'tokenization' 검색 결과

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.

• Preparing Japanese, Chinese, or Korean text for AI models
• Training a custom vocabulary from raw text
• Choosing between BPE and Unigram

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.

• Preparing text for multilingual AI models
• Training a vocabulary from Japanese text
• Reusing a tokenizer for reproducible text splitting

Prepare text for language models using HuggingFace Tokenizers, which splits text into model-readable units. Train custom vocabularies, process batches, track original text positions, and configure input lengths for use with transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing large text collections for language models
• Training a tokenizer on domain-specific text
• Mapping model outputs back to original text

Prepare large text collections for AI models with fast tokenizers, which split text into model inputs. Train custom vocabularies, trace tokens to original text positions, adjust input lengths, and integrate the results with transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Tokenizing large text collections
• Training a custom vocabulary
• Mapping extracted names or answers back to original text

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

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.

Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

텍스트를 AI 모델이 다루는 작은 단위로 나누고 숫자 배열로 변환합니다. 대량 처리, 맞춤 어휘 학습, 원문 위치 대응과 입력 길이 조정을 돕습니다.

• 대량 텍스트를 모델 입력으로 전처리
• 텍스트 파일로 맞춤 어휘 학습
• 예측 결과를 원문 위치에 연결

Prepare and analyze genomic intervals, regions defined by chromosome coordinates, with Gtars. Validate BED files, compare overlaps and coverage, combine region sets, and plan tokenization or reference storage while tracking data provenance and resource limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validating BED coordinates and reference assemblies
• Counting overlaps and comparing genomic region sets
• Converting genomic intervals into token IDs

PCI DSS v4.0 payment card data security compliance, assessment, and implementation. Use for PCI DSS scoping, cardholder data environment (CDE) security, SAQ and ROC preparation, QSA engagement, tokenization, and merchant compliance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assessment of pseudonymization techniques and re-identification risk. Covers tokenization, hashing, encryption-based pseudonymization, and hybrid approaches. Includes re-identification risk scoring using the motivated intruder test, quantitative metrics (marketer, journalist, prosecutor models), and linkage attack resilience evaluation. References ENISA 2019 pseudonymization report.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

벵골어 검색과 AI 처리를 위한 문자 표현 통일, 단어 분리, 숫자 변환, 결합 문자를 보존하는 표시 방법을 정리합니다. 학습 데이터 정비와 텍스트 저장에도 활용할 수 있습니다.

• 벵골어 검색과 입력 기능을 구현합니다
• 벵골어 학습 데이터를 정리합니다
• 벵골어와 영어 대화를 지원합니다

Help secure payment systems using PCI DSS, the payment card data security standard. Covers encryption, log masking, access controls, and tokenization, which replaces card details with substitute values to reduce direct handling of sensitive data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing secure payment flows
• Replacing card details with payment tokens
• Reviewing sensitive data in logs and storage

Add or verify model support in SGLang's Rust processor so chat requests produce the same prompt text and token IDs as Python. Use reference-generated test cases to find differences in tools, reasoning settings, conversation history, and errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding model support to sglang-processor
• Porting model rendering from sgl-router
• Rechecking prompt behavior after a Dynamo update

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit applications and infrastructure handling payment card data against PCI DSS v4.0. Heavy emphasis on scope determination (the single most-leveraged variable) plus the engineering-relevant requirements — Req 3 (storage of CHD), Req 4 (transmission), Req 6 (secure SDLC), Req 7-8 (access), Req 10 (logging), Req 11 (testing), Req 12 (program). Use when the user mentions 'PCI,' 'PCI DSS,' 'PCI DSS 4.0,' 'payment card,' 'cardholder data,' 'CHD,' 'PAN,' 'PCI scope,' 'PCI compliance,' 'SAQ,' 'AoC,' 'attestation of compliance,' 'tokenization,' 'P2PE,' 'network segmentation for PCI,' or audits any system that stores, processes, or transmits payment card data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Rust-backed Python library for fast genomic token arithmetic and BED processing. High-performance BED I/O, interval set ops (intersect, merge, complement, subtract), region tokenization against a universe, universe construction. Use for preprocessing large BED collections and ML token vocabularies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

HuggingFace Transformers with biomedical LMs (BioBERT, PubMedBERT, BioGPT, BioMedLM) for scientific NLP: NER (genes, diseases, chemicals), relation extraction, QA, text classification, abstract summarization. Covers loading, biomedical tokenization, inference pipelines, fine-tuning. Alternatives: spaCy en_core_sci_lg (rule-based NER), Stanza (biomedical models), NLTK.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Fintech engineering expertise — payment processing (Stripe, Plaid), PCI DSS compliance, financial data modeling (double-entry bookkeeping), fraud detection patterns, bank-grade security (encryption, tokenization), open banking APIs, cryptocurrency/blockchain integration, regulatory compliance (KYC/AML), and idempotent financial transaction design. Use for payment systems, banking apps, trading platforms, and fintech infrastructure.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.