Results for “nlp-metrics”

Design a comprehensive product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds.

Identify a North Star Metric and 3-5 supporting input metrics, classify the business game (Attention, Transaction, Productivity), and validate against seven criteria for an effective North Star.

Process text with NLP. Use when tokenizing, analyzing sentiment, extracting entities, summarizing documents, or measuring similarity.

Provides startup metrics frameworks and stage-specific benchmarks for SaaS, Marketplace, Consumer, and B2B models, including investor-ready dashboard generation.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Design NPS programs with measurement and action. TRIGGERS - Use when user needs help with nps-program related tasks.

Define, track, and interpret product metrics across discovery, growth, and mature product stages using frameworks like AARRR, North Star, and HEART.

Build the funnel metrics that actually get trusted. Stage-by-stage conversion, velocity, win rate, and the single biggest leak, with every definition pinned so nobody relitigates the numbers in the meeting. Built for B2B RevOps teams, customizable to your CRM and your stage model. Trigger on "build my funnel metrics", "what's my conversion by stage", "where's the leak", "what's our win rate", "how fast do deals move", or any funnel diagnostic.

NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.

Runs R statistics analyses on local datasets, producing NLSS-format tables, narratives, and JSONL logs from CSV, SAV, RDS, RData, or Parquet files.

NLVR2: A Visual Reasoning Benchmark for Natural Language

Design product metric trees, North Star metrics, input metrics, and instrumentation needs.

Defining KPIs, tracking events, and dashboards: North Star Metric, funnel and cohort analysis, test-intelligence views. GA4/Amplitude/Mixpanel/PostHog. Use when metrics design is needed.

OpenTelemetry observability patterns: traces, metrics, logs, context propagation, OTLP export, Collector pipelines, and troubleshooting

Fast tokenization for NLP using Rust-based tokenizers supporting BPE, WordPiece, and Unigram algorithms, with training, alignment tracking, and padding/truncation.

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

Design NPS programs with measurement and action. TRIGGERS - Use when user needs help with nps-program related tasks.

Solve LP, MILP, and QP problems using the cuOpt C API with a consistent build pattern and core calls.

Understand and query the NPSP data model, including namespace prefixes, GAU allocations, recurring donations, relationships, and affiliations.

Choose and audit startup metrics using the Lean Analytics framework: separate actionable metrics from vanity metrics, identify the One Metric That Matters for your business model and stage, set targets, and plan instrumentation.

Evaluates LLM performance using BLEU, ROUGE metrics and LLM-as-judge. Use for model testing.

Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.

Computes the Recall metric using torchmetrics, including configuration for binary, multiclass, and multilabel tasks.

Audits medical LLM benchmarks across five lifecycle phases using 46 medically tailored criteria to assess clinical relevance, data integrity, safety-critical capabilities, validity, and governance.

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B.

Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution on local Docker, Slurm HPC, or cloud platforms.

Evaluates text-to-speech systems by measuring distributional distance between synthetic and real speech across five factors, producing a scalar score without subjective MOS ratings.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Internal network assessment. VLAN hopping, ARP spoofing detection, broadcast protocol abuse (LLMNR/NBT-NS/mDNS), network segmentation verification, SNMP enumeration, NFS exposure, router/switch audit, and internal service mapping. Assumes attacker has network access. Uses nmap, arp-scan, nbtscan, snmpwalk, onesixtyone, smbmap, nfs-common, masscan, hping3, and netexec.

Analyze product health across acquisition, activation, engagement, retention, quality, and monetization.

Detects insider data exfiltration by analyzing DLP policy violations, file access patterns, upload volume anomalies, and off-hours activity in endpoint and cloud logs using pandas for behavioral analytics and statistical baselines.

Nocaps: Novel Object Captioning at Scale

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.

Every metric encodes an opinion about which mistake hurts.