Extract layers and aggregate them to word embeddings, for all character variables in a given dataframe.

textEmbed(
texts,
model = "bert-base-uncased",
layers = -2,
dim_name = TRUE,
aggregation_from_layers_to_tokens = "concatenate",
aggregation_from_tokens_to_texts = "mean",
aggregation_from_tokens_to_word_types = NULL,
keep_token_embeddings = TRUE,
tokens_select = NULL,
tokens_deselect = NULL,
decontextualize = FALSE,
model_max_length = NULL,
max_token_to_sentence = 4,
tokenizer_parallelism = FALSE,
device = "gpu",
logging_level = "error"
)

## Arguments

texts

A character variable or a tibble/dataframe with at least one character variable.

model

Character string specifying pre-trained language model (default 'bert-base-uncased'). For full list of options see pretrained models at HuggingFace. For example use "bert-base-multilingual-cased", "openai-gpt", "gpt2", "ctrl", "transfo-xl-wt103", "xlnet-base-cased", "xlm-mlm-enfr-1024", "distilbert-base-cased", "roberta-base", or "xlm-roberta-base". Only load models that you trust from HuggingFace; loading a malicious model can execute arbitrary code on your computer).

layers

(string or numeric) Specify the layers that should be extracted (default -2 which give the second to last layer). It is more efficient to only extract the layers that you need (e.g., 11). You can also extract several (e.g., 11:12), or all by setting this parameter to "all". Layer 0 is the decontextualized input layer (i.e., not comprising hidden states) and thus should normally not be used. These layers can then be aggregated in the textEmbedLayerAggregation function.

dim_name

Boolean, if TRUE append the variable name after all variable-names in the output. (This differentiates between word embedding dimension names; e.g., Dim1_text_variable_name). see textDimName to change names back and forth.

aggregation_from_layers_to_tokens

(string) Aggregated layers of each token. Method to aggregate the contextualized layers (e.g., "mean", "min" or "max, which takes the minimum, maximum or mean, respectively, across each column; or "concatenate", which links together each word embedding layer to one long row.

aggregation_from_tokens_to_texts

(string) Aggregates to the individual text (i.e., the aggregation of all tokens/words given to the transformer).

aggregation_from_tokens_to_word_types

(string) Aggregates to the word type (i.e., the individual words) rather than texts.

keep_token_embeddings

(boolean) Whether to also keep token embeddings when using texts or word types aggregation.

tokens_select

Option to select word embeddings linked to specific tokens such as [CLS] and [SEP] for the context embeddings.

tokens_deselect

Option to deselect embeddings linked to specific tokens such as [CLS] and [SEP] for the context embeddings.

decontextualize

(boolean) Provide word embeddings of single words as input to the model (these embeddings are, e.g., used for plotting; default is to use ). If using this, then set single_context_embeddings to FALSE.

model_max_length

The maximum length (in number of tokens) for the inputs to the transformer model (default the value stored for the associated model).

max_token_to_sentence

(numeric) Maximum number of tokens in a string to handle before switching to embedding text sentence by sentence.

tokenizer_parallelism

(boolean) If TRUE this will turn on tokenizer parallelism. Default FALSE.

device

Name of device to use: 'cpu', 'gpu', or 'gpu:k' where k is a specific device number

logging_level

Set the logging level. Default: "warning". Options (ordered from less logging to more logging): critical, error, warning, info, debug

## Value

A tibble with tokens, a column for layer identifier and word embeddings. Note that layer 0 is the input embedding to the transformer

see textEmbedLayerAggregation, textEmbedRawLayers and textDimName

## Examples

# \donttest{
# word_embeddings <- textEmbed(Language_based_assessment_data_8[1:2, 1:2],
#                             layers = 10:11,
#                             aggregation_from_layers_to_tokens = "concatenate",
#                             aggregation_from_tokens_to_texts = "mean",
#                             aggregation_from_tokens_to_word_types = "mean")
## Show information about how the embeddings were constructed
# comment(word_embeddings$texts$satisfactiontexts)
# comment(word_embeddings$word_types) # comment(word_embeddings$tokens\$satisfactiontexts)
# }