datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MapEval-API
MapEval-API
MapEval-API is created using MapQaTor.
Usage
from datasets import load_dataset
# Load dataset
ds = load_dataset("MapEval/MapEval-API", name="benchmark")
# Generate better prompts
for item in ds["test"]:
# Start with a clear task description
prompt = (
"You are a highly intelligent assistant. "
"Answer the multiple-choice question by selecting the correct option.\n\n"
"Question:\n" + item["question"] + "\n\n"… See the full description on the dataset page: https://huggingface.co/datasets/MapEval/MapEval-API.MapEval-Textual
MapEval-Textual
MapEval-Textual is created using MapQaTor.
Usage
from datasets import load_dataset
# Load dataset
ds = load_dataset("MapEval/MapEval-Textual", name="benchmark")
# Generate better prompts
for item in ds["test"]:
# Start with a clear task description
prompt = (
"You are a highly intelligent assistant. "
"Based on the given context, answer the multiple-choice question by selecting the correct option.\n\n"
"Context:\n" +… See the full description on the dataset page: https://huggingface.co/datasets/MapEval/MapEval-Textual.MetaRAG_Cross-Issue_OSSQA
MetaRAG Cross-Issue OSSQA
Dataset page: https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA
MetaRAG Cross-Issue OSSQA is an English open-source software issue question-answering and retrieval benchmark. Each example asks a question grounded in one GitHub issue and requires evidence from a related issue. The data contains explicit cross-issue references and a three-document silver evidence path.
Dataset configurations
Configuration
Splits
Rows… See the full description on the dataset page: https://huggingface.co/datasets/MapleBi/MetaRAG_Cross-Issue_OSSQA.MapEval-Textual
MapEval-Textual
MapEval-Textual is created using MapQaTor.
Usage
from datasets import load_dataset
# Load dataset
ds = load_dataset("MapEval/MapEval-Textual", name="benchmark")
# Generate better prompts
for item in ds["test"]:
# Start with a clear task description
prompt = (
"You are a highly intelligent assistant. "
"Based on the given context, answer the multiple-choice question by selecting the correct option.\n\n"
"Context:\n"… See the full description on the dataset page: https://huggingface.co/datasets/TankVincent/MapEval-Textual.