FRED-T5-1.7B
Maintainer: ai-forever
63
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
The FRED-T5-1.7B (Full-scale Russian Enhanced Denoisers T5) is a language model developed by SberDevices and based on the T5 architecture. It was trained on a 300GB Russian language corpus and has 24 layers and 1536 hidden size. The model was trained on a mixture of 7 different denoisers, similar to the UL2 model, with several differences. It uses a BBPE tokenizer with 50,257 tokens plus 107 special tokens.
The FRED-T5-1.7B model is part of a family of Russian language models developed by the SberDevices team, similar to models like the mGPT which covers 61 languages. The FRED-T5-1.7B focuses specifically on the Russian language and has been enhanced with additional denoising capabilities.
Model inputs and outputs
Inputs
- Text: The model accepts various types of text input, including prompts, tasks, and other natural language text.
- Prefix tokens: The model uses a set of six prefix tokens (
<LM>,<SC1>, ...,<SC6>) to specify the type of task or output desired.
Outputs
- Text: The model generates coherent, fluent text outputs in Russian based on the provided inputs and prefix tokens.
Capabilities
The FRED-T5-1.7B model is capable of a variety of text-to-text tasks in the Russian language, such as language modeling, text generation, and other natural language processing applications. The model's denoising capabilities allow it to generate high-quality, fluent Russian text even when the input is noisy or incomplete.
What can I use it for?
The FRED-T5-1.7B model can be used for a wide range of Russian language applications, including:
- Content generation: Creating Russian-language articles, stories, or other text-based content.
- Language modeling: Evaluating and scoring the grammaticality and fluency of Russian text.
- Text summarization: Generating concise summaries of longer Russian-language documents.
- Machine translation: Translating text between Russian and other languages.
The model's versatility and strong performance on a variety of Russian language tasks make it a valuable resource for researchers, developers, and businesses working with Russian text.
Things to try
One interesting aspect of the FRED-T5-1.7B model is its use of prefix tokens to specify different tasks or output formats. By experimenting with different prefix tokens, you can explore the model's capabilities in areas like language modeling, text generation, and more. For example, you could try using the <SC1> prefix to generate text with a particular style or tone, or the <SC5> prefix to produce text with a specific structure or formatting.
Another interesting area to explore is the model's denoising capabilities. By intentionally introducing noise or errors into your input text, you can see how the model handles and corrects these issues, producing high-quality, fluent Russian output.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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