TextEmbeddingModel {aifeducation} | 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.
Methods
Public methods
Method new()
Method for creating a new text embedding model
Usage
TextEmbeddingModel$new( model_name = NULL, model_label = NULL, model_version = NULL, model_language = NULL, method = NULL, ml_framework = aifeducation_config$get_framework()$TextEmbeddingFramework, max_length = 0, chunks = 1, overlap = 0, emb_layer_min = "middle", emb_layer_max = "2_3_layer", emb_pool_type = "average", model_dir, bow_basic_text_rep, bow_n_dim = 10, bow_n_cluster = 100, bow_max_iter = 500, bow_max_iter_cluster = 500, bow_cr_criterion = 1e-08, bow_learning_rate = 1e-08, trace = FALSE )
Arguments
model_name
string
containing the name of the new model.model_label
string
containing the label/title of the new model.model_version
string
version of the model.model_language
string
containing the language which the model represents (e.g., English).method
string
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, andmethod="lda"
for topic modeling. See details for more information.ml_framework
string
Framework to use for the model.ml_framework="tensorflow"
for 'tensorflow' andml_framework="pytorch"
for 'pytorch'. Only relevant for transformer models.max_length
int
determining the maximum length of token sequences used in transformer models. Not relevant for the other methods.chunks
int
Maximum number of chunks. Only relevant for transformer models.overlap
int
determining the number of tokens which should be added at the beginning of the next chunk. Only relevant for BERT models.emb_layer_min
int
orstring
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_max
int
orstring
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_type
string
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).model_dir
string
path to the directory where the BERT model is stored.bow_basic_text_rep
object of class
basic_text_rep
created via the function bow_pp_create_basic_text_rep. Only relevant formethod="glove_cluster"
andmethod="lda"
.bow_n_dim
int
Number of dimensions of the GlobalVector or number of topics for LDA.bow_n_cluster
int
Number of clusters created on the basis of GlobalVectors. Parameter is not relevant formethod="lda"
andmethod="bert"
bow_max_iter
int
Maximum number of iterations for fitting GlobalVectors and Topic Models.bow_max_iter_cluster
int
Maximum number of iterations for fitting cluster ifmethod="glove"
.bow_cr_criterion
double
convergence criterion for GlobalVectors.bow_learning_rate
double
initial learning rate for GlobalVectors.trace
bool
TRUE
prints information about the progress.FALSE
does not.
Details
method: In the case of
method="bert"
,method="roberta"
, andmethod="longformer"
, a pretrained transformer model must be supplied viamodel_dir
. Formethod="glove"
andmethod="lda"
a new model will be created based on the data provided viabow_basic_text_rep
. The original algorithm for GlobalVectors provides only word embeddings, not text embeddings. To achieve text embeddings the words are clustered based on their word embeddings with kmeans.
Returns
Returns an object of class TextEmbeddingModel.
Method load_model()
Method for loading a transformers model into R.
Usage
TextEmbeddingModel$load_model(model_dir, ml_framework = "auto")
Arguments
model_dir
string
containing the path to the relevant model directory.ml_framework
string
Determines the machine learning framework for using the model. Possible areml_framework="pytorch"
for 'pytorch',ml_framework="tensorflow"
for 'tensorflow', andml_framework="auto"
.
Returns
Function does not return a value. It is used for loading a saved transformer model into the R interface.
Method save_model()
Method for saving a transformer model on disk.Relevant only for transformer models.
Usage
TextEmbeddingModel$save_model(model_dir, save_format = "default")
Arguments
model_dir
string
containing the path to the relevant model directory.save_format
Format for saving the model. For 'tensorflow'/'keras' models
"h5"
for HDF5. For 'pytorch' models"safetensors"
for 'safetensors' or"pt"
for 'pytorch' via pickle. Use"default"
for the standard format. This is h5 for 'tensorflow'/'keras' models and safetensors for 'pytorch' models.
Returns
Function does not return a value. It is used for saving a transformer model to disk.
Method encode()
Method for encoding words of raw texts into integers.
Usage
TextEmbeddingModel$encode( raw_text, token_encodings_only = FALSE, to_int = TRUE, trace = FALSE )
Arguments
raw_text
vector
containing the raw texts.token_encodings_only
bool
IfTRUE
, only the token encodings are returned. IfFALSE
, the complete encoding is returned which is important for BERT models.to_int
bool
IfTRUE
the integer ids of the tokens are returned. IfFALSE
the tokens are returned. Argument only applies for transformer models and iftoken_encodings_only==TRUE
.trace
bool
IfTRUE
, information of the progress is printed.FALSE
if not requested.
