R: Function for creating a new transformer based on Funnel...

create_funnel_model {aifeducation}

R Documentation

Function for creating a new transformer based on Funnel Transformer

Description

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

Usage

create_funnel_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,
  target_hidden_size = 64,
  block_sizes = c(4, 4, 4),
  num_attention_heads = 12,
  intermediate_size = 3072,
  num_decoder_layers = 2,
  pooling_type = "mean",
  hidden_act = "gelu",
  hidden_dropout_prob = 0.1,
  attention_probs_dropout_prob = 0.1,
  activation_dropout = 0,
  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` Initial number of neurons in each layer.
`target_hidden_size`	`int` Number of neurons in the final layer. This parameter determines the dimensionality of the resulting text embedding.
`block_sizes`	`vector` of `int` determining the number and sizes of each block.
`num_attention_heads`	`int` Number of attention heads.
`intermediate_size`	`int` Number of neurons in the intermediate layer of the attention mechanism.
`num_decoder_layers`	`int` Number of decoding layers.
`pooling_type`	`string` `"mean"` for pooling with mean and `"max"` for pooling with maximum values.
`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.
`activation_dropout`	`float` Dropout probability between the layers of the feed-forward blocks.
`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

The model uses a configuration with truncate_seq=TRUE to avoid implementation problems with tensorflow.

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

Model is created with separete_cls=TRUE,truncate_seq=TRUE, and pool_q_only=TRUE.

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

Dai, Z., Lai, G., Yang, Y. & Le, Q. V. (2020). Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. doi:10.48550/arXiv.2006.03236

Hugging Face documentation https://huggingface.co/docs/transformers/model_doc/funnel#funnel-transformer