@huggingface/tasks
Version:
List of ML tasks for huggingface.co/tasks
126 lines (125 loc) • 4.93 kB
JavaScript
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;
;