = NULL,
keywords_native = NULL
)
Arguments
engstring A text describing the training of the classifier,
its theoretical and empirical background, and the different output labels
in English.
nativestring A text describing the training of the classifier,
its theoretical and empirical background, and the different output labels
in the native language of the model.
abstract_engstring A text providing a summary of the description
in English.
abstract_nativestring A text providing a summary of the description
in the native language of the classifier.
keywords_engvectorof keywords in English.
keywords_nativevectorof keywords in the native language of the classifier.
Returns
Function does not return a value. It is used to set the private members for the
description of the model.
Method get_model_description()
Method for requesting the model description.
Usage
TextEmbeddingModel$get_model_description()
Returns
list with the description of the model in English
and the native language.
Method get_model_info()
Method for requesting the model information
Usage
TextEmbeddingModel$get_model_info()
Returns
list of all relevant model information
Method get_package_versions()
Method for requesting a summary of the R and python packages'
versions used for creating the model.
Usage
TextEmbeddingModel$get_package_versions()
Returns
Returns a list containing the versions of the relevant
R and python packages.
Method get_basic_components()
Method for requesting the part of interface's configuration that is
necessary for all models.
Usage
TextEmbeddingModel$get_basic_components()
Returns
Returns a list.
Method get_transformer_components()
Method for requesting the part of interface's configuration that is
necessary for transformer models.
Usage
TextEmbeddingModel$get_transformer_components()
Returns
Returns a list.
Method get_sustainability_data()
Method for requesting a log of tracked energy consumption
during training and an estimate of the resulting CO2 equivalents in kg.
Usage
TextEmbeddingModel$get_sustainability_data()
Returns
Returns a matrix containing the tracked energy consumption,
CO2 equivalents in kg, information on the tracker used, and technical
information on the training infrastructure for every training run.
Method get_ml_framework()
Method for requesting the machine learning framework used
for the classifier.
Usage
TextEmbeddingModel$get_ml_framework()
Returns
Returns a string describing the machine learning framework used
for the classifier.
Method count_parameter()
Method for counting the trainable parameters of a model.
Usage
TextEmbeddingModel$count_parameter(with_head = FALSE)
Arguments
with_headbool If TRUE the number of parameters is returned including
the language modeling head of the model. If FALSE only the number of parameters of
the core model is returned.
Returns
Returns the number of trainable parameters of the model.
Method is_configured()
Method for checking if the model was successfully configured.
An object can only be used if this value is TRUE.
Usage
TextEmbeddingModel$is_configured()
Returns
bool TRUE if the model is fully configured. FALSE if not.
Method get_private()
Method for requesting all private fields and methods. Used
for loading and updating an object.
Usage
TextEmbeddingModel$get_private()
Returns
Returns a list with all private fields and methods.
Method get_all_fields()
Return all fields.
Usage
TextEmbeddingModel$get_all_fields()
Returns
Method returns a list containing all public and private fields
of the object.
Method clone()
The objects of this class are cloneable with this method.
Usage
TextEmbeddingModel$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other Text Embedding:
TEFeatureExtractor
aifeducation documentation built on April 4, 2025, 2:01 a.m.
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| TextEmbeddingModel | R Documentation |
Text embedding model
Description
This R6 class stores a text embedding model which can be used to tokenize, encode, decode, and embed
raw texts. The object provides a unique interface for different text processing methods.
Value
Objects of class TextEmbeddingModel transform raw texts into numerical representations which can be used for downstream tasks. For this aim objects of this class allow to tokenize raw texts, to encode tokens to sequences of integers, and to decode sequences of integers back to tokens.
