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Provides labeled movie-review data for binary sentiment classification: 25,000 training and 25,000 test examples, plus 50,000 unlabeled reviews for unsupervised or semi-supervised use. Labels reflect strong polarity (positive ≥7, negative ≤4) and the set is a widely used NLP benchmark.
Provides about 100,000 crowd‑written question–answer pairs from Wikipedia where each answer is a text span in the passage, used to train and evaluate extractive question‑answering models. Includes train/validation splits, span offsets, Parquet format, CC BY‑SA 4.0.
A multi-task English NLU benchmark for evaluating models across nine tasks (acceptability, sentiment, paraphrase, similarity, and various NLI setups), with a diagnostic evaluation set and an online leaderboard to compare generalization and transfer learning.
A public dataset of one million real-world conversations with 25 LLMs, including conversation text, model name, detected language tags, and OpenAI moderation outputs — useful for studying prompt distributions, safety/moderation, and training/evaluating instruction-following models.
Benchmark for evaluating general AI assistants with 466 short, real-world questions that require tool use, multimodality and reasoning; provides a public dev set and a withheld test set used for leaderboard evaluation.
A multiple-choice benchmark for evaluating LLM understanding in Traditional Chinese across 66 subjects (elementary to professional). Contains ~22K verified questions covering STEM, humanities, social sciences and Taiwan-specific topics, with standardized splits and model leaderboards under an MIT license.
Provides manually curated Japanese instruction pairs (questions and safe reference answers) for improving LLM output safety, covering broad harm categories and regionally sensitive cases. Includes English meta-tags and standard splits for benchmarking and fine-tuning.
A 12K-question multiple-choice benchmark that raises difficulty by expanding choices to ten and adding reasoning-focused problems. Designed for robust LLM evaluation with cross-domain coverage, expert curation, and emphasis on chain-of-thought performance.
Provides low‑latency on‑device speech-to-text, intent recognition, and text-to-speech for building real‑time voice agents and interfaces. Streaming-optimized models, incremental caching, multilingual TTS/ASR and cross-platform bindings (Python, iOS, Android, Linux, Raspberry Pi) target live voice use cases where sub-200ms responsiveness matters.
Measures generative AI inference performance with token-level metrics (TTFT, inter-token latency), latency, and throughput under realistic traffic patterns. Provides a multiprocess engine, real-time TUI dashboard, extensible plugins, and integrations for telemetry and result uploads, aimed at inference benchmarking and capacity planning.
Provides 1.7M+ synthetic and real infographic charts paired with their tabular data for training and evaluating multimodal models on infographic understanding, chart-to-table extraction, chart code generation, and example-based chart synthesis.
Provides MS MARCO queries, passages and answers translated into 14 Indic languages while keeping the original English content and per-example translation metadata. Includes train/validation splits, passage selection flags, and translation model parameters for multilingual IR, QA and RAG research.