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aifeducation (version 1.0.2)

.AIFEBaseTransformer: Base R6 class for creation and definition of .AIFE*Transformer-like classes

Description

This base class is used to create and define .AIFE*Transformer-like classes. It serves as a skeleton for a future concrete transformer and cannot be used to create an object of itself (an attempt to call new-method will produce an error).

See p.1 Base Transformer Class in Transformers for Developers for details.

Arguments

Create

The create-method is a basic algorithm that is used to create a new transformer, but cannot be called directly.

Train

The train-method is a basic algorithm that is used to train and tune the transformer but cannot be called directly.

Concrete transformer implementation

There are already implemented concrete (child) transformers (e.g. BERT, DeBERTa-V2, etc.), to implement a new one see p.4 Implement A Custom Transformer in Transformers for Developers

Public fields

params

A list containing transformer's parameters ('static', 'dynamic' and 'dependent' parameters)

list() containing all the transformer parameters. Can be set with set_model_param().

'Static' parameters

Regardless of the transformer, the following parameters are always included:

  • ml_framework

  • text_dataset

  • sustain_track

  • sustain_iso_code

  • sustain_region

  • sustain_interval

  • trace

  • pytorch_safetensors

  • log_dir

  • log_write_interval

'Dynamic' parameters

In the case of create it also contains (see create-method for details):

  • model_dir

  • vocab_size

  • max_position_embeddings

  • hidden_size

  • hidden_act

  • hidden_dropout_prob

  • attention_probs_dropout_prob

  • intermediate_size

  • num_attention_heads

In the case of train it also contains (see train-method for details):

  • output_dir

  • model_dir_path

  • p_mask

  • whole_word

  • val_size

  • n_epoch

  • batch_size

  • chunk_size

  • min_seq_len

  • full_sequences_only

  • learning_rate

  • n_workers

  • multi_process

  • keras_trace

  • pytorch_trace

'Dependent' parameters

Depending on the transformer and the method used class may contain different parameters:

  • vocab_do_lower_case

  • num_hidden_layer

  • add_prefix_space

  • etc.

temp

A list containing temporary transformer's parameters

list() containing all the temporary local variables that need to be accessed between the step functions. Can be set with set_model_temp().

For example, it can be a variable tok_new that stores the tokenizer from steps_for_creation$create_tokenizer_draft. To train the tokenizer, access the variable tok_new in steps_for_creation$calculate_vocab through the temp list of this class.

Methods

Method new()

An object of this class cannot be created. Thus, method's call will produce an error.

Usage

.AIFEBaseTransformer$new()

Returns

This method returns an error.

Method set_title()

Setter for the title. Sets a new value for the title private attribute.

Usage

.AIFEBaseTransformer$set_title(title)

Arguments

title

string A new title.

Returns

This method returns nothing.

Method set_model_param()

Setter for the parameters. Adds a new parameter and its value to the params list.

Usage

.AIFEBaseTransformer$set_model_param(param_name, param_value)

Arguments

param_name

string Parameter's name.

param_value

any Parameter's value.

Returns

This method returns nothing.

Method set_model_temp()

Setter for the temporary model's parameters. Adds a new temporary parameter and its value to the temp list.

Usage

.AIFEBaseTransformer$set_model_temp(temp_name, temp_value)

Arguments

temp_name

string Parameter's name.

temp_value

any Parameter's value.

Returns

This method returns nothing.

Method set_SFC_check_max_pos_emb()

Setter for the check_max_pos_emb element of the private steps_for_creation list. Sets a new fun function as the check_max_pos_emb step.

Usage

.AIFEBaseTransformer$set_SFC_check_max_pos_emb(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method set_SFC_create_tokenizer_draft()

Setter for the create_tokenizer_draft element of the private steps_for_creation list. Sets a new fun function as the create_tokenizer_draft step.

Usage

.AIFEBaseTransformer$set_SFC_create_tokenizer_draft(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method set_SFC_calculate_vocab()

Setter for the calculate_vocab element of the private steps_for_creation list. Sets a new fun function as the calculate_vocab step.

Usage

.AIFEBaseTransformer$set_SFC_calculate_vocab(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method set_SFC_save_tokenizer_draft()

Setter for the save_tokenizer_draft element of the private steps_for_creation list. Sets a new fun function as the save_tokenizer_draft step.

Usage

.AIFEBaseTransformer$set_SFC_save_tokenizer_draft(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method set_SFC_create_final_tokenizer()

Setter for the create_final_tokenizer element of the private steps_for_creation list. Sets a new fun function as the create_final_tokenizer step.

Usage

.AIFEBaseTransformer$set_SFC_create_final_tokenizer(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method set_SFC_create_transformer_model()

Setter for the create_transformer_model element of the private steps_for_creation list. Sets a new fun function as the create_transformer_model step.

Usage

.AIFEBaseTransformer$set_SFC_create_transformer_model(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method set_required_SFC()

Setter for all required elements of the private steps_for_creation list. Executes setters for all required creation steps.

Usage

.AIFEBaseTransformer$set_required_SFC(required_SFC)

Arguments

required_SFC

list() A list of all new required steps.

Returns

This method returns nothing.

Method set_SFT_load_existing_model()

Setter for the load_existing_model element of the private steps_for_training list. Sets a new fun function as the load_existing_model step.

Usage

.AIFEBaseTransformer$set_SFT_load_existing_model(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method set_SFT_cuda_empty_cache()

Setter for the cuda_empty_cache element of the private steps_for_training list. Sets a new fun function as the cuda_empty_cache step.

