Text normalization is a crucial step in many natural language processing (NLP) tasks, allowing raw text data to be made more structured and readable. In this article, we will explore how to use the fine-tuned FRED-T5 model for Russian text normalization. With this powerful tool, you can transform written text that includes numbers and Latin words into a more readable format.

Getting Started with FRED-T5 Model

Before we dive into the code, it’s essential to understand what the FRED-T5 model is. The FRED-T5 model is a fine-tuned version of the T5 model trained specifically on Russian text datasets such as ficbook, librusec, and pikabu. It can handle text normalization involving numbers and Latin words efficiently.

Installation and Setup

To start using the FRED-T5 model, you need to install the necessary libraries. Here’s how to do it:

• Ensure you have Python and pip installed on your machine.
• Open your terminal and run the following command:
• pip install torch transformers

Using the Model

After installing the required libraries, you can begin to normalize text. Here’s a step-by-step guide:

• Import the necessary libraries:

import torch
from transformers import GPT2Tokenizer, T5ForConditionalGeneration

• Set up the device (GPU or CPU):

device = "cuda" if torch.cuda.is_available() else "cpu"

• Load the tokenizer and the model:

tokenizer = GPT2Tokenizer.from_pretrained('saarus72/russian_text_normalizer', eos_token='s')
model = T5ForConditionalGeneration.from_pretrained('saarus72/russian_text_normalizer').to(device)

• Prepare your input text by placing numbers and Latin words inside square brackets:

lm_text = "Было у отца [3]extra_id_0 сына, но не было даже [2-3]extra_id_1 пиджаков с блёстками за [142 990]extra_id_2 руб."

• Process the input and generate the output:

input_ids = torch.tensor([tokenizer.encode(lm_text)]).to(device)
outputs = model.generate(input_ids, eos_token_id=tokenizer.eos_token_id, early_stopping=True)
print(tokenizer.decode(outputs[0][1:]))

Understanding the Code Through Analogy

Imagine you are trying to bake a cake. You have all the ingredients (like flour, sugar, and eggs), but you need a recipe to guide you through the process. In our code example:

• Imports: Like gathering your ingredients, importing libraries prepares everything you need for baking, or in this case, normalization.
• Device Setup: Choosing between GPU and CPU is like deciding whether to use an oven or a microwave, depending on the speed you want.
• Loading Model: Loading the tokenizer and model is akin to setting your baking temperature and preparing the pan – it’s essential for success.
• Preparing Input: Putting numbers and Latin words in brackets is like layering your cake batter properly before putting it in the oven.
• Generating Output: Finally, just like pulling a perfectly baked cake out of the oven, when you decode the output, you receive your well-normalized text!

Examples of Normalization

Here are a few examples of inputs and their expected outputs:

Input: "Временами я думаю, какое применение найти тем [14 697]extra_id_0 рублям?"
Output: "Временами я думаю, какое применение найти тем четырнадцати тысячам шестистам девяноста семи рублям?"

Input: "я купил [iphone 12]extra_id_0 за [142 990]extra_id_1 руб."
Output: "я купил айфон двенадцатый за сто сорок две тысячи девятьсот девяносто руб."

Troubleshooting

Should you encounter issues during the usage of the FRED-T5 model, consider the following tips:

• Ensure that you correctly formatted your input text with square brackets.
• Check your dependencies; make sure PyTorch and transformers are properly installed.
• If it performs poorly on numbers, try adjusting your text or reloading an earlier version of the model with the command git checkout #8c2476b.

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Conclusion

With the FRED-T5 model, text normalization becomes a manageable task, especially for the Russian language. By following the steps above, you can manipulate and normalize text data efficiently.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.