Formation of the neural network architecture of the model and downloading its weights to obtain personality traits scores (text modality)

_b5.text_model_b5_ - Neural network model tf.keras.Model for obtaining the personality traits scores

Import required packages

[2]:

from oceanai.modules.lab.build import Run

Build

[3]:

_b5 = Run(
    lang = 'en', # Inference language
    color_simple = '#333', # Plain text color (hexadecimal code)
    color_info = '#1776D2', # The color of the text containing the information (hexadecimal code)
    color_err = '#FF0000', # Error text color (hexadecimal code)
    color_true = '#008001', # Text color containing positive information (hexadecimal code)
    bold_text = True, # Bold text
    num_to_df_display = 30, # Number of rows to display in tables
    text_runtime = 'Runtime', # Runtime text
    metadata = True # Displaying information about library
)

[2023-12-10 17:03:46] OCEANAI - personaly traits: Authors: Elena Ryumina [ryumina_ev@mail.ru] Dmitry Ryumin [dl_03.03.1991@mail.ru] Alexey Karpov [karpov@iias.spb.su] Maintainers: Elena Ryumina [ryumina_ev@mail.ru] Dmitry Ryumin [dl_03.03.1991@mail.ru] Version: 1.0.0a16 License: BSD License

Formation of neural network architectures of models

[4]:

res_load_text_model_b5 = _b5.load_text_model_b5(
    show_summary = False, # Displaying the formed neural network architecture of the model
    out = True, # Display
    runtime = True, # Runtime count
    run = True # Run blocking
)

[2023-12-10 17:03:46] Formation of neural network architectures of models for obtaining the personality traits scores (text modality) …

— Runtime: 0.539 sec. —

Downloading weights of neural network models

`FI V2`

[5]:

# Core settings
_b5.path_to_save_ = './models' # Directory to save the file
_b5.chunk_size_ = 2000000 # File download size from network in 1 step

url = _b5.weights_for_big5_['text']['fi']['b5']['sberdisk']

res_load_text_model_weights_b5 = _b5.load_text_model_weights_b5(
    url = url,
    force_reload = False, # Forced download of a file with weights of a neural network model from the network
    out = True, # Display
    runtime = True, # Runtime count
    run = True # Run blocking
)

[2023-12-10 17:03:46] Downloading the weights of neural network models to obtain the personality traits scores (text modality) …

[2023-12-14 21:04:19] File download “ft_fi_2023-12-09_14-25-13.h5”

— Runtime: 0.144 sec. —

`MuPTA`

[6]:

# Core settings
_b5.path_to_save_ = './models' # Directory to save the file
_b5.chunk_size_ = 2000000 # Размер загрузки файла из сети за 1 шаг

url = _b5.weights_for_big5_['text']['mupta']['b5']['sberdisk']

res_load_text_model_weights_b5 = _b5.load_text_model_weights_b5(
    url = url,
    force_reload = False, # Forced download of a file with weights of a neural network model from the network
    out = True, # Display
    runtime = True, # Runtime count
    run = True # Run blocking
)

[2023-12-10 17:03:47] Downloading the weights of neural network models to obtain the personality traits scores (text modality) …

[2023-12-10 17:03:47] File download “ft_mupta_2023-12-09_14-25-13.h5”

— Runtime: 0.137 sec. —

Displaying the formed neural network architecture of the model

[7]:

_b5.text_model_b5_.summary()

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to
==================================================================================================
 input_1 (InputLayer)           [(None, 5)]          0           []

 input_2 (InputLayer)           [(None, 5)]          0           []

 tf.concat (TFOpLambda)         (None, 10)           0           ['input_1[0][0]',
                                                                  'input_2[0][0]']

 dense (Dense)                  (None, 5)            55          ['tf.concat[0][0]']

==================================================================================================
Total params: 55
Trainable params: 55
Non-trainable params: 0
__________________________________________________________________________________________________