Public fields
last_training('list()')
List for storing the history and the results of the last training. This information will be overwritten if a new training is started.tokenizer_statistics('matrix()')
Matrix containing the tokenizer statistics for the creation of the tokenizer and all training runs according to Kaya & Tantuğ (2024).Kaya, Y. B., & Tantuğ, A. C. (2024). Effect of tokenization granularity for Turkish large language models. Intelligent Systems with Applications, 21, 200335. https://doi.org/10.1016/j.iswa.2024.200335
Methods
Public methods
Method configure()
Method for creating a new text embedding model
Usage
TextEmbeddingModel$configure( model_name = NULL, model_label = NULL, model_language = NULL, method = NULL, ml_framework = "pytorch", max_length = 0, chunks = 2, overlap = 0, emb_layer_min = "middle", emb_layer_max = "2_3_layer", emb_pool_type = "average", model_dir = NULL, trace = FALSE )
Arguments
model_namestringcontaining the name of the new model.model_labelstringcontaining the label/title of the new model.model_languagestringcontaining the language which the model represents (e.g., English).methodstringdetermining the kind of embedding model. Currently the following models are supported:method="bert"for Bidirectional Encoder Representations from Transformers (BERT),method="roberta"for A Robustly Optimized BERT Pretraining Approach (RoBERTa),method="longformer"for Long-Document Transformer,method="funnel"for Funnel-Transformer,method="deberta_v2"for Decoding-enhanced BERT with Disentangled Attention (DeBERTa V2),method="glove"`` for GlobalVector Clusters, and method="lda"' for topic modeling. See details for more information.ml_frameworkstringFramework to use for the model.ml_framework="tensorflow"for 'tensorflow' andml_framework="pytorch"for 'pytorch'. Only relevant for transformer models. To request bag-of-words model setml_framework=NULL.max_lengthintdetermining the maximum length of token sequences used in transformer models. Not relevant for the other methods.chunksintMaximum number of chunks. Must be at least 2.overlapintdetermining the number of tokens which should be added at the beginning of the next chunk. Only relevant for transformer models.emb_layer_minintorstringdetermining the first layer to be included in the creation of embeddings. An integer correspondents to the layer number. The first layer has the number 1. Instead of an integer the following strings are possible:"start"for the first layer,"middle"for the middle layer,"2_3_layer"for the layer two-third layer, and"last"for the last layer.emb_layer_maxintorstringdetermining the last layer to be included in the creation of embeddings. An integer correspondents to the layer number. The first layer has the number 1. Instead of an integer the following strings are possible:"start"for the first layer,"middle"for the middle layer,"2_3_layer"for the layer two-third layer, and"last"for the last layer.emb_pool_typestringdetermining the method for pooling the token embeddings within each layer. If"cls"only the embedding of the CLS token is used. If"average"the token embedding of all tokens are averaged (excluding padding tokens)."clsis not supported formethod="funnel".model_dirstringpath to the directory where the BERT model is stored.traceboolTRUEprints information about the progress.FALSEdoes not.
Details
In the case of any transformer (e.g.method="bert",
method="roberta", and method="longformer"),
a pretrained transformer model must be supplied via model_dir.
Returns
Returns an object of class TextEmbeddingModel.
Method load_from_disk()
loads an object from disk and updates the object to the current version of the package.
Usage
TextEmbeddingModel$load_from_disk(dir_path)
Arguments
dir_pathPath where the object set is stored.
Returns
Method does not return anything. It loads an object from disk.
Method load()
Method for loading a transformers model into R.
Usage
TextEmbeddingModel$load(dir_path)
Arguments
dir_pathstringcontaining the path to the relevant model directory.
Returns
Function does not return a value. It is used for loading a saved transformer model into the R interface.
Method save()
Method for saving a transformer model on disk.Relevant only for transformer models.
Usage
TextEmbeddingModel$save(dir_path, folder_name)
Arguments
dir_pathstringcontaining the path to the relevant model directory.folder_namestringName for the folder created within the directory. This folder contains all model files.
Returns
Function does not return a value. It is used for saving a transformer model to disk.
Method encode()
Method for encoding words of raw texts into integers.
