mGPT
Maintainer: ai-forever
228
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| Property | Value |
|---|---|
| Run this model | Run 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 mGPT is a family of autoregressive GPT-like models with 1.3 billion parameters, trained on 61 languages from 25 language families using Wikipedia and Colossal Clean Crawled Corpus. The model was developed by ai-forever and the source code is available on Github. The model reproduces the GPT-3 architecture using GPT-2 sources and the sparse attention mechanism, leveraging the Deepspeed and Megatron frameworks to effectively parallelize the training and inference steps. The resulting models show performance on par with the recently released XGLM models, while covering more languages and enhancing NLP possibilities for low resource languages.
Model inputs and outputs
Inputs
- Sequence of text in any of the 61 supported languages
Outputs
- Predicted next token in the input sequence
Capabilities
The mGPT model is capable of generating text in 61 languages across 25 language families, including low-resource languages. This makes it a powerful tool for multilingual and cross-lingual natural language processing tasks, such as machine translation, text generation, and language understanding.
What can I use it for?
The mGPT model can be used for a variety of natural language processing tasks, such as text generation, language translation, and language understanding. Researchers and practitioners can use this model as a foundation for building more advanced NLP applications, particularly for working with low-resource languages. For example, the model could be fine-tuned on domain-specific data to create specialized language models for applications in fields like healthcare, finance, or education.
Things to try
One interesting aspect of the mGPT model is its ability to handle a wide range of languages, including those with very different writing systems and linguistic structures. Researchers could explore the model's cross-lingual capabilities by evaluating its performance on tasks that require understanding and generating text across multiple languages, such as zero-shot or few-shot translation. Additionally, the model's multilingual nature could be leveraged to build language-agnostic NLP systems that can operate seamlessly across languages.
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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