8000 Merge branch 'main' into update-chat-completion-grammar-specs · huggingface/huggingface.js@5a8188a · GitHub
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Merge branch 'main' into update-chat-completion-grammar-specs
2 parents 08296e5 + d4717a1 commit 5a8188a

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‎packages/jinja/test/templates.test.js

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@@ -4337,7 +4337,7 @@ describe("Templates", () => {
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// TODO add failure cases
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});
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describe("Parsing and intepretation", () => {
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describe("Parsing and interpretation", () => {
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describe("should interpret an AST", () => {
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for (const [name, text] of Object.entries(TEST_PARSED)) {
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const ast = parse(text);

‎packages/tasks/src/model-libraries-snippets.ts

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@@ -1354,7 +1354,7 @@ export const voicecraft = (model: ModelData): string[] => [
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model = VoiceCraft.from_pretrained("${model.id}")`,
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];
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export const vui = (model: ModelData): string[] => [
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export const vui = (): string[] => [
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`# !pip install git+https://github.com/fluxions-ai/vui
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import torchaudio

‎packages/tasks/src/pipelines.ts

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@@ -43,10 +43,6 @@ export interface PipelineData {
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name: string;
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subtasks?: SubTask[];
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modality: Modality;
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/**
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* color for the tag icon.
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*/
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color: "blue" | "green" | "indigo" | "orange" | "red" | "yellow";
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/**
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* whether to hide in /models filters
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*/
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},
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],
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modality: "nlp",
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color: "orange",
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},
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"token-classification": {
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name: "Token Classification",
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},
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],
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modality: "nlp",
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color: "blue",
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},
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"table-question-answering": {
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name: "Table Question Answering",
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modality: "nlp",
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color: "green",
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},
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"question-answering": {
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name: "Question Answering",
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},
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],
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modality: "nlp",
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color: "blue",
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},
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"zero-shot-classification": {
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name: "Zero-Shot Classification",
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modality: "nlp",
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color: "yellow",
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},
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translation: {
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name: "Translation",
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modality: "nlp",
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color: "green",
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},
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summarization: {
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name: "Summarization",
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},
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],
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modality: "nlp",
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color: "indigo",
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},
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"feature-extraction": {
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name: "Feature Extraction",
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modality: "nlp",
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color: "red",
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},
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"text-generation": {
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name: "Text Generation",
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},
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],
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modality: "nlp",
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color: "indigo",
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},
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"text2text-generation": {
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name: "Text2Text Generation",
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},
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],
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modality: "nlp",
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color: "indigo",
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},
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"fill-mask": {
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name: "Fill-Mask",
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},
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],
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modality: "nlp",
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color: "red",
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},
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"sentence-similarity": {
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name: "Sentence Similarity",
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modality: "nlp",
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color: "yellow",
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},
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"text-to-speech": {
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name: "Text-to-Speech",
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modality: "audio",
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color: "yellow",
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},
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"text-to-audio": {
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name: "Text-to-Audio",
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modality: "audio",
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color: "yellow",
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},
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"automatic-speech-recognition": {
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name: "Automatic Speech Recognition",
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modality: "audio",
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color: "yellow",
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},
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"audio-to-audio": {
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name: "Audio-to-Audio",
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modality: "audio",
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color: "blue",
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},
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"audio-classification": {
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name: "Audio Classification",
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},
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],
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modality: "audio",
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color: "green",
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},
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"audio-text-to-text": {
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name: "Audio-Text-to-Text",
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modality: "multimodal",
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color: "red",
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hideInDatasets: true,
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},
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"voice-activity-detection": {
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name: "Voice Activity Detection",
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modality: "audio",
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color: "red",
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},
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"depth-estimation": {
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name: "Depth Estimation",
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modality: "cv",
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color: "yellow",
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},
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"image-classification": {
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name: "Image Classification",
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},
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],
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modality: "cv",
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color: "blue",
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},
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"object-detection": {
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name: "Object Detection",
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},
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],
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modality: "cv",
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color: "yellow",
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},
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"image-segmentation": {
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name: "Image Segmentation",
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},
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],
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modality: "cv",
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color: "green",
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},
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"text-to-image": {
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name: "Text-to-Image",
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modality: "cv",
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color: "yellow",
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},
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"image-to-text": {
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name: "Image-to-Text",
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},
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],
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modality: "cv",
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color: "red",
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},
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"image-to-image": {
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},
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],
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modality: "cv",
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color: "indigo",