Usage

.AIFEBaseTransformer$set_SFT_cuda_empty_cache(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method set_SFT_create_data_collator()

Setter for the create_data_collator element of the private steps_for_training list. Sets a new fun function as the create_data_collator step. Use this method to make a custom data collator for a transformer.

Usage

.AIFEBaseTransformer$set_SFT_create_data_collator(fun)

Arguments

fun

function() A new function.

Returns

This method returns nothing.

Method create()

This method creates a transformer configuration based on the child-transformer architecture and a vocabulary using the python libraries transformers and tokenizers.

This method adds the following parameters to the temp list:

  • log_file

  • raw_text_dataset

  • pt_safe_save

  • value_top

  • total_top

  • message_top

This method uses the following parameters from the temp list:

  • log_file

  • raw_text_dataset

  • tokenizer

Usage

.AIFEBaseTransformer$create(
  ml_framework,
  model_dir,
  text_dataset,
  vocab_size,
  max_position_embeddings,
  hidden_size,
  num_attention_heads,
  intermediate_size,
  hidden_act,
  hidden_dropout_prob,
  attention_probs_dropout_prob,
  sustain_track,
  sustain_iso_code,
  sustain_region,
  sustain_interval,
  trace,
  pytorch_safetensors,
  log_dir,
  log_write_interval
)

Arguments

ml_framework

string Framework to use for training and inference.

  • ml_framework = "tensorflow": for 'tensorflow'.

  • ml_framework = "pytorch": for 'pytorch'.

model_dir

string Path to the directory where the model should be saved.

text_dataset

Object of class LargeDataSetForText.

vocab_size

int Size of the vocabulary.

max_position_embeddings

int Number of maximum 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_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

string 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 Only relevant for pytorch models.

  • TRUE: a 'pytorch' model is saved in safetensors format.

  • FALSE (or 'safetensors' is not available): model is saved in the standard pytorch format (.bin).

log_dir

Path to the directory where the log files should be saved.

log_write_interval

int Time in seconds determining the interval in which the logger should try to update the log files. Only relevant if log_dir is not NULL.

Returns

This method does not return an object. Instead, it saves the configuration and vocabulary of the new model to disk.

Method train()

This method can be used to train or fine-tune a transformer based on BERT architecture with the help of the python libraries transformers, datasets, and tokenizers.

This method adds the following parameters to the temp list:

  • log_file

  • loss_file

  • from_pt

  • from_tf

  • load_safe

  • raw_text_dataset

  • pt_safe_save

  • value_top

  • total_top

  • message_top

This method uses the following parameters from the temp list:

  • log_file

  • raw_text_dataset

  • tokenized_dataset

  • tokenizer

Usage

.AIFEBaseTransformer$train(
  ml_framework,
  output_dir,
  model_dir_path,
  text_dataset,
  p_mask,
  whole_word,
  val_size,
  n_epoch,
  batch_size,
  chunk_size,
  full_sequences_only,
  min_seq_len,
  learning_rate,
  n_workers,
  multi_process,
  sustain_track,
  sustain_iso_code,
  sustain_region,
  sustain_interval,
  trace,
  keras_trace,
  pytorch_trace,
  pytorch_safetensors,
  log_dir,
  log_write_interval
)

Arguments

ml_framework

string Framework to use for training and inference.

  • ml_framework = "tensorflow": for 'tensorflow'.

  • ml_framework = "pytorch": for 'pytorch'.

output_dir

string Path to the directory where the final model should be saved. If the directory does not exist, it will be created.

model_dir_path

string Path to the directory where the original model is stored.

text_dataset

Object of class LargeDataSetForText.

p_mask

double Ratio that determines the number of words/tokens used for masking.

whole_word

bool

  • TRUE: whole word masking should be applied.

  • FALSE: token masking is used.

val_size

double Ratio that determines the amount of token chunks used for validation.

n_epoch

int Number of epochs for training.

batch_size

int Size of batches.

chunk_size

int Size of every chunk for training.

full_sequences_only

bool TRUE for using only chunks with a sequence length equal to chunk_size.

min_seq_len

int Only relevant if full_sequences_only = FALSE. Value determines the minimal sequence length included in training process.

learning_rate

double Learning rate for adam optimizer.

n_workers

int Number of workers. Only relevant if ml_framework = "tensorflow".

multi_process

bool TRUE if multiple processes should be activated. Only relevant if ml_framework = "tensorflow".

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

string 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.

keras_trace

int

  • keras_trace = 0: does not print any information about the training process from keras on the console.

  • keras_trace = 1: prints a progress bar.

  • keras_trace = 2: prints one line of information for every epoch. Only relevant if ml_framework = "tensorflow".

pytorch_trace

int

  • pytorch_trace = 0: does not print any information about the training process from pytorch on the console.

  • pytorch_trace = 1: prints a progress bar.

pytorch_safetensors

bool Only relevant for pytorch models.

  • TRUE: a 'pytorch' model is saved in safetensors format.

  • FALSE (or 'safetensors' is not available): model is saved in the standard pytorch format (.bin).

log_dir

Path to the directory where the log files should be saved.

log_write_interval

int Time in seconds determining the interval in which the logger should try to update the log files. Only relevant if log_dir is not NULL.

Returns

This method does not return an object. Instead, it saves the configuration and vocabulary of the new model to disk.

Method clone()

The objects of this class are cloneable with this method.

Usage

.AIFEBaseTransformer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

References

Hugging Face transformers documantation:

See Also

Other Transformers for developers: .AIFEBertTransformer, .AIFEDebertaTransformer, .AIFEFunnelTransformer, .AIFELongformerTransformer, .AIFEMpnetTransformer, .AIFERobertaTransformer, .AIFETrObj