Omkar2118/datasets huggingface dataset:
Reviews data from trustpilot

This dataset contains a curated sample of 20,000 English-language user reviews sourced exclusively from Trustpilot.com. It is a representative subset of our larger collection containing over 1 million Trustpilot reviews across various industries and companies.

🗂️ Dataset Overview
Source: Trustpilot
Total Records: 20,000
Language: English
GEOM-DRUGS

GEOM-DRUGS is a dataset of 430,000 large organic molecules of up to 180 atoms from [Axelrod and Gómez-Bombarelli, Nature Scientific Data, 2022](https://www.nature.com/articles/s41597-022-01288-4). 

The dataset used by most machine learning papers is a processed version from: [link](https://github.com/cvignac/MiDi) or [link](https://huggingface.co/datasets/chaitjo/GEOM-DRUGS_ADiT). 

The original, unprocessed dataset is available: [link](https://github.com/learningmatter-mit/geom).

Introducing paper: MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
Finetune-RAG

This dataset is part of the Finetune-RAG project, which aims to tackle hallucination in retrieval-augmented LLMs. It consists of synthetically curated and processed RAG documents that can be utilised for LLM fine-tuning.

Each line in the finetunerag_dataset.jsonl file is a JSON object:

```json
{
  "content": "<correct content chunk retrieved>",
  "filename": "<original document filename>",
  "fictitious_filename1": "<filename of fake doc 1>",
  "fictitious_content1": "<misleading content chunk 1>",
  "fictitious_filename2": "<filename of fake doc 2>",
  "fictitious_content2": "<misleading content chunk 2>",
  "question": "<user query>",
  "answer": "<GPT-4o answer based only on correct content>",
  "content_before": "<optional preceding content>",
  "content_after": "<optional succeeding content>"
}
```

Note that the documents contain answers generated by GPT-4o. Additionally, the prompts used to generate the selected answers do not involve any ficticious data, ensuring that the answers are not contaminated when used for fine-tuning.

Introducing paper: Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation