115 件 ・ 関連度順
概要と使いどころ
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.
日本語の概要は準備中です。原文の説明を表示しています。
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.
日本語の概要は準備中です。原文の説明を表示しています。
Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text. Triggers: "text analytics", "sentiment analysis", "entity recognition", "key phrase", "PII detection", "TextAnalyticsClient".
日本語の概要は準備中です。原文の説明を表示しています。
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
日本語の概要は準備中です。原文の説明を表示しています。
Haystack NLP pipeline configuration for document processing and QA
日本語の概要は準備中です。原文の説明を表示しています。
Use when deciding whether a project is a genuine ACM MM (ACM Multimedia) contribution rather than single-modality work, choosing a thematic area, and routing between ACM MM, CVPR/ICCV, ACL/EMNLP, ICMR, MMSys, NeurIPS/ICLR, and the ACM TOMM journal by finding the cross-modal or media-systems core of the contribution.
日本語の概要は準備中です。原文の説明を表示しています。
Use when positioning an ACL submission against the NLP literature, covering ACL Anthology citation practice, arXiv-versus-published version citation, concurrent LLM-era preprints, prior-cycle ARR resubmission overlap, anonymity-preserving self-citation, and the fast-moving baseline problem in computational linguistics.
日本語の概要は準備中です。原文の説明を表示しています。
Use when strengthening reproducibility evidence for an ACL paper reviewed through ACL Rolling Review, covering the Responsible NLP checklist end to end, hyperparameter and compute reporting, prompt and decoding disclosure for LLM experiments, data contamination auditing, variance across runs, and checklist-to-paper consistency.
日本語の概要は準備中です。原文の説明を表示しています。
Use when packaging code, datasets, prompts, model outputs, or annotation materials for an ACL submission under ACL Rolling Review, covering anonymized supplement archives, scientific-artifact items of the Responsible NLP checklist, licensing and intended-use documentation, data statements, and post-acceptance public release.
日本語の概要は準備中です。原文の説明を表示しています。
Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.
日本語の概要は準備中です。原文の説明を表示しています。
Use when auditing an ACL submission before an ACL Rolling Review cycle deadline, covering long/short paper page budgets, the mandatory Limitations section, Responsible NLP checklist, anonymized PDFs and supplements, preprint declarations, dual-submission rules, resubmission linking, and ARR desk-reject triggers on OpenReview.
日本語の概要は準備中です。原文の説明を表示しています。
Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation, super-resolution, etc.), given a GitHub repo URL for an image/vision model, or asked to create an MPyReq demo for a deep-learning vision pipeline. Do NOT use for general-purpose Python-MATLAB interfacing, non-vision models (NLP, tabular, audio), model deployment/serving, or MATLAB-only image processing workflows.
日本語の概要は準備中です。原文の説明を表示しています。
帮助用户从NLP理解层次的顶层重新规划人生；当用户感到迷茫、深陷日复一日的执行循环、不知如何突破现状时使用
日本語の概要は準備中です。原文の説明を表示しています。
根据上下文或对话历史，对碎片化句子或最后一句话进行指代消解、主谓宾补全及改写，生成语义完整的句子，或提取核心意图。
日本語の概要は準備中です。原文の説明を表示しています。
对包含代码变量（{{}}）的中文任务模板进行改写，生成6组表达多样但意图不变的生产模板，严格保留变量原样。
日本語の概要は準備中です。原文の説明を表示しています。
Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use raw prompting or LangChain templates instead.
日本語の概要は準備中です。原文の説明を表示しています。
Python library for healthcare ML on EHR data: process MIMIC-III/IV, eICU, OMOP-CDM; encode medical codes (ICD, ATC, NDC); build patient-level datasets; train Transformer, RETAIN, GRASP, MedBERT for mortality, drug recommendation, readmission, diagnosis prediction. Alternatives: FIDDLE (preprocessing), clinical-longformer (clinical NLP), ehr-ml (embeddings).
日本語の概要は準備中です。原文の説明を表示しています。
Analyze construction contract clauses. Identify risks, obligations, and key terms using NLP.
日本語の概要は準備中です。原文の説明を表示しています。
Use Flair for NLP annotation, embeddings, text/sequence model prediction, corpus loading, training/fine-tuning, HunFlair biomedical NER/linking, and production troubleshooting.
日本語の概要は準備中です。原文の説明を表示しています。
Guides Gensim topic modelling, document indexing, embeddings, similarity retrieval, downloader, and script workflows for Python NLP and IR tasks.
日本語の概要は準備中です。原文の説明を表示しています。
DeepPavlov package routing for config-driven NLP workflows, model-family selection, and serving.
日本語の概要は準備中です。原文の説明を表示しています。
Route Deepchecks package tasks for ML data/model validation across tabular, NLP, vision, result export, and integration workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Routes CS230 code-example requests to the correct PyTorch or TensorFlow vision and NLP workflows for SIGNS image classification and named-entity recognition.
日本語の概要は準備中です。原文の説明を表示しています。
Write SEO pages that rank on Google AND get cited by LLMs. Uses live SERP data, 500-token chunk architecture, RAG optimization for Gemini 3.5 Flash, the Two-Gate AEO framework (retrieval-pool entry + selected-citation extraction), the Anti-NLP Stuffing Protocol (structural entity placement, no keyword-density stuffing), strict single-service local isolation, and the Reddit Test quality gate. Triggers on: "write an SEO page", "seo-agi", "seo page for [keyword]", "rank for [keyword]", "rewrite this page for SEO", "GEO", "AEO", "write a page that ranks".
日本語の概要は準備中です。原文の説明を表示しています。