199건 · 관련도순
Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.
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Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build text tokenizers that split multilingual text into small units for AI models. Learn how to train SentencePiece on raw text, configure BPE or Unigram splitting methods, and convert text to token IDs and back.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Replace sentence-by-sentence revised or paraphrased text back into an original paper or document while preserving the original formatting, typography, paragraph settings, citation layout, and document conventions as the source of truth. Use when the user provides revised text that maps back to original sentences, paragraphs, sections, or the whole document, but may also contain clear local sentence merges or splits, and asks to replace it back into the source, keep the original formatting, do sentence-by-sentence replacement, preserve original formatting, or only swap wording without rewriting the document. Only allow minimal edits to the rewritten text when fixing citation formatting, restoring protected original content, restoring the original person reference, or minimally splitting/merging text back into original sentence slots; otherwise do not improve grammar, style, logic, or wording.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Generate embeddings, numerical representations of text or images, for meaning-based search, similarity comparisons, and document grouping. The skill guides model selection, multilingual processing, local execution, and integration with retrieval workflows.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build text tokenizers, which split text into units for AI models, directly from raw multilingual text. Learn custom vocabularies with BPE or Unigram, convert text to pieces or numeric IDs, and decode the results.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), `CrossEncoder` (reranker, pair scoring for two-stage retrieval / pair classification), `SparseEncoder` (SPLADE, sparse embedding model for learned-sparse retrieval), and `MultiVectorEncoder` (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-negative mining, evaluators, distillation, LoRA, Matryoshka, and Hugging Face Hub publishing. Use for any sentence-transformers training task.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Unwraps hard-wrapped markdown files so that each sentence ends with a newline instead of mid-sentence line breaks. Joins continuation lines within a paragraph into single lines, then re-breaks at sentence boundaries (period, question mark, exclamation point). Preserves blank lines, headings, fenced code blocks, block quotes, and list items. Use when asked to unwrap text, fix line breaks, reflow sentences, or clean up hard-wrapped markdown or .qmd files.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Unwraps hard-wrapped markdown files so that each sentence ends with a newline instead of mid-sentence line breaks. Joins continuation lines within a paragraph into single lines, then re-breaks at sentence boundaries (period, question mark, exclamation point). Preserves blank lines, headings, fenced code blocks, block quotes, and list items. Use when asked to unwrap text, fix line breaks, reflow sentences, or clean up hard-wrapped markdown or .qmd files.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Requires OFOX_API_KEY — create one at https://app.ofox.ai. Turn an article, doc or release note into a short explainer clip — one person to camera, or a voiceover over illustrative footage. The user supplies the source text and the model generates the speech; audio cannot be uploaded, measured. A 30-second clip holds about eighty spoken words in eight sentences — measured, and well under a tenth of a 1,200-word post — so this skill does not summarise an article, it picks the single idea worth saying and helps choose which one. Use when a user asks to turn writing into a short spoken video, e.g. "make a 30-second explainer from this blog post", "explain this feature in a short video", "turn our changelog into a clip", "a quick video explaining what this paper found". Do not use for a scene between people (see seedance-short-drama), a brand or product ad (see seedance-ad-creative), a handheld creator clip (see ugc-ads), or when the user already has both a portrait and the finished words (see talking-head). Budget sentences as well as words — each sentence boundary costs about 0.7 seconds of silence, so a script with more sentences runs longer at the same word count.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Writes user-facing comments, plans, and documents in Simplified Technical English, a controlled form of English based on ASD-STE100. It uses short sentences, consistent vocabulary, and one instruction per sentence to make explanations clearer.
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Turn supplied notes or a PDF and a syllabus into focused exam revision material. Extract definitions, key points, answer sentences, and practice questions, with adjustments for multiple-choice exams and limited study time, using only the provided material.
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Generate tiered sentence frames for academic tasks that scaffold language production across proficiency levels. Use when EAL students need structured language support for classroom discourse.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Train or fine-tune SentenceTransformer bi-encoders, CrossEncoder rerankers, or SparseEncoder models, including losses, negatives, evaluation, distillation, LoRA, and Matryoshka. Use for sentence-transformers training tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate tiered sentence frames for academic tasks that scaffold language production across proficiency levels. Use when EAL students need structured language support for classroom discourse.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review blog posts, landing pages, and guides for readability. Estimate reading difficulty, identify long sentences and jargon, and simplify wording and paragraph structure so broad audiences can follow the content more easily.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Improve existing marketing copy while preserving its message and voice. The skill reviews clarity, evidence, specific details, and prompts to act, refreshes outdated content, and replaces formulaic AI phrasing with language grounded in available facts.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review and rewrite Chinese product announcements, articles, emails, and README text using eleven style checks. Identify repetitive phrasing, overloaded sentences, vague openings, and decorative language, then revise them into clearer statements suited to each type of writing.
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Document why you own a stock, the assumptions behind the purchase, and conditions for reconsidering it. Review these against quarterly earnings and major events, score the investment rationale's health, and outline whether to hold, add, trim, or exit.
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Apply over 240 reusable prompt templates to summarize content, extract insights, improve writing, review code, or analyze security threats. It reads each template directly and returns results in the format defined for that task.
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Edit drafts to remove predictable AI writing habits, including filler phrases, formulaic contrasts, vague claims, and repetitive sentence rhythms. Use concrete wording and active voice, then assess the prose across five review criteria.
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Explore personal meaning by reflecting on significant experiences, sacrifices, and mortality. Develop a one-sentence statement of what matters, then reconsider how to spend limited time using a monthly question about having one year left.
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