RBERT (version 0.1.11)

extract_features: Extract output features from BERT

Description

Given example sentences (as a list of InputExample_EFs), apply an existing BERT model and capture certain output layers. (These could potentially be used as features in downstream tasks.)

Usage

extract_features(
  examples,
  model = c("bert_base_uncased", "bert_base_cased", "bert_large_uncased",
    "bert_large_cased", "bert_large_uncased_wwm", "bert_large_cased_wwm",
    "bert_base_multilingual_cased", "bert_base_chinese", "scibert_scivocab_uncased",
    "scibert_scivocab_cased", "scibert_basevocab_uncased", "scibert_basevocab_cased"),
  ckpt_dir = NULL,
  vocab_file = find_vocab(ckpt_dir),
  bert_config_file = find_config(ckpt_dir),
  init_checkpoint = find_ckpt(ckpt_dir),
  output_file = NULL,
  max_seq_length = 128L,
  layer_indexes = -4:-1,
  batch_size = 2L,
  features = c("output", "attention"),
  verbose = FALSE
)

Arguments

examples

List of InputExample_EF objects, or character vector(s) that can be converted to InputExample_EF objects.

model

Character; which model checkpoint to use. If specified, ckpt_dir, codevocab_file, bert_config_file, and init_checkpoint will be inferred. If you do not have this checkpoint, you will be prompted to download it in interactive mode.

ckpt_dir

Character; path to checkpoint directory. If specified, any other checkpoint files required by this function (vocab_file, bert_config_file, or init_checkpoint) will default to standard filenames within ckpt_dir.

vocab_file

path to vocabulary file. File is assumed to be a text file, with one token per line, with the line number corresponding to the index of that token in the vocabulary.

bert_config_file

Character; the path to a json config file.

init_checkpoint

Character; path to the checkpoint directory, plus checkpoint name stub (e.g. "bert_model.ckpt"). Path must be absolute and explicit, starting with "/".

output_file

(optional) Character; file path (stub) for writing output to.

max_seq_length

Integer; the maximum number of tokens that will be considered together.

layer_indexes

Integer vector; indexes (positive, or negative counting back from the end) indicating which layers to extract as "output features". The "zeroth" layer embeddings are the input embeddings vectors to the first layer.

batch_size

Integer; how many examples to process per batch.

features

Character; whether to return "output" (layer outputs, the default), "attention" (attention probabilities), or both.

verbose

Logical; if FALSE, suppresses most of the TensorFlow chatter by temporarily setting the logging threshold to its highest level. If TRUE, keeps the current logging threshold, which defaults to "WARN". To change the logging threshold of the current session, run tensorflow::tf$logging$set_verbosity(tensorflow::tf$logging$DEBUG) (setting whatever verbosity level you want).

Value

A list with elements "output" (the layer outputs as a tibble) and/or "attention" (the attention weights as a tibble).

Examples

# NOT RUN {
BERT_PRETRAINED_DIR <- download_BERT_checkpoint("bert_base_uncased")
examples <- c("I saw the branch on the bank.",
              "I saw the branch of the bank.")

# Just specify checkpoint directory.
feats <- extract_features(
  examples = examples,
  ckpt_dir = BERT_PRETRAINED_DIR
)
# Can also just specify the model, if you have it downloaded.
# In interactive mode, you'll be prompted to download the model if you do not
# have it.
feats <- extract_features(
  examples = examples,
  model = "bert_base_uncased"
)
# }