Rag Instruct Benchmark Tester by Kaggle | Other

About this Dataset

Rag Instruct Benchmark Tester

200 Samples for Enterprise Core Q&A Tasks

By Huggingface Hub [source]

About this dataset

This RAG: Financial & Legal Retrieval-Augmented-Generation Benchmark Evaluation Dataset provides a unique opportunity for professionals in the legal and financial industries to analyze the latest retrieval augmented generation (RAG) technology. With 200 diverse samples that contains both a relevant context passage and a related question, it is an invaluable assessment tool to measure different capabilities of retrieval augmented generation enterprise use cases. Whether you are looking to optimize Core Q&A, classify Not Found topics, apply Boolean Yes/No principles, delve into deep math equations, explore complex Q&A inquiries or summarize core principles – this dataset is here provide all of these tasks in an accurate and efficient manner. Illuminating solutions from robust questions and context passages, this is a benchmark for advanced techniques across all areas of legal & financial services which will allow decision-makers full insight into retrieval augmented generation technology

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How to use the dataset

Explore the dataset by examining the columns listed above: query, answer, sample_number and tokens; and also take a look at the category of each sample.

Create hypotheses using a sample question from one of the categories you are interested in studying more closely. Formulate questions that relate directly to your hypothesis using more or fewer variables from this dataset as well as others you may find useful for your particular research needs.

Take into account any limitations or assumptions that may exist related to either this set’s data or any other related external sources when crafting research questions based upon this dataset’s data schema or content: before formulating any conclusions be sure to double check your work with reliable references on hand!

Utilize statistics analysis tools such as correlation coefficients (i..e r), linear regression equations (slope/intercept) and scatter plots (or other visualizations) if necessary– prioritizing one variable from each category over another should be handled accordingly within context what would best suit your research needs given these limitation constraints! As mentioned earlier additional external data might come into play here too — remember keep records all evidence for future reference purposes!
5 .Refine specific questions and develop an experimental setup wherein promising results can begin testing theories with improved accuracy — note whether failures occurred due too trivial errors taken during human analytical processing outlier distortion produced by manipulated outliers / variables accompanied by deflated explanatory power leading up these erroneous outcomes on their own according's subject matter expertise level difficulty settings versus expected mean standard deviations etc.. Reforming further experiments around other more accurate working models involving this same series' empirical studies should continuously reviewed if needed – linking back core findings associated with initial input(s)! Advice recommended prior engaging research emphasis involves breaking individual questing resolving into smaller subtasks continuingly providing measurable evidence explains large scale phenomena in terms once those analyzed better comprehended domain professionals evaluated current progress undergone since prior iteration trials begun had formerly scoped examine subcomponents separated them one part discuss branch individual components related discussed subsequent progression stages between sections backdrop applicable aspects... Pruning methods utilized slim down information Thus while Working Develop Practical

Research Ideas

Utilizing the tokens to create a sophisticated text-summarization network for automatic summarization of legal documents.

Training models to recognize problems for which there may not be established answers/solutions yet, and estimate future outcomes based on data trends an patterns with machine learning algorithms.

Analyzing the dataset to determine keywords, common topics or key issues related to financial and legal services that can be used in enterprise decision making operations

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv

Column name	Description
query	The query or question associated with the context passage. (String)
answer	The answer to the query or question. (String)
sample_number	The sample number associated with the query or question. (Integer)
tokens	The tokens associated with the query or question. (String)
category	The category associated with the query or question. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit Huggingface Hub.

Tables

Train

@kaggle.thedevastator_rag_financial_legal_evaluation_dataset.train

45.12 KB
200 rows
6 columns


CREATE TABLE train (
  "query" VARCHAR,
  "answer" VARCHAR,
  "context" VARCHAR,
  "sample_number" BIGINT,
  "tokens" BIGINT,
  "category" VARCHAR
);