datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.phased-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.dxf-gears-unstructured
Gear Design Dataset
Dataset Overview
The Gear Design Dataset contains structured data that includes gear design specifications and their corresponding DXF (Drawing Exchange Format) files. The dataset is intended for training and evaluating machine learning models focused on generative design and CAD (Computer-Aided Design) tasks. The dataset is split into three parts: training, validation, and test sets.
Each entry in the dataset represents a specific gear type and… See the full description on the dataset page: https://huggingface.co/datasets/mvrdock/dxf-gears-unstructured.AAID_new_unstructured
Citation Information
Liu, Z., Yang, K., Xie, Q., Zhang, T., & Ananiadou, S. (2024, August). Emollms: A series of emotional large language models and annotation tools
for comprehensive affective analysis. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5487-5496).
AAID_unstructured
Citation Information
Liu, Z., Yang, K., Xie, Q., Zhang, T., & Ananiadou, S. (2024, August). Emollms: A series of emotional large language models and annotation tools
for comprehensive affective analysis. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5487-5496).
unstructured-to-jsonunstructured-data-multilingual
Dataset Card for "unstructured-data-multilingual"
More Information needed
JSON-Unstructured-StructuredDataset Contains Synthetically Generated Unstructured Text, Set of Rules for Schema Creation, Filled Structured JSON
Can be used for any unstructured to structured tasks
long_sparse_unstructured_tableThis dataset is generated syhthetically to create tables with following characteristics:
Empty cell percentage in following range [40,70] (Sparse)
There is no seperator between rows and columns (un-structured).
15 <= num rows <= 30, 7 <= num columns <= 15 (Long)
Load the dataset
import io
import pandas as pd
from PIL import Image
def bytes_to_image(self, image_bytes: bytes):
return Image.open(io.BytesIO(image_bytes))
def parse_annotations(self, annotations: str) ->… See the full description on the dataset page: https://huggingface.co/datasets/nanonets/long_sparse_unstructured_table.risk-dataset-unstructuredfull-unstructured-nonansmall_sparse_unstructured_tableThis dataset is generated syhthetically to create tables with following characteristics:
Empty cell percentage in following range [40,70] (Sparse)
There is no seperator between rows and columns (un-structured).
4 <= num rows <= 10, 2 <= num columns <= 6 (Small)
Load the dataset
import io
import pandas as pd
from PIL import Image
def bytes_to_image(self, image_bytes: bytes):
return Image.open(io.BytesIO(image_bytes))
def parse_annotations(self, annotations: str) ->… See the full description on the dataset page: https://huggingface.co/datasets/nanonets/small_sparse_unstructured_table.ei_oc_unstructured
Citation Information
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana
Kiritchenko. 2018. Semeval-2018 task 1: Affect in tweets. In Proceedings of the
12th international workshop on semantic evaluation. 1–17
full-unstructuredchat_qd_all_stages_with_linking_data_unstructuredcybersec-unstructuredphased-self-discover-mistral-unstructured-MATH-eval-annotatedphased-self-discover-mistral-unstructured-MATH-evalfinretune_unstructured_v2risk-dataset-unstructured-paraphrased-humarin-evalrisk-dataset-unstructured-evaluationrisk-dataset-unstructured-paraphrased-humarinhaystack_docs_raw_unstructuredunstructured
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/halloween90/unstructured.risk-dataset-unstructured-paraphrasedfinretune_unstructured_v3phased-self-discover-mistral-unstructured-0-shot-t4d-eval