datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
quantem-base-model-sources
QuantEM — base model data sources
Every dataset in the corpus the QuantEM ViT-B encoder was pretrained on: public repository holdings, data contributed by external laboratories through the QuantEM outreach campaign, and in-house acquisitions.
Emitted verbatim from Supplementary Table 2 of the QuantEM manuscript — 657 rows. Please cite the
original sources listed here alongside QuantEM; rows carry a DOI or repository URL where one
exists.
Related:
ArrojoeDrigoLab/quantem — the… See the full description on the dataset page: https://huggingface.co/datasets/ArrojoeDrigoLab/quantem-base-model-sources.base_model_sprint
Base Model Metadata Sprint
Description
Join us in improving the discoverability and understanding of models on the Hugging Face Hub by adding base_model metadata! This sprint aims to enhance the information available for models derived from, fine-tuned on, or quantized versions of existing base models.
🤗 Strong contributions will win prizes!! 🤗
Why It Matters
Adding base_model metadata helps users:
Easily find models derived from specific architectures… See the full description on the dataset page: https://huggingface.co/datasets/librarian-bots/base_model_sprint.granite-base-model-errors
Granite-1B Base Model Errors
Overview
This dataset contains 10 examples where the Granite-4.0-1B-Base language model produces incorrect or awkward outputs. Each row includes:
id: a unique identifier for each example
input: the prompt given to the model
expected_output: what the correct answer or completion should be
model_output: what the model actually produced
The dataset demonstrates common blind spots of a base causal language model, including factual errors, logic… See the full description on the dataset page: https://huggingface.co/datasets/thatgirltomiie/granite-base-model-errors.flair-base-model-detection
Flair Base Model Detection
For detailed instructions of dataset generation process, please refer to this GIST.
span-marker-base-model-detection
SpanMarker Base Model Detection
It is relative simply to determine base model of a fine-tuned SpanMarker model:
import os
from huggingface_hub import login, HfApi
hf_token = os.environ.get("HF_TOKEN")
login(token=hf_token, add_to_git_credential=True)
api = HfApi()
Please make sure that HF_TOKEN is set as environment variable.
After that, list of all SpanMarker models can be retrieved and configuration file is parsed.
Please make sure that span-marker library is installed:
from… See the full description on the dataset page: https://huggingface.co/datasets/stefan-it/span-marker-base-model-detection.CNNOV_Eng_BaseModelbase_model_eval
Base Model Blind Spots: Zero-Shot Instruction Failures
Model Name: Qwen3-4b-base.
The model was loaded using a standard Kaggle environment with a P100 GPU using the transformers library.
from transformers import AutoModelForCausalLM, AutoTokenizer
import kagglehub
model_name = kagglehub.model_download("qwen-lm/qwen-3/transformers/4b-base")
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(… See the full description on the dataset page: https://huggingface.co/datasets/dd42/base_model_eval.