Returns
list
containing the integer sequences of the raw texts with
special tokens.
Method decode()
Method for decoding a sequence of integers into tokens
Usage
TextEmbeddingModel$decode(int_seqence, to_token = FALSE)
Arguments
int_seqence
list
containing the integer sequences which should be transformed to tokens or plain text.to_token
bool
IfFALSE
a plain text is returned. ifTRUE
a sequence of tokens is returned. Argument only relevant if the model is based on a transformer.
Returns
list
of token sequences
Method get_special_tokens()
Method for receiving the special tokens of the model
Usage
TextEmbeddingModel$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
In the case of using a GPU and running out of memory reduce the batch size or restart R and switch to use cpu only via set_config_cpu_only.
Usage
TextEmbeddingModel$embed( raw_text = NULL, doc_id = NULL, batch_size = 8, trace = FALSE )
Arguments
raw_text
vector
containing the raw texts.doc_id
vector
containing the corresponding IDs for every text.batch_size
int
determining the maximal size of every batch.trace
bool
TRUE
, if information about the progression should be printed on console.
Returns
Method returns a R6 object of class EmbeddedText. This object
contains the embeddings as a data.frame
and information about the
model creating the embeddings.
Method fill_mask()
Method for calculating tokens behind mask tokens.
Usage
TextEmbeddingModel$fill_mask(text, n_solutions = 5)
Arguments
text
string
Text containing mask tokens.n_solutions
int
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$set_publication_info(type, authors, citation, url = NULL)
Arguments
type
string
Type of information which should be changed/added.type="developer"
, andtype="modifier"
are possible.authors
List of people.
citation
string
Citation in free text.url
string
Corresponding URL if applicable.
Returns
Function does not return a value. It is used to set the private members for publication information of the model.
Method get_publication_info()
Method for getting the bibliographic information of the model.
Usage
TextEmbeddingModel$get_publication_info()
Returns
list
of bibliographic information.
Method set_software_license()
Method for setting the license of the model
Usage
TextEmbeddingModel$set_software_license(license = "GPL-3")
Arguments
license
string
containing the abbreviation of the license or the license text.
Returns
Function does not return a value. It is used for setting the private member for the software license of the model.
Method get_software_license()
Method for requesting the license of the model
Usage
TextEmbeddingModel$get_software_license()
Returns
string
License of the model
Method set_documentation_license()
Method for setting the license of models' documentation.
Usage
TextEmbeddingModel$set_documentation_license(license = "CC BY-SA")
Arguments
license
string
containing the abbreviation of the license or the license text.
Returns
Function does not return a value. It is used to set the private member for the documentation license of the model.
Method get_documentation_license()
Method for getting the license of the models' documentation.
Usage
TextEmbeddingModel$get_documentation_license()
Arguments
license
string
containing the abbreviation of the license or the license text.
Method set_model_description()
Method for setting a description of the model
Usage
TextEmbeddingModel$set_model_description( eng = NULL, native = NULL, abstract_eng = NULL, abstract_native = NULL, keywords_eng = NULL, keywords_native = NULL )
Arguments
eng
string
A text describing the training of the classifier, its theoretical and empirical background, and the different output labels in English.native
string
A text describing the training of the classifier, its theoretical and empirical background, and the different output labels in the native language of the model.abstract_eng
string
A text providing a summary of the description in English.abstract_native
string
A text providing a summary of the description in the native language of the classifier.keywords_eng
vector
of keywords in English.keywords_native
vector
of keywords in the native language of the classifier.
Returns
Function does not return a value. It is used to set the private members for the description of the model.
Method get_model_description()
Method for requesting the model description.
Usage
TextEmbeddingModel$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$get_model_info()
Returns
list
of all relevant model information
Method get_package_versions()
Method for requesting a summary of the R and python packages' versions used for creating the classifier.
Usage
TextEmbeddingModel$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$get_basic_components()
Returns
Returns a list
.
Method get_bow_components()
Method for requesting the part of interface's configuration that is necessary bag-of-words models.
Usage
TextEmbeddingModel$get_bow_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$get_ml_framework()
Returns
Returns a string
describing the machine learning framework used
for the classifier
Method clone()
The objects of this class are cloneable with this method.
Usage
TextEmbeddingModel$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
See Also
Other Text Embedding:
EmbeddedText
,
combine_embeddings()