R: Function for creating a new transformer based on BERT

create_bert_model {aifeducation}

R Documentation

Function for creating a new transformer based on BERT

Description

This function creates a transformer configuration based on the BERT base architecture and a vocabulary based on WordPiece by using the python libraries 'transformers' and 'tokenizers'.

Usage

create_bert_model(
  ml_framework = aifeducation_config$get_framework(),
  model_dir,
  vocab_raw_texts = NULL,
  vocab_size = 30522,
  vocab_do_lower_case = FALSE,
  max_position_embeddings = 512,
  hidden_size = 768,
  num_hidden_layer = 12,
  num_attention_heads = 12,
  intermediate_size = 3072,
  hidden_act = "gelu",
  hidden_dropout_prob = 0.1,
  attention_probs_dropout_prob = 0.1,
  sustain_track = TRUE,
  sustain_iso_code = NULL,
  sustain_region = NULL,
  sustain_interval = 15,
  trace = TRUE,
  pytorch_safetensors = TRUE
)

Arguments

`ml_framework`	`string` Framework to use for training and inference. `ml_framework="tensorflow"` for 'tensorflow' and `ml_framework="pytorch"` for 'pytorch'.
`model_dir`	`string` Path to the directory where the model should be saved.
`vocab_raw_texts`	`vector` containing the raw texts for creating the vocabulary.
`vocab_size`	`int` Size of the vocabulary.
`vocab_do_lower_case`	`bool` `TRUE` if all words/tokens should be lower case.
`max_position_embeddings`	`int` Number of maximal position embeddings. This parameter also determines the maximum length of a sequence which can be processed with the model.
`hidden_size`	`int` Number of neurons in each layer. This parameter determines the dimensionality of the resulting text embedding.
`num_hidden_layer`	`int` Number of hidden layers.
`num_attention_heads`	`int` Number of attention heads.
`intermediate_size`	`int` Number of neurons in the intermediate layer of the attention mechanism.
`hidden_act`	`string` name of the activation function.
`hidden_dropout_prob`	`double` Ratio of dropout.
`attention_probs_dropout_prob`	`double` Ratio of dropout for attention probabilities.
`sustain_track`	`bool` If `TRUE` energy consumption is tracked during training via the python library codecarbon.
`sustain_iso_code`	`string` ISO code (Alpha-3-Code) for the country. This variable must be set if sustainability should be tracked. A list can be found on Wikipedia: https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes.
`sustain_region`	Region within a country. Only available for USA and Canada See the documentation of codecarbon for more information. https://mlco2.github.io/codecarbon/parameters.html
`sustain_interval`	`integer` Interval in seconds for measuring power usage.
`trace`	`bool` `TRUE` if information about the progress should be printed to the console.
`pytorch_safetensors`	`bool` If `TRUE` a 'pytorch' model is saved in safetensors format. If `FALSE` or 'safetensors' not available it is saved in the standard pytorch format (.bin). Only relevant for pytorch models.

Value

This function does not return an object. Instead the configuration and the vocabulary of the new model are saved on disk.

Note

To train the model, pass the directory of the model to the function train_tune_bert_model.

This models uses a WordPiece Tokenizer like BERT and can be trained with whole word masking. Transformer library may show a warning which can be ignored.

References

Devlin, J., Chang, M.‑W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North (pp. 4171–4186). Association for Computational Linguistics. doi:10.18653/v1/N19-1423

Hugging Face documentation https://huggingface.co/docs/transformers/model_doc/bert#transformers.TFBertForMaskedLM