textEmbed: Extract layers and aggregate them to word embeddings, for all...

In text: Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning

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

Description

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

Usage

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 = "cpu",
  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', 'gpu:k' or 'mps'/'mps:k' for MacOS, where k is a specific device number such as 'mps:1'.
logging_level	Set the logging level. Default: "warning". Options (ordered from less logging to more logging): critical, error, warning, info, debug
...	settings from textEmbedRawLayers().

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 Also

see textEmbedLayerAggregation, textEmbedRawLayers and textDimName

Examples

# 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)

text documentation built on Aug. 9, 2023, 5:08 p.m.