LLM Training

The LLM Training data listing includes four text-based datasets covering filings with the SEC, US patent grants, US government contracts, and the global research system catalog from OpenAlex. This product was built to provide users with a set of text-rich data for training, fine-tuning, and inference of large language models (LLMs).

**Topics Covered**

- SEC filings including fiscal calendars, press releases, earnings, annual & quarterly reports, major company events, and quarterly fund holdings
- US patent grants including patent type, invention title, and contributor name/location
- US federal government contract opportunities and awards
- OpenAlex index of scholarly entities (e.g. works, sources, authors, funders, publishers) and how they are connected to one another

**[Documentation](https://data-docs.snowflake.com/foundations/products/llm-training/)**

Refer to [Docs](https://data-docs.snowflake.com/foundations/products/llm-training/) for detailed attributes (e.g. history, release frequency, granularity), source links, release frequencies, notes and methodologies.

**Data Sources**

1. US Securities & Exchange Commission (SEC)
2. United States Patent and Trademark Office (USPTO)
3. OpenAlex
4. System for Award Management (SAM.gov)

Visit the [Data Catalog](https://data-catalog.snowflake.com/) to search all sources Snowflake integrates and the Snowflake Marketplace listings where you can find the data.

**Snowflake's Unified Schema**

All Snowflake products follow the EAV (entity, attributes, value) model with a unified schema. Entities are tangible objects (e.g. geography, company) that Snowflake provides data on. All timeseries' dates and values that refer to the entity are included in a timeseries table. Descriptors of the timeseries are included in an attributes table. Data is joinable across all Snowflake products that have a GEO_ID. Visit [docs](https://data-docs.snowflake.com/intro/concepts/unified-schema/) for more details.

*Disclaimer: The data in this app is sourced [here](https://data-docs.snowflake.com/foundations/products/llm-training/). Links to provider license, terms and disclaimers are provided where appropriate. Snowflake is not endorsed by or affiliated with any of these providers.*

搜集汇总

背景与挑战

背景概述

该数据集整合了SEC申报文件、美国专利授权、政府合同及OpenAlex学术目录四大文本资源，适用于大语言模型的训练与优化。所有数据遵循Snowflake统一的EAV模型架构，可通过GEO_ID实现跨产品关联。