Usage
TextEmbeddingModel$encode( raw_text, token_encodings_only = FALSE, to_int = TRUE, trace = FALSE )
Arguments
raw_textvectorcontaining the raw texts.token_encodings_onlyboolIfTRUE, only the token encodings are returned. IfFALSE, the complete encoding is returned which is important for some transformer models.to_intboolIfTRUEthe integer ids of the tokens are returned. IfFALSEthe tokens are returned. Argument only applies for transformer models and iftoken_encodings_only=TRUE.traceboolIfTRUE, information of the progress is printed.FALSEif not requested.
Returns
list containing the integer or token sequences of the raw texts with
special tokens.
Method decode()
Method for decoding a sequence of integers into tokens
Usage
TextEmbeddingModel$decode(int_seqence, to_token = FALSE)
Arguments
int_seqencelistcontaining the integer sequences which should be transformed to tokens or plain text.to_tokenboolIfFALSEplain text is returned. IfTRUEa sequence of tokens is returned. Argument only relevant if the model is based on a transformer.
Returns
list of token sequences
Method get_special_tokens()
Method for receiving the special tokens of the model
Usage
TextEmbeddingModel$get_special_tokens()
Returns
Returns a matrix containing the special tokens in the rows
and their type, token, and id in the columns.
Method embed()
Method for creating text embeddings from raw texts.
This method should only be used if a small number of texts should be transformed
into text embeddings. For a large number of texts please use the method embed_large.
In the case of using a GPU and running out of memory while using 'tensorflow' reduce the
batch size or restart R and switch to use cpu only via set_config_cpu_only. In general,
not relevant for 'pytorch'.
Usage
TextEmbeddingModel$embed( raw_text = NULL, doc_id = NULL, batch_size = 8, trace = FALSE, return_large_dataset = FALSE )
Arguments
raw_textvectorcontaining the raw texts.doc_idvectorcontaining the corresponding IDs for every text.batch_sizeintdetermining the maximal size of every batch.traceboolTRUE, if information about the progression should be printed on console.return_large_dataset'bool' If
TRUEthe retuned object is of class LargeDataSetForTextEmbeddings. IfFALSEit is of class EmbeddedText
Returns
Method returns an object of class EmbeddedText or LargeDataSetForTextEmbeddings. This object contains the embeddings as a data.frame and information about the model creating the embeddings.
Method embed_large()
Method for creating text embeddings from raw texts.
Usage
TextEmbeddingModel$embed_large( large_datas_set, batch_size = 32, trace = FALSE, log_file = NULL, log_write_interval = 2 )
Arguments
large_datas_setObject of class LargeDataSetForText containing the raw texts.
batch_sizeintdetermining the maximal size of every batch.traceboolTRUE, if information about the progression should be printed on console.log_filestringPath to the file where the log should be saved. If no logging is desired set this argument toNULL.log_write_intervalintTime in seconds determining the interval in which the logger should try to update the log files. Only relevant iflog_fileis notNULL.
Returns
Method returns an object of class LargeDataSetForTextEmbeddings.
Method fill_mask()
Method for calculating tokens behind mask tokens.
Usage
TextEmbeddingModel$fill_mask(text, n_solutions = 5)
Arguments
textstringText containing mask tokens.n_solutionsintNumber estimated tokens for every mask.
Returns
Returns a list containing a data.frame for every
mask. The data.frame contains the solutions in the rows and reports
the score, token id, and token string in the columns.
Method set_publication_info()
Method for setting the bibliographic information of the model.
Usage
TextEmbeddingModel$set_publication_info(type, authors, citation, url = NULL)
Arguments
typestringType of information which should be changed/added.developer, andmodifierare possible.authorsList of people.
citationstringCitation in free text.urlstringCorresponding URL if applicable.
Returns
Function does not return a value. It is used to set the private members for publication information of the model.
Method get_publication_info()
Method for getting the bibliographic information of the model.
Usage
TextEmbeddingModel$get_publication_info()
Returns
list of bibliographic information.
Method set_model_license()
Method for setting the license of the model
Usage
TextEmbeddingModel$set_model_license(license = "CC BY")
Arguments
licensestringcontaining the abbreviation of the license or the license text.
