AI Agent2025
Builds a table-of-contents tree index over long PDFs and uses LLM tree search to fetch relevant sections — no embeddings, chunking, or vector database. Hits 98.7% on FinanceBench, for financial, legal, and technical docs where relevance needs reasoning.
AI Dataset2025
A human-verified subset of 500 SWE-bench test cases for evaluating models that resolve GitHub issues into PRs using unit-test verification. Contains problem statements and base commits (pre-fix) for reproducible unit-test based evaluation; suitable for benchmarking code-fix and issue-resolution capabilities.
AI Client2025
Translates full-length books, subtitles, and documents with LLMs while preserving original formatting and structure. Uses intelligent chunking to handle arbitrarily long files, supports local or cloud providers, and resumes interrupted jobs without losing progress.
Practical, full-stack tutorial for building Retrieval-Augmented Generation (RAG) systems—covers data preprocessing, vector embedding and indexing, hybrid and multimodal retrieval, generation integration, evaluation and production-ready engineering. Includes hands-on projects and examples for developers with Python experience.
AI Dataset2025
1,000,000 US-focused synthetic persona records (6M persona texts) grounded to demographic, geographic and personality distributions. Contains age, sex, education, occupation and ZCTA/city fields; CC BY 4.0 license for LLM training, diversity augmentation, and bias mitigation.
AI API2025
Runs named-entity recognition, text classification, structured-JSON parsing and relation extraction from one 205M-parameter encoder in a single CPU forward pass, using schemas with per-field regex validators. A larger 1B model is available via API.
AI Client2025
Extracts structured data from unstructured text with LLMs, mapping every extraction to its exact character span in the source for visual review. Uses few-shot examples, schema enforcement, and multi-pass chunking to handle long documents.
AI Dataset2025
A collection of ready-to-run Hugging Face Jobs OCR scripts that add a markdown column (or structured JSON) to image datasets, with model switching, layout detection, server-mode serving, and per-model options for table/form extraction.
AI Dataset2025
A ~3.2M-conversation Hugging Face dataset of non-toxic human–ChatGPT interactions for instruction finetuning and evaluation; includes full transcripts plus request headers, hashed IP/geolocation, turn-level moderation scores and usage metadata.
AI Dataset2025
A large multi-config collection of query–document pairs assembled to reproduce and extend the mGTE/LateOn data recipe for pre-training text embedding models. Data come in source-specific configs and include per-row drop/duplicate flags and guidance for using cleaned subsets for training.
AI Dataset2025
A 1,000,000-sample Vietnamese historical conversation dataset in ShareGPT/ChatML format for question-answering and text-generation. Approximately 78% of samples include step-by-step reasoning chains; remaining samples are final-only. Useful for training or evaluating Vietnamese LLMs and chat agents.
Compares standard human psychometric questionnaires (PVQ, BFI) with generation‑based profiling to test whether questionnaires predict real LLM responses. Finds big divergences: questionnaires exploit lexical cues and elicit alignment‑consistent answers, mischaracterizing LLM behavior on everyday queries.