datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Financial_Context_DatasetThis dataset contains over 50,000 samples of user financial queries paired with their corresponding structured data requests (context). It was created to facilitate the creation of the Financial Agent LLM for accurate data extraction and query answering.
How to load the Dataset
You can load the dataset using the code below:
from datasets import load_dataset
ds = load_dataset("Chaitanya14/Financial_Context_Dataset")
Dataset Construction
Diverse Query Sources… See the full description on the dataset page: https://huggingface.co/datasets/Chaitanya14/Financial_Context_Dataset.Synthetic-Context-Conversations
Synthetic-Context-Conversations
Overview
The Synthetic-Context-Conversations dataset is a collection of synthetic conversations designed to simulate empathetic and context-rich dialogues. It is particularly useful for tasks such as text generation, summarization, and question answering. The dataset is available in English and contains between 10,000 to 100,000 entries.
Dataset Details
Modalities: Text
Languages: English
Size: 10K-100K
Formats: Parquet
License:… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Synthetic-Context-Conversations.ContextStretchQABelow is a structured, professional‐tone description of the “QA Increasing Context Length” dataset. You can use this text as a README, a data card, or incorporate it directly into documentation.
QA Increasing Context Length Dataset
1. Overview
The QA Increasing Context Length dataset is designed to facilitate benchmarking and research on question‐answering (QA) systems as the size of the input context grows. It compiles QA examples drawn from multiple LongBench subsets… See the full description on the dataset page: https://huggingface.co/datasets/slinusc/ContextStretchQA.qrecc_with_context_and_answersThis is the QRECC dataset arranged to be used for a query rewriting task.
Each line is composed as follow:
INTRODUCTION token is followed by the PREVIOUS TURNS of the conversation together WITH THE ANSWERS provided in the dataset
QUESTION token is followed by the current query the system should rewrite
ANSWER token is followed by the rewriting of the current query
qrecc_context_and_answersThis is the QRECC dataset arranged to be used for a query rewriting task. Each line is composed as follow:
INTRODUCTION token is followed by the PREVIOUS TURNS of the same conversation, which means only the previous questions
QUESTION token is followed by the current query the system should rewrite
ANSWER token is followed by the REWRITING of the current query + the given ANSWER