Returns
Function does not return a value. It is used for setting the private member for the software license of the model.
Method get_model_license()
Method for requesting the license of the model
Usage
TextEmbeddingModel$get_model_license()
Returns
string License of the model
Method set_documentation_license()
Method for setting the license of models' documentation.
Usage
TextEmbeddingModel$set_documentation_license(license = "CC BY")
Arguments
licensestringcontaining the abbreviation of the license or the license text.
Returns
Function does not return a value. It is used to set the private member for the documentation license of the model.
Method get_documentation_license()
Method for getting the license of the models' documentation.
Usage
TextEmbeddingModel$get_documentation_license()
Arguments
licensestringcontaining the abbreviation of the license or the license text.
Method set_model_description()
Method for setting a description of the model
Usage
TextEmbeddingModel$set_model_description( eng = NULL, native = NULL, abstract_eng = NULL, abstract_native = NULL, keywords_eng = NULL, keywords_native = NULL )
Arguments
engstringA text describing the training of the classifier, its theoretical and empirical background, and the different output labels in English.nativestringA text describing the training of the classifier, its theoretical and empirical background, and the different output labels in the native language of the model.abstract_engstringA text providing a summary of the description in English.abstract_nativestringA text providing a summary of the description in the native language of the classifier.keywords_engvectorof keywords in English.keywords_nativevectorof keywords in the native language of the classifier.
Returns
Function does not return a value. It is used to set the private members for the description of the model.
Method get_model_description()
Method for requesting the model description.
Usage
TextEmbeddingModel$get_model_description()
Returns
list with the description of the model in English
and the native language.
Method get_model_info()
Method for requesting the model information
Usage
TextEmbeddingModel$get_model_info()
Returns
list of all relevant model information
Method get_package_versions()
Method for requesting a summary of the R and python packages' versions used for creating the model.
Usage
TextEmbeddingModel$get_package_versions()
Returns
Returns a list containing the versions of the relevant
R and python packages.
Method get_basic_components()
Method for requesting the part of interface's configuration that is necessary for all models.
Usage
TextEmbeddingModel$get_basic_components()
Returns
Returns a list.
Method get_transformer_components()
Method for requesting the part of interface's configuration that is necessary for transformer models.
Usage
TextEmbeddingModel$get_transformer_components()
Returns
Returns a list.
Method get_sustainability_data()
Method for requesting a log of tracked energy consumption during training and an estimate of the resulting CO2 equivalents in kg.
Usage
TextEmbeddingModel$get_sustainability_data()
Returns
Returns a matrix containing the tracked energy consumption,
CO2 equivalents in kg, information on the tracker used, and technical
information on the training infrastructure for every training run.
Method get_ml_framework()
Method for requesting the machine learning framework used for the classifier.
Usage
TextEmbeddingModel$get_ml_framework()
Returns
Returns a string describing the machine learning framework used
for the classifier.
Method count_parameter()
Method for counting the trainable parameters of a model.
Usage
TextEmbeddingModel$count_parameter(with_head = FALSE)
Arguments
with_headboolIfTRUEthe number of parameters is returned including the language modeling head of the model. IfFALSEonly the number of parameters of the core model is returned.
Returns
Returns the number of trainable parameters of the model.
Method is_configured()
Method for checking if the model was successfully configured.
An object can only be used if this value is TRUE.
Usage
TextEmbeddingModel$is_configured()
Returns
bool TRUE if the model is fully configured. FALSE if not.
Method get_private()
Method for requesting all private fields and methods. Used for loading and updating an object.
Usage
TextEmbeddingModel$get_private()
Returns
Returns a list with all private fields and methods.
Method get_all_fields()
Return all fields.
Usage
TextEmbeddingModel$get_all_fields()
Returns
Method returns a list containing all public and private fields
of the object.
Method clone()
The objects of this class are cloneable with this method.
Usage
TextEmbeddingModel$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
Other Text Embedding:
TEFeatureExtractor
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