datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
python_documentation_codeDataset created using https://github.com/jeffmeloy/py2dataset using the Python code from the following:
https://github.com/ansible/ansible
https://github.com/apache/airflow
https://github.com/arogozhnikov/einops
https://github.com/arviz-devs/arviz
https://github.com/astropy/astropy
https://github.com/biopython/biopython
https://github.com/bjodah/chempy
https://github.com/bokeh/bokehhttps://github.com/CalebBell/thermo
https://github.com/camDavidsonPilon/lifelines
https://github.com/coin-or/pulp… See the full description on the dataset page: https://huggingface.co/datasets/jeffmeloy/python_documentation_code.worldbank-project-documents
Dataset Card for World Bank Project Documents
Dataset Summary
This is a dataset of documents related to World Bank development projects in the period 1947-2020. The dataset includes
the documents used to propose or describe projects when they are launched, and those in the review. The documents are indexed
by the World Bank project ID, which can be used to obtain features from multiple publicly available tabular datasets.
Supported Tasks and Leaderboards
No… See the full description on the dataset page: https://huggingface.co/datasets/lukesjordan/worldbank-project-documents.oold-wikidata-schemaorg-documents
Wikipedia leads, as the corpus measured them
The article leads that
oold-llm-bench's Wikidata-schema.org corpus cites: 959
documents, one per entity, each the plain-text introduction of a named
revision with its whitespace collapsed.
Published because the alternative does not work. The corpus commits each
lead's revision id and sha256 rather than its text, and a third party was
meant to re-fetch. Extracts are not versioned: the MediaWiki API ignores
revids for prop=extracts and… See the full description on the dataset page: https://huggingface.co/datasets/OO-LD/oold-wikidata-schemaorg-documents.Corrupted-russian-word-documents-text-datasetThis is synthetic text-corruption dataset, based on Russian open official/government documents text.
This dataset is intended to be used to train LLM to perform text-recovery task.
All the errors in text is made solely in Russian sentences, hence ignoring any English sentence.
Texts contains complex formatting, which is common for documents.
Each line contains json object that have array messages value, which consists of role-based conversation.
Each first message is randomly chosen system… See the full description on the dataset page: https://huggingface.co/datasets/Kemsekov/Corrupted-russian-word-documents-text-dataset.grpo-oumi-synthetic-document-claims
Dataset Card for GRPO Oumi ANLI Subset
Dataset
This dataset is a reformatted version of the oumi-ai/oumi-synthetic-document-claims dataset, specifically structured for use with the GRPO trainer.
You can find more detailed information about the original dataset at the provided link.
Link: https://huggingface.co/datasets/oumi-ai/oumi-synthetic-document-claims
Dataset Structure
The dataset consists of a list of dictionaries, where each dictionary represents a… See the full description on the dataset page: https://huggingface.co/datasets/Teen-Different/grpo-oumi-synthetic-document-claims.document-summarization-dpo-100k
Document Summarization DPO (100K)
100,000 DPO (Direct Preference Optimization) preference pairs for training models to summarize business and professional documents with precision, structure, and analytical depth.
Motivation
Document summarization is one of the highest-value enterprise AI applications — analysts, lawyers, product managers, and executives use AI to process reports, contracts, and research daily. Models commonly fail by:
Losing quantitative data:… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/document-summarization-dpo-100k.spelling-creator-document-import
Spelling Creator document import
One section of a lesson as someone might have typed it up, paired with that section as lesson JSON. Spelling Creator is a lesson builder for Spelling to Communicate (S2C), used with nonspeaking spellers. Its Import from text reads a typed lesson with rules, and hands the sections the rules cannot read to a small on-device model; this dataset trains that model.
What is in it
Chat-format JSONL, ready for a supervised fine-tune (TRL's… See the full description on the dataset page: https://huggingface.co/datasets/playforgecoding/spelling-creator-document-import.