---
license: apache-2.0
datasets:
- NebulasBellum/pizdziuk_luka
language:
- ru
pipeline_tag: text-generation
tags:
- legal
---

# Lukashenko Generator

There is the Text-to-Text Generative AI
This model generate the phrases which are familar to that 
the Diktator and fascist from Belarus - **Aliaksandr Lukashenko** 
could speak.

## Documentation

- [Description](#description)
- [Quick Start](#quick-start)
- [Try in HF space](#hf-space)

## Description

The model used the dataset: [NebulasBellum/pizdziuk_luka](https://huggingface.co/datasets/NebulasBellum/pizdziuk_luka)
the [parquet dataset version](https://huggingface.co/datasets/NebulasBellum/pizdziuk_luka/tree/refs%2Fconvert%2Fparquet)
which was collected by the telegram chanel **Pul Pervogo**.
Only with the help of this chanel we could make this great 
job like creating the **fascist Diktator Lukashenko** speech :)

Model was trained with **340** epochs and produce a very good results.
```commandline
loss: 0.2963 - accuracy: 0.9159
```

The model all time in the improving by use the extended datasets which 
all time improving by adding the new speach of the **Fascist Lukashenko**. 
All information collected from public, and not only :) (thanks our partizanz).

Right now the model folder [NebulasBellum/Lukashenko_tarakan](https://huggingface.co/NebulasBellum/Lukashenko_tarakan/tree/main) 
contain all neccesary files for download and use the model in the ```TensorFlow``` library 
with collected weights ```weights_lukash.h5```


## Quick Start

For use this model with the ```TensorFlow``` library you need:

1. Download the model:

```commandline
md Luka_Pizdziuk
cd Luka_Pizdziuk
git clone https://huggingface.co/NebulasBellum/Lukashenko_tarakan/tree/main
```

and create the python script:

```python
import tensorflow as tf
import copy
import numpy as np


# add the start generation of the lukashenko speech from the simple seed
seed_text = 'я не глядя поддержу'
weights_path = 'weights_lukash.h5'
model_path = 'Lukashenko_tarakan'

# Load the model to the Keras from saved files
model = tf.keras.models.load_model(model_path)
model.load_weights(weights_path)
# Show the Model summary
model.summary()

# Load the dataset to the model
with open('source_text_lukash.txt', 'r') as source_text_file:
    data = source_text_file.read().splitlines()

tmp_data = copy.deepcopy(data)
sent_length = 0
for idx, line in enumerate(data):
    if len(line) < 5:
        tmp_data.pop(idx)
    else:
        sent_length += len(line.split())
data = tmp_data
lstm_length = int(sent_length / len(data))

# Tokenize the dataset
token = tf.keras.preprocessing.text.Tokenizer()
token.fit_on_texts(data)
encoded_text = token.texts_to_sequences(data)
# Vocabular size
vocab_size = len(token.word_counts) + 1

# Create the sequences
datalist = []
for d in encoded_text:
    if len(d) > 1:
        for i in range(2, len(d)):
            datalist.append(d[:i])

max_length = 20
sequences = tf.keras.preprocessing.sequence.pad_sequences(datalist, maxlen=max_length, padding='pre')

# X - input data, y - target data
X = sequences[:, :-1]
y = sequences[:, -1]

y = tf.keras.utils.to_categorical(y, num_classes=vocab_size)
seq_length = X.shape[1]

# Generate the Lukashenko speech from the seed
generated_text = ''
number_lines = 3
for i in range(number_lines):
    text_word_list = []
    for _ in range(lstm_length * 2):
        encoded = token.texts_to_sequences([seed_text])
        encoded = tf.keras.preprocessing.sequence.pad_sequences(encoded, maxlen=seq_length, padding='pre')

        y_pred = np.argmax(model.predict(encoded), axis=-1)

        predicted_word = ""
        for word, index in token.word_index.items():
            if index == y_pred:
                predicted_word = word
                break

        seed_text = seed_text + ' ' + predicted_word
        text_word_list.append(predicted_word)

    seed_text = text_word_list[-1]
    generated_text = ' '.join(text_word_list)
    generated_text += '\n'

print(f"Lukashenko are saying: {generated_text}")

```

## Try in HF space

The ready to check space with working model are placed here: 

[Hugging Face Test Space](https://huggingface.co/spaces/NebulasBellum/gen_ai_simple_space)


**To contribute to the project you could help**

to TRX: TDqjSX6dB6eaFbpHRhX8CCZUSYDmMVvvmb

to BNB: 0x107119102c2EC84099cDce3D5eFDE2dcbf4DEB2a

to USDT TRC20: TDqjSX6dB6eaFbpHRhX8CCZUSYDmMVvvmb

25% goes to the Help Ukraine to Win