updated README.md

README.md

@@ -35,7 +35,7 @@ license:
 <img src="nomiracl.png" alt="NoMIRACL Hallucination Examination (Generated using miramuse.ai and Adobe photoshop)" width="500" height="400">
 
 ## Quick Overview
-This repository contains the topics, qrels and top-k (a maximum of 10) annotated passages. The passage collection can be found be here on HF: [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
+This repository contains the topics, qrels, and top-k (a maximum of 10) annotated passages. The passage collection can be found here on HF: [miracl/miracl-corpus](https://huggingface.co/datasets/miracl/miracl-corpus).
 
 ```
 import datasets
@@ -45,21 +45,21 @@ subset = 'relevant'  # or 'non_relevant' (two subsets: relevant & non-relevant)
 split = 'test'       # or 'dev' for the development split
 
 # four combinations available: 'dev.relevant', 'dev.non_relevant', 'test.relevant' and 'test.non_relevant'
-nomiracl = datasets.load_dataset('miracl/nomiracl', language, split=f'{split}.{subset}')
+nomiracl = datasets.load_dataset('miracl/nomiracl', language, split=f'{split}.{subset}', trust_remote_code=True)
 ```
 
 ## What is NoMIRACL?
-Retrieval Augmented Generation (RAG) is a powerful approach to incorporate external knowledge into large language models (LLMs) to enhance the accuracy and faithfulness of LLM generated responses. However, evaluating query-passage relevance across diverse language families has been a challenge, leading to gaps in understanding the model's performance against errors in external retrieved knowledge. To address this, we present NoMIRACL, a completely human-annotated dataset designed for evaluating multilingual LLM relevance across 18 diverse languages.
+Retrieval Augmented Generation (RAG) is a powerful approach to incorporating external knowledge into large language models (LLMs) to enhance the accuracy and faithfulness of LLM-generated responses. However, evaluating query-passage relevance across diverse language families has been a challenge, leading to gaps in understanding the model's performance against errors in external retrieved knowledge. To address this, we present NoMIRACL, a completely human-annotated dataset designed for evaluating multilingual LLM relevance across 18 diverse languages.
 
-NoMIRACL evaluates LLM relevance as a binary classification objective, where it contains two subsets: `non-relevant` and `relevant`. The `non-relevant` subset contains queries with all passages manually judged by an expert assessor as non-relevant, while the `relevant` subset contains queries with at least one judged relevant passage within the labeled passages. LLM relevance is measured using two key metrics: 
+NoMIRACL evaluates LLM relevance as a binary classification objective, containing two subsets: `non-relevant` and `relevant`. The `non-relevant` subset contains queries with all passages manually judged by an expert assessor as non-relevant, while the `relevant` subset contains queries with at least one judged relevant passage within the labeled passages. LLM relevance is measured using two key metrics: 
 - *hallucination rate* (on the `non-relevant` subset) measuring model tendency to recognize when none of the passages provided are relevant for a given question (non-answerable).
 - *error rate* (on the `relevant` subset) measuring model tendency as unable to identify relevant passages when provided for a given question (answerable).
 
 ## Acknowledgement
 
-This dataset would not have been possible without all the topics are generated by native speakers of each language in conjuction from our **multilingual RAG universe** work in part 1, **MIRACL** [[TACL '23]](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438/MIRACL-A-Multilingual-Retrieval-Dataset-Covering). The queries with all non-relevant passages are used to create the `non-relevant` subset whereas queries with at least a single relevant passage (i.e., MIRACL dev and test splits) are used to create `relevant` subset.
+This dataset would not have been possible without all the topics are generated by native speakers of each language in conjunction with our **multilingual RAG universe** work in part 1, **MIRACL** [[TACL '23]](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00595/117438/MIRACL-A-Multilingual-Retrieval-Dataset-Covering). The queries with all non-relevant passages are used to create the `non-relevant` subset whereas queries with at least a single relevant passage (i.e., MIRACL dev and test splits) are used to create `relevant` subset.
 
-This repository contains the topics, qrels and top-10 (maximum) annotated documents of NoMIRACL. The whole collection can be found be [here](https://huggingface.co/datasets/miracl/miracl-corpus).
+This repository contains the topics, qrels and top-10 (maximum) annotated documents of NoMIRACL. The whole collection can be found [here](https://huggingface.co/datasets/miracl/miracl-corpus).
 
 ## Quickstart
 
@@ -71,7 +71,7 @@ subset = 'relevant'  # or 'non_relevant'
 split = 'test'       # or 'dev' for development split
 
 # four combinations available: 'dev.relevant', 'dev.non_relevant', 'test.relevant' and 'test.non_relevant'
-nomiracl = datasets.load_dataset('miracl/nomiracl', language, split=f'{split}.{subset}')
+nomiracl = datasets.load_dataset('miracl/nomiracl', language, split=f'{split}.{subset}', trust_remote_code=True)
 ```
 
 
@@ -84,7 +84,7 @@ nomiracl = datasets.load_dataset('miracl/nomiracl', language, split=f'{split}.{s
 1. To download the files:
 
 Under folders `data/{lang}`,
-the subset of corpus is saved in `.jsonl.gz` format, with each line to be:
+the subset of the corpus is saved in `.jsonl.gz` format, with each line to be:
 ```
 {"docid": "28742#27", 
 "title": "Supercontinent", 
@@ -134,7 +134,7 @@ Paper: [https://aclanthology.org/2024.findings-emnlp.730/](https://aclanthology.
 
 
 ## Citation Information
-This work was conducted as a collaboration between University of Waterloo and Huawei Technologies.
+This work was conducted as a collaboration between the University of Waterloo and Huawei Technologies.
 
 ```
 @inproceedings{thakur-etal-2024-knowing,