「training data」の検索結果

ライブラリやフレームワークの設定方法、API仕様、コード例をContext7 MCPで調べ、取得した現行ドキュメントと指定バージョンに基づく回答を支援するスキル。

• フレームワークの導入・設定の確認
• ライブラリのAPIと使い方の調査
• Prismaのクエリ例を調べたいとき

Curate LLM training data: dedupe, filter, PII redaction.

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。

Fully sharded data-parallel training for large models.

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。

Fetches current, version-specific library documentation and code examples through the Context7 MCP server. Use whenever the user asks about a library, framework, SDK, API, CLI tool, or cloud service, including API syntax, configuration, setup instructions, version migration, CLI usage, and library-specific debugging. Use when generating code that calls a third-party library, and when the user names a version such as Next.js 15 or React 19. Use even for well-known libraries like React, Vue, Next.js, Prisma, Supabase, Express, Tailwind, Django, and Spring Boot, because training data may not reflect recent changes. Prefer this over web search for library documentation. Do not use it for refactoring, writing scripts from scratch, debugging business logic, code review, or general programming concepts, or when the user has already supplied the relevant documentation.

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。

Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.

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。

Applies Arbor Hypothesis Tree Refinement to research artifacts with repeatable evaluators, including model training, agent harnesses, data synthesis and benchmark optimization. Uses persistent hypotheses, isolated experiments, evidence propagation and held-out candidate comparison for multi-experiment research runs. Includes a standard-library state manager and guidance for the RUC-NLPIR Arbor CLI.

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。

Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.

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。

Machine-learning predictive strategy based on sklearn walk-forward training, feature engineering, and signal generation. Suitable for any OHLCV data.

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。

Configure engine and version. Pins it in CLAUDE.md; WebSearch fills reference docs when the version is beyond LLM training data.

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。

Mounts Cloud Storage buckets on Google Cloud as a POSIX file system using Cloud Storage FUSE (and the gcsfuse CLI tool). Use when interacting with the gcsfuse CLI or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre; use gke-storage).

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。

LLM fine-tuning expert for LoRA, QLoRA, dataset preparation, and training optimization

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。

This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.

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。

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Trackio monitoring, and Hub persistence. Use when users mention training object detection, image classification, SAM, SAM2, segmentation, image matting, DETR, D-FINE, RT-DETR, ViT, timm, MobileNet, ResNet, bounding box models, or fine-tuning vision models on Hugging Face Jobs.

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。

Review vendor AI terms — agreement, addendum, or ToS AI provisions — against your governance positions; flag training-on-data, liability, model changes, and AI policy consistency. Use when user says "review this AI agreement", "check OpenAI terms", "what did we agree to with [vendor]", "vendor sent an AI addendum", "is this AI contract okay", or attaches vendor AI terms.

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。

Web information retrieval expert. ALWAYS invoke this skill when the user needs to search the web, fetch a URL, or access real-time information beyond training data. Do not guess or use stale knowledge — use this skill first.

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 technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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。

Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

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。

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Automatically checks system RAM/GPU before loading the model, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

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。

This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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 the live web and read URLs through the You.com MCP server. Use when you need current information beyond training data: recent releases, docs, pricing, news, or any question where facts may have changed. Two endpoints: keyless basic search at https://api.you.com/mcp?profile=free, or full tools with an API key from https://you.com/platform/api-keys.

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 Google, Bing, Google News and Google Scholar, and read public pages, through the Serply MCP server. Use when you need current information beyond training data, recent news coverage, or academic papers, with source URLs. Remote server at https://api.serply.io/mcp with an API key from https://serply.io sent in the X-Api-Key header.

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。

Used for generating synthetic CT volumes and masks with NV-Generate-CTMR rflow-ct. Not for production training data without review.

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。

Used for generating synthetic body MRI volumes with NV-Generate-CTMR rflow-mr. Not for paired masks or production training data.

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。

Official NVIDIA-authored guidance for navigating PhysicsNeMo — pick the model, datapipe, or example for a SciML/AI4Science task (surrogates, forecasting, downscaling, physics-informed, inverse, generative). Points at existing files via live repo search; never writes code. Do NOT use for installation or environment setup, training-loop or other code authoring/scaffolding, contributor/CI/packaging questions, repo-specific questions in physicsnemo-sym/-cfd/-curator, or general (non-physics) ML/PyTorch.

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。