mt0-large
Maintainer: bigscience
40
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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 mt0-large model is part of the BLOOMZ and mT0 family of models developed by the BigScience workshop. These models are capable of following human instructions in dozens of languages without explicit training, a capability known as zero-shot cross-lingual generalization. The mt0-large model was finetuned on the BigScience xP3 dataset and is recommended for prompting in English. Similar models in the family include the larger mt0-xxl and smaller variants like mt0-base and mt0-small.
Model inputs and outputs
The mt0-large model is a text-to-text transformer that can accept natural language prompts as input and generate corresponding text outputs. The model was trained to perform a wide variety of tasks, from translation and summarization to open-ended generation and question answering.
Inputs
- Natural language prompts expressing specific tasks or requests
Outputs
- Generated text outputs corresponding to the input prompts, such as translated sentences, answers to questions, or continuations of stories.
Capabilities
The mt0-large model demonstrates impressive cross-lingual capabilities, able to understand and generate text in many languages without being explicitly trained on all of them. This allows users to prompt the model in their language of choice and receive relevant and coherent responses. The model also exhibits strong few-shot and zero-shot performance on a variety of tasks, suggesting its versatility and adaptability.
What can I use it for?
The mt0-large model can be useful for a wide range of natural language processing tasks, from language translation and text summarization to open-ended generation and question answering. Developers and researchers could leverage the model's cross-lingual abilities to build multilingual applications, while business users could utilize the model to automate content creation, customer support, and other language-based workflows.
Things to try
One interesting aspect of the mt0-large model is its ability to follow complex, multi-step instructions expressed in natural language. For example, you could prompt the model with a request like "Write a fairy tale about a troll saving a princess from a dangerous dragon. The fairy tale should be a masterpiece that has achieved praise worldwide and its moral should be 'Heroes Come in All Shapes and Sizes'. Story (in Spanish):" and the model would attempt to generate a complete fairy tale meeting those specifications.
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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The mt0-xxl model, part of the BLOOMZ & mT0 model family, is a large language model capable of following human instructions in dozens of languages zero-shot. It was created by the BigScience workshop by finetuning the pretrained BLOOM and mT5 models on the cross-lingual task mixture dataset xP3. This process of multitask finetuning has enabled the model to generalize across a wide range of unseen tasks and languages. Model inputs and outputs Inputs Natural language prompts expressing tasks or queries The model can understand a diverse set of languages, spanning those used in the pretraining data (mc4) and finetuning dataset (xP3). Outputs Relevant, coherent text responses to the input prompts The model can generate text in the languages it was trained on, allowing it to perform tasks like translation, generation, and explanation across many languages. Capabilities The mt0-xxl model is highly versatile, able to perform a wide variety of language tasks in multiple languages. It can translate text, summarize information, answer questions, generate creative stories, and even explain complex technical concepts. For example, it can translate a French sentence to English, write a fairy tale about a troll saving a princess, or explain backpropagation in neural networks in Telugu. What can I use it for? The mt0-xxl model is well-suited for applications that require multilingual natural language processing, such as chat bots, virtual assistants, and language learning tools. Its zero-shot capabilities allow it to handle tasks in languages it was not explicitly trained on, making it a valuable asset for global or multilingual projects. Companies could potentially use the model to provide customer support in multiple languages, generate content in various languages, or even assist with language learning and translation. Things to try One interesting aspect of the mt0-xxl model is its ability to follow instructions and perform tasks based on natural language prompts. Try providing the model with prompts that require reasoning, creativity, or cross-lingual understanding, such as asking it to write a short story about a troll saving a princess, or explaining a technical concept in a non-English language. Experiment with different levels of detail and context in the prompts to see how the model responds. You can also try the model on a variety of languages to assess its multilingual capabilities.
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