Alternative data datasets
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Datamule, Teraflop AI, and Eventual jointly contributed a 590 gigabyte SEC EDGAR collection encompassing about 8 million filings and 43 billion tokens, assembled using the datamule-python library and the official datamule API.
ALEA Patent Claim Graph Reading questions about the dependency structure of real U.S. patent claim sets: which claim a dependent claim refers back to, the chain of claims it rests on, its independent claim, which and how many claims depend on a given claim, and yes / no questions about dependency. Every answer is computed deterministically from the claim text shown in the row. This dataset is part
US patent full text assembled by the Allen Institute from the USPTO, millions of records under ODC-BY: claims, abstracts, descriptions and metadata, in Parquet. A broad base for measuring IP intensity, technology landscapes and the direction of R&D spending.
SEC-EDGAR filings, part f (copy) An unmodified copy of part of TeraflopAI/SEC-EDGAR. I keep it here as staging data for language-model pretraining. Source: TeraflopAI/SEC-EDGAR, at commit 43de32c Files: 587 files, 45.6 GB, in the source's form-type folders: 8-K (217), S-1 (216), 4 (76), 20-F (40), S-8 (25), 3 (6), 5 (4), plus the source's LICENSE file and two chart images from its top folder. Lice
A large-scale release produced jointly by Datamule, Teraflop AI, and Eventual covering roughly 590 GB of SEC EDGAR content. It comprises about eight million filings totaling 43 billion tokens, gathered using the datamule-python package and the official datamule API.
S&P 500 earnings-call transcripts in Parquet under MIT, tens of thousands of calls. Independently compiled and comparable to other public transcript corpora for sentiment and tone work.
A collection of earnings call transcripts covering S&P 500 firms and other U.S. large-cap companies across the years 2005 through 2025, intended for financial analysis, NLP modeling, and sentiment research.
Vector embeddings of clinical-trial records, hundreds of thousands of entries in Parquet (Apache 2.0). Enables similarity and retrieval analysis of trial design, indication crowding and competitive positioning.
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A joint release by Datamule, Teraflop AI, and Eventual provides the SEC-EDGAR dataset, comprising 590 GB of content drawn from all major filings in the SEC EDGAR database and totaling roughly 8 million samples and 43 billion tokens. The data was assembled using the datamule-python library and the official datamule API developed by John Friedman, with datamule serving as a Python package for collecting and manipulating such records.
sec-earnings-bench sec-earnings-bench measures how accurately language models extract GAAP figures from US earnings press releases: revenue, net income and diluted EPS, for the quarter and the fiscal year to date (the full year in a fourth-quarter release). Each release is the press-release exhibit of an 8-K filed on SEC EDGAR, and each figure is graded automatically against XBRL in the company's
Datamule partnered with Teraflop AI and Eventual to publish the SEC-EDGAR dataset, which covers all major filings from the SEC EDGAR database and amounts to 590 GB across about 8 million samples and 43 billion tokens. Collection relied on the datamule-python library and John Friedman's official datamule API, tools designed in Python for gathering and handling SEC filing data.
Earnings-call transcripts for S&P 500 companies, tens of thousands of calls in English with timestamps, in Parquet (MIT). Direct input for call-tone and question-and-answer sentiment analysis across the large-cap earnings cycle.
PatSnap Patent OAR Bench A benchmark dataset for patent Office Action Response (OAR) tasks. The test split contains 40 cases: 13 CN, 13 EP, and 14 US. It includes 40 Office Action PDF documents totaling 229 pages, structured case materials, and 19 shared Judge-scored criteria per case (760 entries). Dataset: data/test.parquet, 40 cases in Parquet format. Office Action PDFs: assets/, named OAR-<id>
The ja_patent mirror of llm-jp-corpus-v4, assembled by the LLM-jp Corpus Building Working Group at NII, reproduces the Japanese patent sub-corpus from the larger LLM-jp Corpus v4 release. It is delivered as 621 jsonl.gz files totaling 58.2 GB in compressed form, with each line holding a JSON object that includes a text field and a meta field containing the document identifier, URL, and other provenance information.
The us-patents dataset is a collection of ~ 8M US patent grants and applications from 1976-2025, cleaned, filtered, and formatted for pre-training of language models. Document Format corpus_id: Unique integer key with no semantic value. filing_date: The filing date of the grant or application. In case of duplicates, earliest filing date from the duplicate cluster. patent_type: The type of patent.
Text of SEC filings pulled from EDGAR spanning millions of records in English (Apache 2.0): 10-K, 10-Q, 8-K, annual reports and exhibits, tagged for text-generation and classification. A practical base corpus for earnings- and filings-driven signals, from risk factors and MD&A to management commentary.
SEC EDGAR filings (processed) 🌐 The Fin AI Pretraining / reference corpus released by The Fin AI. Source: U.S. SEC EDGAR filings — https://www.sec.gov/search-filings. Note: Some parquet files in this repository are empty (0 bytes). Source Text extracted from public filings on the SEC EDGAR system. Structure Rows: 2,839,488 Columns: text Quick Start from datasets import load_dataset ds = load_datas
S&P 500 Earnings Transcripts Dataset This comprehensive dataset contains earnings call transcripts for S&P 500 companies and US large-caps, spanning from 2005 to 2025. Earnings calls provide valuable insights into company performance, strategic initiatives, and management perspectives that are essential for financial analysis, natural language processing research, and market sentiment studies. Dat