datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
penicillin
Penicillin dataset
Paper: https://arxiv.org/abs/2510.25771
Note
This dataset is a processed combination of existing benchmark datasets. The licensing and data rights belong to the original dataset owners. Please refer to the original datasets for more information.
halvest-geometric
HALvest-Geometric
Citation Network of Open Scientific Papers Harvested from HAL
Dataset Summary
overview:
French and English fulltexts from open papers found on Hyper Articles en Ligne (HAL) and its citation network.
You can download the dataset using Hugging Face datasets:
from datasets import load_dataset
ds = load_dataset("Madjakul/HALvest-Geometric", "en")
Details
Nodes
Papers: 18,662,037
Authors: 238,397… See the full description on the dataset page: https://huggingface.co/datasets/almanach/halvest-geometric.penicillin_plus
Penicillin-Plus dataset
Paper: https://arxiv.org/abs/2510.25771
Note
This dataset is a processed combination of existing benchmark datasets. The licensing and data rights belong to the original dataset owners. Please refer to the original datasets for more information.
OntoBook
OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining
Dataset Authors
Rian Touchent & Eric de la ClergerieInria, Sorbonne Université
Overview
OntoBook is a French biomedical pretraining corpus generated from the relational structure of three medical ontologies: CIM-10 FR PMSI for diagnoses, CCAM for medical procedures, and ATC for drugs. Weighted random walks turn ontology graphs into structured sequences of codes and… See the full description on the dataset page: https://huggingface.co/datasets/almanach/OntoBook.tripalchemy-experiences
🧪 TripAlchemy — Synthetic Travel Experiences
10,396 rich, vibe-scored travel experiences across 30 cities — generated by a
pre-trained Hugging Face model and served through a live recommender app.
🚀 Live demo: huggingface.co/spaces/almador2002/tripalchemy
✨ What makes it special
Every experience is scored 0–1 across all six categories at once — 🍽️ culinary, 🏛️ historical,
🛍️ shopping, 🌲 nature, 🌃 nightlife, 🎨 art & culture. That multi-label… See the full description on the dataset page: https://huggingface.co/datasets/almador2002/tripalchemy-experiences.Almanac
Almanac
Human–human collaborative map-reproduction sessions for studying grounding acts, mental models, and next-action prediction in guide–follower teams.
Dataset contents
Component
Location
Description
Raw sessions
data/raw_sessions/{c1,c2}/{study}/
Per-session guide/follower timelines, full action logs, score boards
SFT splits
data/sft/{task}/train.jsonl, test.jsonl
Chat-format fine-tuning data for 4 prediction tasks
Grounding
data/grounding/{c1… See the full description on the dataset page: https://huggingface.co/datasets/NEU-HAI/Almanac.lfm2.5-1.2b-blindspots
LFM2.5-1.2B-Base Blind Spots Dataset
Model Link: https://huggingface.co/LiquidAI/LFM2.5-1.2B-BaseNotebook Link: https://colab.research.google.com/drive/13sDzKbvsaYxpKwBD-ePktBrDFX2qoPiI?usp=sharing
Model Loading and Inference
I loaded the model in a high-level manner using the pipeline function from the transformers library:
# Loading the model using the pipeline function from the transformers library
from transformers import pipeline
pipe = pipeline("text-generation"… See the full description on the dataset page: https://huggingface.co/datasets/Almaan/lfm2.5-1.2b-blindspots.Urdu-Training-for-NLP
Urdu Instruction Dataset for NLP
A manually curated dataset of 578 Urdu instruction-response
pairs for fine-tuning language models on Urdu NLP tasks.
Dataset Description
This dataset was created to address the lack of
instruction-tuning data for Urdu, a low-resource language
spoken by over 230 million people. All examples were written
and verified by a native Urdu speaker.
Dataset Structure
Each example contains a conversation with a user… See the full description on the dataset page: https://huggingface.co/datasets/Almanships/Urdu-Training-for-NLP.