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},
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"image-to-video": {
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name: "Image-to-Video",
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modality: "cv",
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color: "indigo",
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},
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"unconditional-image-generation": {
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name: "Unconditional Image Generation",
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modality: "cv",
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color: "green",
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},
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"video-classification": {
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name: "Video Classification",
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modality: "cv",
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color: "blue",
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},
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"reinforcement-learning": {
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name: "Reinforcement Learning",
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modality: "rl",
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color: "red",
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},
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robotics: {
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name: "Robotics",
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name: "Task Planning",
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},
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],
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color: "blue",
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},
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"tabular-classification": {
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name: "Tabular Multi Label Classification",
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},
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],
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color: "blue",
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},
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"tabular-regression": {
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name: "Tabular Regression",
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name: "Tabular Single Column Regression",
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},
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],
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color: "blue",
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},
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"tabular-to-text": {
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name: "Tabular to Text",
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name: "RDF to text",
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},
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],
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color: "blue",
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hideInModels: true,
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},
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"table-to-text": {
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name: "Table to Text",
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modality: "nlp",
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color: "blue",
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hideInModels: true,
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},
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"multiple-choice": {
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},
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],
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modality: "nlp",
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color: "blue",
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hideInModels: true,
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},
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"text-ranking": {
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name: "Text Ranking",
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modality: "nlp",
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color: "red",
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},
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"text-retrieval": {
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name: "Text Retrieval",
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},
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],
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modality: "nlp",
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color: "indigo",
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hideInModels: true,
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},
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"time-series-forecasting": {
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name: "Multivariate Time Series Forecasting",
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},
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],
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color: "blue",
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},
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"text-to-video": {
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name: "Text-to-Video",
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modality: "cv",
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color: "green",
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},
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"image-text-to-text": {
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name: "Image-Text-to-Text",
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modality: "multimodal",
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color: "red",
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hideInDatasets: true,
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},
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"visual-question-answering": {
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},
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],
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modality: "multimodal",
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color: "red",
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},
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"document-question-answering": {
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name: "Document Question Answering",
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},
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],
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modality: "multimodal",
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color: "blue",
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hideInDatasets: true,
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},
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"zero-shot-image-classification": {
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name: "Zero-Shot Image Classification",
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modality: "cv",
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color: "yellow",
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},
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"graph-ml": {
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name: "Graph Machine Learning",
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modality: "other",
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color: "green",
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},
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"mask-generation": {
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name: "Mask Generation",
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modality: "cv",
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color: "indigo",
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},
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"zero-shot-object-detection": {
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name: "Zero-Shot Object Detection",
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modality: "cv",
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color: "yellow",
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},
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"text-to-3d": {
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name: "Text-to-3D",
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modality: "cv",
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color: "yellow",
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},
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"image-to-3d": {
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name: "Image-to-3D",
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modality: "cv",
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color: "green",
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},
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"image-feature-extraction": {
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name: "Image Feature Extraction",
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modality: "cv",
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color: "indigo",
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},
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"video-text-to-text": {
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name: "Video-Text-to-Text",
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modality: "multimodal",
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color: "blue",
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hideInDatasets: false,
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},
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"keypoint-detection": {
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],
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modality: "cv",
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color: "red",
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hideInDatasets: true,
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},
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"visual-document-retrieval": {
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name: "Visual Document Retrieval",
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modality: "multimodal",
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color: "yellow",
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hideInDatasets: true,
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},
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"any-to-any": {
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name: "Any-to-Any",
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modality: "multimodal",
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color: "yellow",
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hideInDatasets: true,
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},
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other: {
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name: "Other",
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modality: "other",
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color: "blue",
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hideInModels: true,
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hideInDatasets: true,
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},

‎packages/tasks/src/tasks/text-generation/about.md

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@@ -101,7 +101,7 @@ Would you like to learn more about the topic? Awesome! Here you can find some cu
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- You can use [PEFT](https://github.com/huggingface/peft) to adapt large language models in efficient way.
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- [ChatUI](https://github.com/huggingface/chat-ui) is the open-source interface to conversate with Large Language Models.
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- [text-generation-inferface](https://github.com/huggingface/text-generation-inference)
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- [text-generation-interface](https://github.com/huggingface/text-generation-inference)
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- [HuggingChat](https://huggingface.co/chat/) is a chat interface powered by Hugging Face to chat with powerful models like Meta Llama 3 70B, Mixtral 8x7B, etc.
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### Documentation

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