up

README.md

@@ -63,6 +63,17 @@ configs:
 This datasets contains all query & document embeddings for [BEIR](https://github.com/beir-cellar/beir), embedded with the [Cohere embed-english-v3.0](https://huggingface.co/Cohere/Cohere-embed-english-v3.0) embedding model.
 
 
+## Overview of datasets
+
+This repository hosts all 18 datasets from BEIR, including query and document embeddings. The following table gives an overview of the available datasets.
+See the next section how to load the individual datasets.
+
+| Dataset | #Test Queries | #Documents
+| --- | ---- | --- |
+| nfcorpus | | 3633 |
+
+
+
 ## Loading the dataset
 
 ### Loading the document embeddings
@@ -203,7 +214,7 @@ dataset_split = "test"
 num_dim = 1024
 
 #Load qrels
-df = load_dataset(dataset_name, "qrels", split=dataset_split)
+df = load_dataset("Cohere/beir-embed-english-v3", f"{dataset_name}-qrels", split=dataset_split)
 qrels = {}
 for row in df:
     qid = row['query_id']
@@ -215,14 +226,14 @@ for row in df:
         qrels[qid][cid] = row['score']
 
 #Load queries
-df = load_dataset(dataset_name, "queries", split=dataset_split)
+df = load_dataset("Cohere/beir-embed-english-v3", f"{dataset_name}-queries", split=dataset_split)
 
 query_ids = df['_id']
 query_embs = np.asarray(df['emb'])
 print("Query embeddings:", query_embs.shape)
 
 #Load corpus
-df = load_dataset(dataset_name, "corpus", split="train")
+df = load_dataset("Cohere/beir-embed-english-v3", f"{dataset_name}-corpus", split="train")
 
 docs_ids = df['_id']