unstructured
SCORE-Bench
Dataset Description
SCORE-Bench is a curated collection of 224 diverse, real-world documents manually annotated by experts. It is designed to benchmark document parsing systems against true production-grade challenges. Unlike traditional academic datasets often composed of clean, digital-native PDFs, this benchmark specifically targets the complexity found in actual enterprise workflows.
Note on replication: This dataset is a standalone benchmark released after the publication of… See the full description on the dataset page: https://huggingface.co/datasets/unstructuredio/SCORE-Bench.argilla_sdk_docs_raw_unstructured
Dataset info
This dataset contains documentation chunks from repositories (ADD REPOS).
Postprocessing
After some inspection, some chunks contain text too short to be meaningful, so we decided to remove those by removing chunks whose number of tokens (computed
with the same tokenizer of the model to be used for the embeddings) is lower or equal to the 5%:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-base-en-v1.5")
df =… See the full description on the dataset page: https://huggingface.co/datasets/plaguss/argilla_sdk_docs_raw_unstructured.rag-for-unstructured-dataphased-self-discover-mistral-unstructured-5-shot-bbh-evalphased-self-discover-mistral-unstructured-0-shot-bbh-evalR1-Reasoning-Unstructured-To-Structured
MasterControl AIML Team 🚀
Overview
The MasterControl AIML team supports the Hugging Face initiative of re-creating DeepSeek R1 training, recognizing it as one of the most impactful open-source projects today.
We aim to contribute to reasoning datasets, specifically those where:
A real-world problem involves generating complex structured output
It is accompanied by step-by-step reasoning and unstructured input
Challenges in Integrating Generative AI… See the full description on the dataset page: https://huggingface.co/datasets/MasterControlAIML/R1-Reasoning-Unstructured-To-Structured.