UNPKG

@huggingface/tasks

Version:
126 lines (125 loc) 4.93 kB
"use strict"; Object.defineProperty(exports, "__esModule", { value: true }); const taskData = { datasets: [ { description: "Multilingual dataset used to evaluate text generation models.", id: "CohereForAI/Global-MMLU", }, { description: "High quality multilingual data used to train text-generation models.", id: "HuggingFaceFW/fineweb-2", }, { description: "Truly open-source, curated and cleaned dialogue dataset.", id: "HuggingFaceH4/ultrachat_200k", }, { description: "A reasoning dataset.", id: "open-r1/OpenThoughts-114k-math", }, { description: "A multilingual instruction dataset with preference ratings on responses.", id: "allenai/tulu-3-sft-mixture", }, { description: "A large synthetic dataset for alignment of text generation models.", id: "HuggingFaceTB/smoltalk", }, { description: "A dataset made for training text generation models solving math questions.", id: "HuggingFaceTB/finemath", }, ], demo: { inputs: [ { label: "Input", content: "Once upon a time,", type: "text", }, ], outputs: [ { label: "Output", content: "Once upon a time, we knew that our ancestors were on the verge of extinction. The great explorers and poets of the Old World, from Alexander the Great to Chaucer, are dead and gone. A good many of our ancient explorers and poets have", type: "text", }, ], }, metrics: [ { description: "Cross Entropy is a metric that calculates the difference between two probability distributions. Each probability distribution is the distribution of predicted words", id: "Cross Entropy", }, { description: "The Perplexity metric is the exponential of the cross-entropy loss. It evaluates the probabilities assigned to the next word by the model. Lower perplexity indicates better performance", id: "Perplexity", }, ], models: [ { description: "A text-generation model trained to follow instructions.", id: "google/gemma-2-2b-it" }, { description: "Powerful text generation model for coding.", id: "Qwen/Qwen3-Coder-480B-A35B-Instruct", }, { description: "Great text generation model with top-notch tool calling capabilities.", id: "openai/gpt-oss-120b", }, { description: "Powerful text generation model.", id: "zai-org/GLM-4.5", }, { description: "A powerful small model with reasoning capabilities.", id: "Qwen/Qwen3-4B-Thinking-2507", }, { description: "Strong conversational model that supports very long instructions.", id: "Qwen/Qwen2.5-7B-Instruct-1M", }, { description: "Text generation model used to write code.", id: "Qwen/Qwen2.5-Coder-32B-Instruct", }, { description: "Powerful reasoning based open large language model.", id: "deepseek-ai/DeepSeek-R1", }, ], spaces: [ { description: "An application that writes and executes code from text instructions and supports many models.", id: "akhaliq/anycoder", }, { description: "An application that builds websites from natural language prompts.", id: "enzostvs/deepsite", }, { description: "A leaderboard for comparing chain-of-thought performance of models.", id: "logikon/open_cot_leaderboard", }, { description: "An text generation based application based on a very powerful LLaMA2 model.", id: "ysharma/Explore_llamav2_with_TGI", }, { description: "An text generation based application to converse with Zephyr model.", id: "HuggingFaceH4/zephyr-chat", }, { description: "A leaderboard that ranks text generation models based on blind votes from people.", id: "lmsys/chatbot-arena-leaderboard", }, { description: "An chatbot to converse with a very powerful text generation model.", id: "mlabonne/phixtral-chat", }, ], summary: "Generating text is the task of generating new text given another text. These models can, for example, fill in incomplete text or paraphrase.", widgetModels: ["mistralai/Mistral-Nemo-Instruct-2407"], youtubeId: "e9gNEAlsOvU", }; exports.default = taskData;